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Title | Authors | Year | Perception Perspective | Level | Order | Temporality | Selective Attention | Abstraction | Competitive | Chaining | Acquisition | Prediction | Generalization | Exploitation | Learning | Kind | Abstraction | Brain Areas | Method | Features | Training | Evaluation | Data added |
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Title | Authors | Year | Perception Perspective | Level | Order | Temporality | Selective Attention | Abstraction | Competitive | Chaining | Acquisition | Prediction | Generalization | Exploitation | Learning | Kind | Abstraction | Brain Areas | Method | Features | Training | Evaluation | Data added |

A model-based approach to finding substitute tools in 3D vision data | Abelha et al. | 2016 | agent | local | 0th | stable | no | micro | yes | no | ground truth | Optimization | yes | action selection | offline | tool-use | mathematical | geometric part fitting | point clouds, superquadrics | supervised | benchmark | original study | |

Unsupervised learning of affordance relations on a humanoid robot | Akgun et al. | 2009 | agent | meso | 1st | stable | no | micro | no | no | exploration | Classification | yes | action selection | offline | rollability | mathematical | not specified | SOM, SVM | shape, size | unsupervised | real robot | original study |

Supervised learning of hidden and non-hidden 0-order affordances and detection in real scenes | Aldoma et al. | 2012 | agent | global | 1st | stable | no | micro | no | no | ground truth | Classification | yes | not specified | offline | general | mathematical | not specified | SVM, Boost, RF | SEE, SHOT, NDS, SI, PFH | supervised | benchmark | original study |

From Human Instructions to Robot Actions: Formulation of Goals, Affordances and Probabilistic Planning | Antunes et al. | 2016 | agent | global | 2nd | stable+variable | no | micro+macro | yes | yes | exploration | Inference | yes | planning | online | pulling, dragging, grasping | mathematical | BN, PRAXICON Semantic Network, PRADA Planner | 2D geom feat., 2D tracked object displacement | not specified | real robot | original study | |

On Exploiting Haptic Cues for Self-Supervised Learning of Depth-Based Robot Navigation Affordances | Baleia et al | 2015 | agent | global | 1st | stable | no | micro | no | no | exploration | Inference | yes | planning | online | traversability | mathematical | histograms, clustering, similarity metrics | depth, haptic | self-supervised | real robot | original study | |

Self-supervised learning of depth-based navigation affordances from haptic cues | Baleia et al. | 2014 | agent | global | 1st | stable | no | micro | no | no | exploration | Inference | yes | planning | online | traversability | mathematical | histograms, clustering, similarity metrics | depth, haptic | self-supervised | real robot | original study | |

Learning grasping affordance using probabilistic and ontological approaches | Barck-Horst et al | 2009 | agent | meso | 1st | stable | no | micro | no | no | exploration | Inference | yes | planning | online | grasping | mathematical | voting function, ontological rule-engine | shape, size, grasp region, force | self-supervised | simulation | original study | |

Grasp affordances from multi-fingered tactile exploration using dynamic potential fields | Bierbaum et al. | 2009 | agent | local | 1st | stable | no | micro | no | no | exploration | Regression | yes | planning | offline | grasping | mathematical | Potential Fields | planar faces of object | self-supervised | simulation | original study | |

Behavioural plasticity in evolving robots | Carvalho and Nolfi | 2016 | agent | global | 1st | stable | no | micro | no | no | exploration | Regression | yes | action selection | online | traversability | mathematical | NN | depth, haptic | self-supervised | simulation | original study | |

Using Object Affordances to Improve Object Recognition | Castellini et al. | 2011 | agent | meso | 1st | stable | no | micro | no | no | demonstration | Regression | yes | action selection | offline | grasping | mathematical | MLP, SVM, k-means, histograms | SIFT BoW, contact joints | supervised | benchmark | original study | |

A Probabilistic Concept Web on a Humanoid Robot | Celikkanat at al. | 2015 | agent | meso | 1st | stable | no | micro | no | no | exploration | Optimization | no | language | offline | pushing, grasping, throwing, shaking | mathematical | MRF, Loopy belief propagation | depth, haptic, proprioceptive and audio | semi-supervised | real robot | original study | |

Determining proper grasp configurations for handovers through observation of object movement patterns and inter-object interactions during usage | Chan et al. | 2014 | agent | meso | 1st | stable | no | micro | yes | no | demonstration | Optimization | yes | action selection | online | grasping | mathematical | k-means, nearest neighbor | pose, action-object relation | unsupervised | real robot | original study | |

A Bio-Inspired Robot with Visual Perception of Affordances | Chang | 2015 | agent | meso | 1st | stable | no | micro | no | no | ground truth | Classification | yes | action selection | offline | cutting, painting | neural | AL, MB | ANN | edges, TSSC | supervised | real robot | original study |

DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving | Chen et al. | 2015 | agent | global | 1st | stable+variable | no | micro | no | no | ground truth | Regression | yes | planning | offline | traversability | mathematical | CNN | RGB images, motor controls | supervised | simulation | original study | |

Learning haptic affordances from demonstration and human-guided exploration | Chu et al. | 2016 | agent | meso | 1st | stable | no | micro | no | no | demonstration+exploration | Classification | yes | action selection | offline | openable, scoopable | mathematical | HMM | forces and torques | supervised | real robot | original study | |

Learning Object Affordances by Leveraging the Combination of Human-Guidance and Self-Exploration | Chu et al. | 2016 | observer | meso | 1st | stable | no | micro | no | no | demonstration+exploration | Optimization | no | action selection | offline | pushing, openning, turning | mathematical | HMM | color, size, pose, force torque, robot arm pose | self-supervised | real robot | original study | |

Learning Affordances of Consummatory Behaviors: Motivation-Driven Adaptive Perception | Cos et al. | 2010 | agent | meso | 1st | stable | no | micro | no | no | exploration | Classification | yes | planning | online | general | mathematical | GWRN+RL | illumination | supervised | simulation | original study | |

Training Agents with Interactive Reinforcement Learning and Contextual Affordances | Cruz et al. | 2016 | agent | meso | 1st | stable+variable | no | micro | no | no | ground truth | Classification | no | action selection | offline | manipulation, locomotion | mathematical | DMLP | agent state, action, object | unsupervised | simulation | original study | |

Interactive reinforcement learning through speech guidance in a domestic scenario | Cruz et al. | 2015 | agent | meso | 1st | stable | no | micro | no | no | ground truth | Classification | no | action selection | offline | graspable, dropable, moveable, cleanable | mathematical | DMLP | robot state, intended action, object information | supervised | simulation | original study | |

A Cognitive Control Architecture for the Perception–Action Cycle in Robots and Agents | Cutsuridis and Taylor | 2013 | agent | meso | 1st | stable | yes | micro | yes | no | exploration | Inference | yes | action selection | online | grasping | neural | AIP, DVv | ANN | shape | unsupervised | real robot | original study |

Learning Affordances for Categorizing Objects and Their Properties | Dag et al | 2010 | observer | meso | 1st | stable | no | micro | no | no | demonstration | Classification | no | single-/multi-step prediction | offline | manipulation | mathematical | - | SVM, k-means, spectral clustering | 3D position, orientation, shape, size | unsupervised | benchmark | original study |

Semantic grasping: planning task-specific stable robotic grasps | Dang and Allen | 2014 | agent | local | 1st | stable | no | micro | yes | no | exploration | Optimization | yes | action selection | offline | grasping | mathematical | Nearest Neighbor | grasp, shape context | supervised | real robot | original study | |

Denoising Auto-encoders for Learning of Objects and Tools Affordances in Continuous Space | Dehban et al. | 2016 | agent | meso | 1st | stable | no | micro | no | no | exploration | Inference | yes | action selection | online | pulling, dragging | mathematical | Denoising auto-encoders | 2D shape, object displacement | unsupervised | simulation+real robot | original study | |

Predicting Functional Regions on Objects | Desai and Ramanan | 2013 | agent | global | 0th | stable | no | micro | no | no | ground truth | Optimization | yes | not specified | offline | grasping, support | mathematical | Deformable Part Models | HOG | supervised | benchmark | original study | |

Learning object-specific grasp affordance densities | Detry et al. | 2009 | agent | meso | 1st | stable | no | micro | no | no | exploration | Optimization | no | planning | online | grasping | mathematical | not specified | KDE, Sampling | ECV | self-supervised | real robot | original study |

Learning grasp affordance densities | Detry et al. | 2011 | agent | meso | 1st | stable | no | micro | no | no | exploration | Optimization | no | planning | online | grasping | mathematical | not specified | KDE, Sampling | ECV | self-supervised | real robot | original study |

Refining Grasp Affordance Models by Experience | Detry et al. | 2010 | agent | meso | 1st | stable | no | micro | no | no | exploration | Optimization | no | action selection | online | grasping | mathematical | KDE, Sampling | ECV | self-supervised | real robot | original study | |

From primitive behaviors to goal-directed behavior using affordances | Dogar et al. | 2007 | agent | meso | 2nd | stable | yes | micro | yes | yes | exploration | Classification | yes | action selection | offline | traversability | mathematical | not specified | k-Means, SVM | shape, distance | unsupervised | real robot | original study |

Ecological Robotics | Duchon et al. | 1998 | agent | global | 1st | variable | no | micro | no | no | exploration | Optimization | yes | planning | online | locomotion, survival | mathematical | law of control | optical flow | not specified | simulation+real robot | original study | |

Predicting the Intention of Human Activities for Real-Time Human-Robot Interaction (HRI) | Dutta and Zielinski | 2016 | agent | meso | 1st | stable | no | micro | yes | no | ground truth | Optimization | yes | action selection | offline | reachable, pourable, movable, drinkable | mathematical | Heat maps | angular, location + dist. to object, sematic labels | supervised | simulation | original study | |

Discrete fuzzy grasp affordance for robotic manipulators | Eizicovits et al. | 2012 | agent | meso | 1st | stable | no | micro | yes | no | demonstration | Optimization | no | planning | offline | grasping | mathematical | Affordance manifolds | wrist location, roll angle | supervised | real robot | original study | |

Learning structural affordances through self-exploration | Erdemir et al. | 2012 | agent | global | 1st | stable | no | micro | no | no | exploration | Classification | yes | planning | offline | crawling | mathematical | SOM, k-means, LVQ | fixation point, motor values | semi-supervised | real robot | original study | |

A robot rehearses internally and learns an affordance relation | Erdemir et al. | 2008 | agent | meso | 1st | stable | no | micro | no | no | exploration | Regression | no | planning | offline | traversabilty | mathematical | GMM | object edges | self-supervised | simulation+real robot | original study | |

Learning probabilistic discriminative models of grasp affordances under limited supervision | Erkan et al. | 2010 | agent | meso | 1st | stable | no | micro | yes | no | ground truth | Optimization | yes | action selection | online | grasping | mathematical | Kernel Logistic Regression | ECV | semi-supervised | real robot | original study | |

Bootstrapping Relational Affordances of Object Pairs using Transfer | Fichtl et al. | 2016 | agent | meso | 1st | stable | no | micro | yes | no | exploration | Classification | yes | single-/multi-step prediction | online | rake, pull/sh, move, lift, take, pour, slide | mathematical | Random Forests | pose, size; relational hist.feat./PCA on PCL | semi-supervised | simulation | original study | |

Learning About Objects Through Action - Initial Steps Towards Artificial Cognition | Fitzpatrick et al. | 2003 | agent | meso | 1st | stable | no | micro | no | no | exploration | Optimization | no | action selection | offline | general | neural | F5/AIP | Histogram | shape, identity | unsupervised | real robot | original study |

Neural Model for the Visual Recognition of Goal-Directed Movements | Fleischer et al. | 2008 | agent | meso | 1st | stable | no | micro | yes | no | ground truth | Classification | yes | action selection | offline | grasping | neural | AIP | NN, k-means | orientation, object + hand shape, saliency of feat. | unsupervised | benchmark | original study |

Learning Predictive Features in Affordance based Robotic Perception Systems | Fritz et al. | 2006 | agent | meso | 1st | stable | no | micro | no | no | ground truth | Optimization | yes | action selection | offline | lifting | mathematical | k-means, MAP, decision tree | SIFT, color, mass-center, shape descr., actuator | supervised | simulation | original study | |

Visual Learning of Affordance Based Cues | Fritz et al. | 2006 | agent | meso | 1st | stable | no | micro | no | no | ground truth | Classification | no | action selection | offline | lifting | mathematical | Nearest Neighbor, C4.5 Decision tree | SIFT | self-supervised | simulation | original study | |

Synergy-based affordance learning for robotic grasping | Geng et al. | 2013 | agent | meso | 2nd | stable | no | micro | yes | no | demonstration+exploration | Classification | yes | planning | offline | grasping | neural | VIP, CIPS, 7a, 7b, AIP | Growing Neural Gas (GNG) | not specified | unsupervised | real robot | original study |

Object recognition using visuo-affordance maps | Gijsberts et al. | 2010 | agent | meso | 1st | stable | no | micro | no | no | ground truth | Regression | yes | single-/multi-step prediction | offline | grasping | mathematical | Regularized Least Squares | SIFT | supervised | benchmark | original study | |

Towards Lifelong Affordance Learning using a Distributed Markov Model | Glover and Wyeth | 2016 | agent | meso | 1st | stable | no | micro | no | no | exploration | Inference | yes | action selection | online | grasping | mathematical | Distributed Markov Model | object pose, tactile readings | unsupervised | real robot | original study | |

Learning visual affordances of objects and tools through autonomous robot exploration | Gonçalves et al. | 2014 | agent | meso | 1st | stable | no | micro | no | no | exploration | Inference | yes | action selection | offline | pulling, dragging | mathematical | BN | 2D geom. feat., 2D tracked object displacement | self-supervised | simulation | original study | |

Learning intermediate object affordances: Towards the development of a tool concept | Gonçalves et al. | 2014 | agent | meso | 1st | stable | no | micro | no | no | exploration | Inference | yes | action selection | offline | pulling, dragging | mathematical | BN, PCA, BN structure learning | 2D geom. feat., 2D tracked object displacement | self-supervised | simulation+real robot | original study | |

A Behavior-Grounded Approach to Forming Object Categories: Separating Containers From Noncontainers | Griffitth et al. | 2012 | agent | meso | 1st | stable | no | micro | no | no | exploration | Classification | yes | action selection | offline | drop, move, grasping, /shake | mathematical | SOM, Spectral clustering, PCA and k-NN | auditory and visual feature trajectories, depth | unsupervised | real robot | original study | |

Affordance in Autonomous Robot | Hakura et al. | 1996 | agent | meso | 1st | stable | no | micro | no | no | exploration | Classification | yes | action selection | online | traversability | mathematical | ART, RL | pulse sensor readings | semi-supervised | simulation | original study | |

Intrinsically Motivated Affordance Discovery and Modeling | Hart and Grupen | 2013 | agent | meso | 1st | stable | no | micro | yes | no | exploration | Optimization | yes | action selection | online | grasping | mathematical | RL | hue, shape, pose of object | unsupervised | real robot | original study | |

Attribute Based Affordance Detection from Human-Object Interaction Images | Hassan and Dharmaratne | 2016 | observer | meso | 1st | stable | no | micro | no | no | demonstration | Classification | yes | action selection | offline | general | mathematical | BN, SVM | SIFT, HOG, textons, color hist., object attributes | supervised | benchmark | original study | |

Affordance-Based Grasp Planning for Anthropomorphic Hands from Human Demonstration | Hendrich and Bernardino | 2014 | agent | meso | 1st | stable | no | micro | no | no | demonstration | Regression | yes | action selection | offline | grasping | mathematical | PCA, FK, IK | shape, size | supervised | real robot | original study | |

Decoupling behavior, perception, and control for autonomous learning of affordances | Hermans et al. | 2013 | agent | meso | 1st | stable | no | micro | no | no | exploration | Regression | yes | action selection | online | pushing, pulling | mathematical | Feedback control | pose, depth | supervised | real robot | original study | |

Learning Contact Locations for Pushing and Orienting Unknown Objects | Hermans et al. | 2013 | agent | meso | 1st | stable | no | micro | yes | no | exploration | Regression | yes | action selection | offline | pushing | mathematical | SVR | Histogram of points (in pose space) | semi-supervised | real robot | original study | |

Hallucinated Humans as the Hidden Context for Labeling 3D Scenes | Jiang et al. | 2013 | agent | meso | 1st | stable | no | micro | no | no | demonstration | Optimization | yes | not specified | offline | general | mathematical | Infinite Factory Topic Models (DPPM) | human pose, object pose | supervised | benchmark | original study | |

Extracting whole-body affordances from multimodal exploration | Kaiser et al. | 2014 | agent | global | 1st | stable | no | micro | no | no | hardcoded | Inference | yes | action selection | offline | support, lean, grasping, hold | mathematical | Reasoning | surface characteristics | supervised | real robot | original study | |

Validation of Whole-Body Loco-Manipulation Affordances for Pushability and Liftability | Kaiser et al. | 2015 | agent | global | 2nd | stable | no | micro | no | no | hardcoded | Inference | no | action selection | not specified | pushing, lifting | mathematical | RANSAC, clustering | surface normals, area | not specified | real robot | original study | |

Representation and extraction of image feature associated with maneuvering affordance | Kamejima | 2002 | agent | global | 1st | stable | no | micro | no | no | exploration | Regression | yes | planning | online | maneuverability | mathematical | Directional Fourier Imaging, Self similarity | scene image | unsupervised | real robot | original study | |

Anticipative generation and in-situ adaptation of maneuvering affordance in naturally complex scene | Kamejima et al. | 2008 | agent | global | 1st | stable+variable | no | micro | no | no | exploration | Classification | yes | planning | online | maneuverability | mathematical | Fractal Coding | scene image | unsupervised | real robot | original study | |

Perceiving, learning, and exploiting object affordances for autonomous pile manipulation | Katz et al. | 2014 | agent | local | 0th | stable | no | macro | yes | yes | ground truth | Classification | yes | action selection | offline | pushing, pulling, grasping | mathematical | SVM, PCA, Mean shift | PCA axes, size, center of gravity | supervised | real robot | original study | |

Semantic Labeling of 3D Point Clouds with Object Affordance for Robot Manipulation | Kim et al. | 2014 | agent | local | 0th | stable | no | micro | no | no | ground truth | Regression | yes | not specified | offline | pushing, lifting, grasping | mathematical | Logistic regression, k-means | geometric features | supervised | benchmark | original study | |

Interactive Affordance Map Building for a Robotic Task | Kim et al. | 2015 | agent | local | 2nd | variable | no | micro | no | no | ground truth | Optimization | yes | single-/multi-step prediction | offline | pushing | mathematical | Logistic regression, MRF | geometric features | supervised | simulation | original study | |

Traversability classification using unsupervised on-line visual learning for outdoor robot navigation | Kim et al. | 2006 | agent | global | 1st | stable | no | macro | no | no | exploration | Classification | yes | planning | online | traversability | mathematical | Clustering, Classification | 3D pixel information, texture | self-supervised | real robot | original study | |

Visual object-action recognition: Inferring object affordances from human demonstration | Kjellström et al. | 2011 | agent | meso | 0th | stable | no | micro | no | no | demonstration | Classification | no | not specified | offline | open, pour, hammer | mathematical | Factorial CRF | spatial pyramids of HoG | supervised | benchmark | original study | |

Physically Grounded Spatio-temporal Object Affordances | Koppula et al | 2014 | observer | meso | 2nd | stable | no | macro | yes | yes | ground truth | Optimization | yes | action selection | offline | general | mathematical | Graphical model, GPR | human pose, feat. w.r.t. skeleton joints / objects | supervised | benchmark | original study | |

Learning human activities and object affordances from RGB-D videos | Koppula et al. | 2013 | observer | meso | 2nd | stable | yes | micro | yes | yes | ground truth | Classification | yes | action selection | online | general | mathematical | not specified | SVM (MRF, kNN, Particle Filter) | BB, centroid, SIFT | supervised | benchmark | original study |

Collision risk assessment for autonomous robots by offline traversability learning | Kostavelis et al. | 2012 | agent | global | 1st | stable | no | micro | no | no | ground truth | Classification | yes | single-/multi-step prediction | offline | traversability | mathematical | SVM | dispartiy maps, hist. of pixel distribution | supervised | real robot | original study | |

A kernel-based approach to direct action perception | Kroemer et al. | 2012 | agent | local | 1st | stable | no | micro | yes | no | demonstration | Regression | yes | action selection | offline | pouring, grasping | mathematical | not specified | non-parametric surface kernel, kernel logistic regression, DMP | pointclouds | supervised | real robot | original study |

A Flexible Hybrid Framework for Modeling Complex Manipulation Tasks | Kroemer et al. | 2011 | observer | meso | 1st | stable | no | macro | yes | yes | hardcoded | Optimization | no | planning | offline | grasping, pushing, striking | mathematical | RL | pose | supervised | real robot | original study | |

A perceptual system for vision-based evolutionary robotics | Kubota et al. | 2003 | agent | global | 1st | variable | no | micro | no | no | exploration | Optimization | yes | action selection | online | traversability | mathematical | SSGA, clustering | optical flow | unsupervised | real robot | original study | |

Goal-oriented Dependable Action Selection using Probabilistic Affordance | Lee et al. | 2010 | agent | meso | 1st | stable | no | micro | yes | yes | ground truth | Classification | yes | action selection | offline | general | mathematical | multilayer naive Bayesian classifier | not specified | supervised | real robot | original study | |

Skill Learning and Inference Framework for Skilligent Robot | Lee et al. | 2013 | agent | meso | 1st | stable | no | micro+macro | yes | yes | demonstration | Optimization | no | action selection | offline | general | mathematical | BN, DMP | trajectories (joints and end-effectors) | supervised | real robot | original study | |

Foot Placement Selection Using Non-geometric Visual Properties | Lewis et al. | 2005 | agent | global | 1st | stable | no | micro | yes | no | exploration | Classification | yes | planning | online | locomotion | mathematical | NN | color, texture | supervised | simulation+real robot | original study | |

Affordance-based imitation learning in robots | Lopes et al. | 2007 | agent | meso | 1st | stable | no | micro | yes | no | demonstration+exploration | Optimization | yes | action selection | offline | grasping, tapping, touching | mathematical | BN, RL | shape, color, scale | semi-supervised | real robot | original study | |

Responding to affordances: Learning and Projecting a Sensorimotor Mapping | MacDorman | 2000 | agent | meso | 1st | stable | no | micro | no | yes | exploration | Classification | no | planning | online | navigation | mathematical | Partition Nets | color | self-supervised | simulation | original study | |

Multi-model approach based on 3D functional features for tool affordance learning in robotics | Mar et al. | 2015 | agent | meso | 1st | stable+variable | no | micro | yes | no | exploration | Regression | yes | not specified | offline | pulling/dragging | mathematical | not specified | SOM, k-means, GRNN | OMS-EGI (3D) | unsupervised | real robot | original study |

Self-supervised learning of grasp dependent tool affordances on the iCub Humanoid robot | Mar et al. | 2015 | agent | meso | 1st | stable+variable | no | micro | no | no | exploration | Classification | yes | single-/multi-step prediction | offline | pulling, dragging | mathematical | not specified | SVM, K-means. | 2D geometrical features | self-supervised | simulation+real robot | original study |

Extending sensorimotor contingency theory: prediction, planning, and action generation | Maye & Engl | 2013 | agent | global | 1st | stable+variable | no | micro | no | no | exploration | Inference | yes | single-/multi-step prediction | online | traversability | mathematical | SMC, Markov models | not specified | unsupervised | real robot | original study | |

Better Vision through Manipulation | Metta & Fitzpatrick | 2003 | agent | meso | 1st | stable | no | micro | no | no | exploration | Classification | no | action selection | online | rollability | neural | AIP-F5 | Clustering | color | unsupervised | real robot | original study |

Affordance Learning Based on Subtask's Optimal Strategy | Min et al | 2015 | agent | meso | 1st | stable+variable | no | micro | no | no | exploration | Inference | no | action selection | online | locomotion | mathematical | HRL | shape | supervised | simulation+real robot | original study | |

The initial development of object knowledge by a learning robot | Modayil et al. | 2008 | agent | meso | 1st | stable | yes | micro | no | no | exploration | Optimization | yes | planning | online | manipulability | mathematical | clustering, utility functions | shape | unsupervised | real robot | original study | |

From object-action to property-action: Learning causally dominant properties through cumulative explorative interactions | Mohan et al. | 2014 | agent | meso | 1st | stable | no | micro | no | no | exploration | Classification | yes | planning | online | reach, grasp, push, search | mathematical | SOMs | size, color, shape, world map | unsupervised | real robot | original study | |

Learning relational affordance models for robots in multi-object manipulation tasks | Moldovan et al. | 2012 | agent | global | 2nd | stable | no | micro | yes | yes | ground truth | Inference | no | single-/multi-step prediction | offline | general | mathematical | not specified | Stastistical Relational Learning | not specified | unsupervised | real robot | original study |

Occluded Object Search by Relational Affordances | Moldovan et al. | 2014 | agent | meso | 0th | stable | no | micro | yes | no | hardcoded | Optimization | no | action selection | offline | general | mathematical | BN | geometric properties | supervised | simulation | original study | |

Learning Grasping Affordances From Local Visual Descriptors | Montesano and Lopes | 2009 | agent | local | 1st | stable | no | micro | no | no | exploration | Inference | yes | action selection | offline | grasping | mathematical | not specified | Bayes | Gaussian, Sobel, Laplacian Filters | unsupervised | real robot | original study |

Modelling Affordances Using Bayesian Networks | Montesano et al. | 2007 | agent | meso | 1st | stable | no | micro | no | no | exploration | Inference | no | action selection | offline | general | mathematical | not specified | BN, MCMC | color, shape, size, position; robot gripper pose | unsupervised | real robot | original study |

Learning Object Affordances: From Sensory-Motor Coordination to Imitation | Montesano et al. | 2008 | agent | meso | 2nd | stable | no | micro | yes | no | exploration | Inference | yes | single-/multi-step prediction | offline | general | mathematical | not specified | BN, MCMC | convexity, compactness, circleness, squareness | unsupervised | real robot | original study |

Affordances, development and imitation | Montesano et al. | 2007 | agent | meso | 1st | stable | no | micro | yes | no | demonstration | Optimization | no | action selection | offline | grasping, taping | mathematical | BN | color, shape, size | supervised | real robot | original study | |

Case Studies of Applying Gibson’s Ecological Approach to Mobile Robots | Murphy | 1999 | agent | meso | 1st | stable | no | micro | no | no | hardcoded | Classification | no | action selection | offline | docking, path following, picking | mathematical | Hard-coded perceptual affordance detectors | HC perceptual affordance detectors | not specified | real robot | original study | |

Affordance Estimation For Vision-Based Object Replacement on a Humanoid Robot | Mustafa et al. | 2016 | agent | meso | 2nd | stable | no | micro | yes | no | ground truth | Classification | yes | action selection | offline | general | mathematical | not specified | JointSVM | 3D texlets | unsupervised | real robot | original study |

Affordance Detection of Tool Parts from Geometric Features | Myers et al. | 2015 | agent | local | 0th | stable | no | micro | no | no | ground truth | Inference | yes | action selection | offline | general | mathematical | not specified | SRF | Depth, SNorm, PCurv, SI+CV | unsupervised | benchmark | original study |

Structural Feature Extraction based on Active Sensing Experiences | Nishide et al. | 2008 | agent | meso | 1st | stable | no | micro+macro | no | no | exploration | Regression | no | single-/multi-step prediction | offline | pushing | mathematical | RNNPB, hierarchical NN | shape, motion | supervised | real robot | original study | |

Active Sensing based Dynamical Object Feature Extraction | Nishide et al. | 2008 | agent | meso | 1st | stable | no | micro+macro | no | no | exploration | Regression | no | single-/multi-step prediction | offline | pushing | mathematical | RNNPB, hierarchical NN | shape, motion | supervised | real robot | original study | |

Modeling Tool-Body Assimilation using Second-order Recurrent Neural Network | Nishide et al. | 2009 | agent | global | 1st | stable | no | micro+macro | no | yes | exploration | Regression | yes | single-/multi-step prediction | offline | pulling, dragging | mathematical | SOM, Multiple time-scales RNN | SOM object feature from image | supervised | real robot | original study | |

Tool–Body Assimilation of Humanoid Robot Using a Neurodynamical System | Nishide et al. | 2012 | agent | meso | 1st | stable | no | micro | no | no | exploration | Regression | yes | action selection | offline | manipulability | mathematical | SOM, MTRNN, HNN | SOM output | semi-supervised | real robot | original study | |

Generation of behavior automaton on neural network | Ogata et al. | 1997 | agent | global | 1st | stable | no | micro | no | no | exploration | Classification | yes | planning | online | traversability | mathematical | SOM, Temporal Sequence Network | SOM output | semi-supervised | simulation | original study | |

Symbol Generation and Feature Selection for Reinforcement Learning Agents Using Affordances and U-Trees | Oladell et al. | 2012 | agent | meso | 1st | stable | no | micro | yes | no | hardcoded | Optimization | no | action selection | offline | lifting, dropping, stacking | mathematical | MDP | location, shape, color | supervised | simulation | original study | |

Autonomous acquisition of pushing actions to support object grasping with a humanoid robot | Omrcen et al. | 2009 | agent | meso | 1st | stable | not specified | micro+macro | yes | no | exploration | Optimization | yes | single-/multi-step prediction | offline | grasping, pushing | mathematical | NN | objecet image | supervised | real robot | original study | |

Reinforcement Learning of Predictive Features in Affordance Perception | Paletta & Fritz | 2008 | agent | meso | 1st | stable+variable | no | micro | no | no | exploration | Classification | yes | planning | online | liftability | mathematical | Q-Learning, k-means | SIFT | supervised | simulation | original study | |

Perception and Developmental Learning of Affordances in Autonomous Robots | Paletta et al. | 2007 | agent | local | 1st | stable | yes | micro | no | no | ground truth | Optimization | no | action selection | offline | lifting | mathematical | MDP | SIFT, color, shape | supervised | real robot | original study | |

Affordance-feasible planning with manipulator wrench spaces | Price et al. | 2016 | agent | meso | 1st | stable | no | micro | no | yes | exploration | Classification | yes | planning | offline | grasping | mathematical | BN | wrenches | not specified | simulation+real robot | original study | |

Bio-inspired Model of Robot Adaptive Learning and Mapping | Ramierz & Widel | 2006 | agent | global | 1st | stable | no | micro | yes | no | exploration | Classification | yes | action selection | online | traversability | neural | hippocampus | RL, Hebbian Learning | color | supervised | real robot | original study |

Increasing the Autonomy of Mobile Robots by On-line Learning Simultaneously at Different Levels of Abstraction | Richert et al | 2008 | agent | meso | 1st | stable | no | micro | yes | no | exploration | Classification | yes | action selection | online | traversability | mathematical | RL, Decision Trees | color, distance, angle | supervised | simulation | original study | |

Action-grounded push affordance bootstrapping of unknown objects | Ridge and Ude | 2013 | agent | meso | 1st | stable | no | micro | no | no | exploration | Classification | yes | single-/multi-step prediction | online | pushing | mathematical | not specified | SOM, LVQ, Hebbian learning, K-means | action-grounded 3D shape | self-supervised | real robot | original study |

Self-supervised cross-modal online learning of basic object affordances for developmental robotic systems | Ridge et al. | 2010 | agent | meso | 1st | stable | no | micro | no | no | exploration | Classification | yes | single-/multi-step prediction | online | pushing | mathematical | not specified | SOM, LVQ, Hebbian learning, K-means | 2D,3D shape, 2D motion | self-supervised | real robot | original study |

Self-supervised Online Learning of Basic Push Affordances | Ridge et al. | 2015 | agent | meso | 1st | stable | no | micro | no | no | exploration | Classification | yes | single-/multi-step prediction | online | pushing | mathematical | not specified | SOM, LVQ, Hebbian learning, K-means | 2D,3D shape and motion | self-supervised | real robot | original study |

The MACS Project: An Approach to Affordance-Inspired Robot Control | Rome et al. | 2008 | agent | global | 1st | stable | yes | micro | no | no | ground truth | Classification | no | planning | offline | lifting, trabersability | mathematical | Nearest Neighbor | SIFT | supervised | real robot | original study | |

A Multi-scale CNN for Affordance Segmentation in RGB Images | Roy et al. | 2016 | agent | global | 0th | stable | no | micro | no | no | ground truth | Classification | yes | not specified | offline | walkable, sittable, lyable, and reachable | mathematical | Multi-Scale CNN | RGB+D, surface normals, semantic labels | supervised | benchmark | original study | |

Learning the Consequences of Actions: Representing Effects as Feature Changes | Rudolph et al. | 2010 | agent | meso | 1st | stable | no | micro | no | no | demonstration | Inference | yes | single-/multi-step prediction | online | general | mathematical | BN | object, world, meta (object-object) features | supervised | simulation | original study | |

To Afford or Not to Afford: A New Formalization of Affordances Toward Affordance-Based Robot Control | Şahin et al. | 2007 | agent | meso | 1st | stable | no | micro | no | no | exploration | Classification | yes | planning | online | general | mathematical | not specified | SVM, STRIPS | not specified | not specified | real robot | original study |

The acquisition of intentionally indexed and object centered affordance gradients: A biomimetic controller and mobile robotics benchmark | Sánchez-Fibla et al. | 2011 | agent | meso | 1st | stable | no | micro | no | no | exploration | Regression | yes | action selection | online | pushing | mathematical | Affordance gradients | shape, position, orientation | unsupervised | real robot | original study | |

A Logic-based Computational Framework for Inferring Cognitive Affordances | Sarathy & Scheutz | 2016 | agent | meso | 1st | stable+variable | no | micro | no | no | demonstration+exploration | Inference | yes | planning | online | cognitive affordance | mathematical | Logic Programming | visual information | unsupervised | simulation | original study | |

Bootstrapping the Semantics of Tools: Affordance Analysis of Real World Objects on a Per-part Basis | Schoeler and Wörgötter | 2015 | agent | local | 1st | stable | no | micro | no | no | ground truth | Classification | yes | action selection | offline | general | mathematical | not specified | SVM | SHOT, ESF | unsupervised | benchmark | original study |

Bayesian Network Model for Object Concept | Shinchi et al. | 2007 | agent | meso | 0th | stable | no | micro | no | no | demonstration | Inference | yes | not specified | online | general | mathematical | BN | color, contour, barycentric pos., num. of objects | unsupervised | benchmark | original study | |

Learning and generalization of behavior-grounded tool affordances | Sinapov and Stoytchev | 2007 | agent | meso | 1st | stable+variable | no | micro | no | no | exploration | Classification | yes | single-/multi-step prediction | offline | pulling, dragging | mathematical | not specified | K-NN, decision tree. | changes in raw pixels | self-supervised | simulation | original study |

Detecting the functional similarities between tools using a hierarchical representation of outcomes | Sinapov and Stoytchev | 2008 | agent | meso | 1st | stable | no | micro | no | no | exploration | Classification | no | single-/multi-step prediction | online | pulling, dragging | mathematical | not specified | X-Means, Ensemble of C4.5 Decision tree classifiers. | raw pixels, trajectories | self-supervised | simulation | original study |

Learning to Detect Visual Grasp Affordances | Song et al. | 2016 | agent | meso | 1st | stable | no | micro | no | no | ground truth | Classification | yes | action selection | offline | grasping | mathematical | not specified | MMR | BB, category, texture | supervised | real robot | original study |

Learning Task Constraints for Robot Grasping using Graphical Models | Song et al. | 2010 | agent | meso | 1st | stable | no | micro | yes | no | ground truth | Optimization | yes | action selection | offline | grasping | mathematical | BN (GMM, Multinomial distribution) | size, convexity, grasp pose | supervised | simulation | original study | |

Visual Grasp Affordances From Appearance-Based Cues | Song et al. | 2011 | agent | meso | 0th | stable | no | micro | yes | no | ground truth | Regression | no | not specified | offline | grasping | mathematical | MMR | local features, HOG | supervised | benchmark | original study | |

Predicting Human Intention in Visual Observations of Hand/Object Interactions | Song et al. | 2013 | observer | meso | 1st | stable | not specified | micro | yes | no | demonstration | Optimization | no | action selection | offline | grasping | mathematical | BN, GMM, SOM | grasp parameters, dimension | supervised | real robot | original study | |

Embodiment-Specific Representation of Robot Grasping using Graphical Models and Latent-Space Discretization | Song et al. | 2011 | observer | meso | 1st | stable | not specified | micro | yes | no | demonstration | Optimization | yes | action selection | offline | grasping | mathematical | BN, Gaussian Latent Variable Model | grasp parameters, dimension | supervised | simulation | original study | |

Task-Based Robot Grasp Planning Using Probabilistic Inference | Song et al. | 2015 | agent | meso | 1st | stable | not specified | micro | yes | no | demonstration | Optimization | yes | action selection | offline | general | mathematical | BN | shape, grasp parameters | supervised | simulation+real robot | original study | |

Functional object class detection based on learned affordance cues | Stark et al. | 2008 | agent | meso | 0th | stable | no | micro | no | no | demonstration | Classification | yes | action selection | offline | grasping | mathematical | not specified | KDE, Hough transform | k-adjacent segments, ISM | supervised | real robot | original study |

Learning the Affordances of Tools Using a Behavior-Grounded Approach | Stoytchev | 2008 | agent | meso | 2nd | stable+variable | no | micro | yes | yes | exploration | Inference | no | action selection | online | grasping | mathematical | not specified | Grounding | position, color | unsupervised | real robot | original study |

Behavior-Grounded Representation of Tool Affordances | Stoytchev | 2005 | agent | meso | 1st | stable | no | micro | no | no | exploration | Classification | no | action selection | offline | extend, slide, contract | mathematical | Affordance table | position, color | self-supervised | real robot | original study | |

Behavior-Grounded Representation of Tool Affordances | Stoytchev | 2005 | agent | global | 1st | stable+variable | no | micro+macro | no | yes | exploration | Regression | no | single-/multi-step prediction | online | pulling, dragging, pushing, grasping | mathematical | Probabilistic lookup table | object postion, tool color | unsupervised | real robot | original study | |

A Bayesian Approach Towards Affordance Learning in Aritifical Agents | Stramandinoli et al. | 2015 | agent | meso | 1st | stable | no | micro | no | no | exploration | Optimization | no | action selection | offline | general | mathematical | not specified | BN, MLE | not specified | unsupervised | real robot | original study |

Learning Visual Object Categories for Robot Affordance Prediction | Sun et al. | 2010 | agent | meso | 1st | stable | no | micro | no | yes | ground truth | Classification | no | planning | offline | locomotion | mathematical | BN, EM, GM, DIRECT | color, edge | supervised | real robot | original study | |

A model of shared grasp affordances from demonstration | Sweeney and Grupen | 2007 | agent | meso | 1st | stable | no | micro | no | no | demonstration | Inference | yes | action selection | offline | grasping | mathematical | not specified | BN, MLE | moment feature | supervised | real robot | original study |

Knowledge Propagation and Relation Learning for Predicting Action Effects | Szedmak et al. | 2014 | agent | meso | 1st | stable | no | micro | no | no | ground truth | Classification | yes | not specified | offline | object | mathematical | not specified | MMMVR | shape, size | supervised | benchmark | original study |

Perception driven robotic assembly based on ecological approach | Tagawa et al. | 2002 | agent | meso | 1st | stable | no | micro | no | no | exploration | Inference | yes | action selection | online | general (positive and negative) | mathematical | Genetic Algorithms, State-machines | object postion | unsupervised | simulation | original study | |

Localizing Handle-like Grasp Affordances In 3D Point Clouds | Ten Pas et al | 2016 | agent | local | 0th | stable | no | micro | no | no | hardcoded | Optimization | yes | action selection | not specified | grasping | mathematical | Importance sampling, quadratic surface fitting | curvature, circle fitting | not specified | real robot | original study | |

Exploring affordances and tool use on the iCub | Tikhanoff et al. | 2013 | agent | meso | 1st | stable | no | micro | yes | no | exploration | Regression | no | single-/multi-step prediction | online | pulling, dragging | mathematical | not specified | Linear SVM, least squares | SIFT, pull angle, tracked dist. | supervised | real robot | original study |

Traversability: A Case Study for Learning and Perceiving Affordances in Robots | Ugur et al. | 2010 | agent | global | 1st | stable | yes | micro | no | no | exploration | Classification | yes | action selection | offline | traversability | mathematical | not specified | SVM | shape, size | self-supervised | real robot | original study |

Goal emulation and planning in perceptual space using learned affordances | Ugur et al. | 2011 | agent | meso | 1st | stable | no | micro | no | yes | exploration | Classification | yes | planning | offline | object | mathematical | not specified | X-means, SVM | shape, size | unsupervised | real robot | original study |

Staged Development of Robot Skills: Behavior Formation, Affordance Learning and Imitation | Ugur et al. | 2015 | agent | meso | 1st | stable | no | micro | no | yes | demonstration+exploration | Classification | yes | single-/multi-step prediction | offline | object | mathematical | not specified | DTW, X-means, SVM, EM | shape, size | unsupervised | real robot | original study |

Emergent structuring of interdependent affordance learning tasks using intrinsic motivation and empirical feature selection | Ugur et al. | 2016 | agent | meso | 1st | stable | yes | micro | no | no | ground truth | Classification | yes | action selection | online | object | mathematical | not specified | SVM, intrinsic motivation | shape, size | supervised | benchmark | original study |

Bottom-Up Learning of Object Categories, Action Effects and Logical Rules: From Continuous Manipulative Exploration to Symbolic Planning | Ugur et al. | 2015 | agent | meso | 1st | stable+variable | no | micro | no | yes | exploration | Classification | yes | planning | offline | object | mathematical | not specified | SVM, C4.5 Decision tree, X-means, PDDL | shape, size | unsupervised | real robot | original study |

AfNet: The Affordance Network | Varadarajan et al. | 2012 | agent | local | 0th | stable | no | micro | no | no | ground truth | Classification | yes | action selection | offline | general | mathematical | not specified | superquadrics | supervised | benchmark | original study | |

AfRob: The Affordance Network Ontology for Robots | Varadarajan et al. | 2012 | agent | local | 0th | stable | no | micro | no | no | ground truth | Classification | yes | action selection | offline | general | mathematical | not specified | gradient image, superquadrics | supervised | benchmark | original study | |

Predicting slippage and learning manipulation affordances through Gaussian Process regression | Vina et al | 2013 | agent | meso | 1st | stable | no | micro | yes | no | exploration | Regression | yes | planning | offline | grasping | mathematical | GP | hand-object relative pose | supervised | real robot | original study | |

Robot Learning and Use of Affordances in Goal-directed Tasks | Wang et al. | 2013 | agent | global | 1st | variable | no | micro | no | no | exploration | Optimization | no | action selection | online | moveability | mathematical | Extended classifier system (XCS) | color, size | semi-supervised | real robot | original study | |

An Entropy-Based Approach to the Hierarchical Acquisition of Perception-Action Capabilities | Windridge et al. | 2008 | agent | meso | 1st | stable | no | micro | yes | yes | exploration | Optimization | yes | action selection | online | sorting | mathematical | SGD | image point entropy | unsupervised | simulation | original study | |

A novel formalization for robot cognition based on Affordance model | Yi et al. | 2012 | agent | meso | 1st | stable | no | micro | no | no | exploration | Inference | yes | action selection | online | carryable, stackable, liftable, moveable | mathematical | First-order logic, analysis functions | color, size | unsupervised | simulation | original study | |

Fill and Transfer: A Simple Physics-based Approach for Containability Reasoning | Yu et al. | 2015 | agent | meso | 0th | stable | no | micro | yes | no | ground truth | Optimization | yes | action selection | offline | containability | mathematical | smoothing-based optimization, Gaussian sampling | voxels | supervised | benchmark | original study | |

The learning of adjectives and nouns from affordance and appearance features | Yuruten et al | 2013 | agent | meso | 1st | stable | yes | micro | no | no | exploration | Classification | yes | language | offline | manipulation | mathematical | - | ReliefF, SVM | 3D shape, size | self-supervised | benchmark+real robot | original study |

Learning Adjectives and Nouns from Affordances on the iCub Humanoid Robot | Yuruten et al | 2012 | agent | meso | 1st | stable | yes | micro | no | no | exploration | Classification | no | language | offline | manipulation | mathematical | - | ReliefF, SVM, Growing Neural Gas | 3D shape, size | self-supervised | real robot | original study |

Reasoning about Object Affordances in a Knowledge Base Representation | Zhu et al. | 2014 | agent | meso | 1st | stable | no | micro | no | no | hardcoded | Inference | yes | single-/multi-step prediction | offline | general | mathematical | Markov Logic Network | pose, human-object pose info | supervised | benchmark | original study | |

Understanding tools: Task-oriented object modeling, learning and recognition | Zhu et al. | 2015 | agent | local | 1st | stable | no | micro | yes | no | demonstration | Optimization | yes | action selection | online | tool-use | mathematical | SVM, ranking function | material, volume, mass | supervised | benchmark | original study | |

Learning the semantics of object–action relations by observation | Aksoy et al. | 2011 | agent | meso | 0th | stable | no | micro | no | no | demonstration | Classification | no | not specified | offline | Moving Object, Making Sandwich, Filling Liquid, and Opening Book | mathematical | Semantic Object-Hand and Object-Object relations | Color and Depth | supervised | benchmark | Eren Aksoy (09/2017) | |

Model-free incremental learning of the semantics of manipulation actions | Aksoy et al. | 2015 | agent | meso | 0th | stable | no | micro | no | no | demonstration | Classification | no | not specified | online | Pushing, hiding, cutting, chopping, uncovering, putting | mathematical | Semantic Object-Hand and Object-Object relations | Color and Depth | unsupervised | benchmark | Eren Aksoy (09/2017) | |

Object-Action Complexes: Grounded Abstractions of Sensorimotor Processes | Krüger et al. | 2011 | agent | meso | 1st | stable | no | micro+macro | no | yes | exploration | Regression | yes | planning | online | pushing, grasping | mathematical | NN, KDE, Sampling | co-planar contours, location, ECV | supervised | real robot | Tamim Asfour (09/2017) | |

What can I do with this tool? Self-supervised learning of tool affordances from their 3D geometry | Mar et al. | 2017 | agent | meso | 1st | stable | no | micro | no | no | exploration | Regression | yes | action selection | offline | tool-use | mathematical | SOM, regression | OMS-EGI | self-supervised | simulation+real robot | Tanis Mar (09/2017) | |

Towards a Hierarchy of Loco-Manipulation Affordances | Kaiser et al. | 2016 | agent | global | 1st | stable | no | macro | no | yes | ground truth | Optimization | yes | action selection | not specified | loco-manipulation | mathematical | sampling, decision functions | shape, distance, orientation | not specified | simulation+real robot | Peter Kaiser (09/2017) | |

A modular Dynamic Sensorimotor Model for affordances learning, sequences planning and tool-use | Braud et al. | 2017 | agent | local | 1st | stable | no | micro | no | no | demonstration+exploration | Optimization | yes | single-/multi-step prediction | online | tool-use | mathematical | Sensorimotor Law Encoders/Simualtor, Dynamic Sensorimotor Model | sensor and motor readings | self-supervised | simulation+real robot | Alexandre Pitti (10/2017) | |

Detecting object affordances using Convolutional Neural Networks | Nguyen et al. | 2016 | agent | meso | 0th | stable | no | micro | no | no | ground truth | Classification | yes | action selection | offline | general | mathematical | CNN | RGB, depth | supervised | benchmark+real robot | Philipp Zech (10/2017) | |

Object-Based Affordances Detection with Convolutional Neural Networks and Dense Conditional Random Fields | Nguyen et al. | 2017 | agent | meso | 0th | stable | no | micro | no | no | ground truth | Classification | yes | action selection | offline | general | mathematical | CNN, CRF | RGB , depth | supervised | benchmark+real robot | Philipp Zech (10/2017) | |

Iterative affordance learning with adaptive action generation | Maestre et al. | 2017 | agent | meso | 1st | stable | no | micro | no | no | exploration | Inference | yes | action selection | online | pushing | mathematical | BN | position | self-supervised | simulation+real robot | Philipp Zech (10/2017) | |

Learning to Segment Affordances | Lübbecke and Wörgötter | 2017 | agent | global | 0th | stable | no | micro | no | no | ground truth | Classification | yes | not specified | offline | general | mathematical | CNN | RGB, object segments, object parts | supervised | benchmark | Philipp Zech (10/2017) | |

Discovering and Manipulating Affordances | Chavez-Garcia et al. | 2016 | agent | meso | 1st | stable | no | micro | no | no | exploration | Optimization | yes | not specified | offline | pushing | mathematical | clustering, BN | supervoxels, forces | supervised | real robot | Mihai Andries (10/2017) | |

Title | Authors | Year | Perception Perspective | Level | Order | Temporality | Selective Attention | Abstraction | Competitive | Chaining | Acquisition | Prediction | Generalization | Exploitation | Learning | Kind | Abstraction | Brain Areas | Method | Features | Training | Evaluation | Data added |

@Article{Zech-2017-AB,

title = {{Computational models of affordance in robotics: a taxonomy and systematic classification}},

author = {Zech, Philipp and Haller, Simon and Rezapour Lakani, Safoura and Ridge, Barry and Ugur, Emre and Piater, Justus},

journal = {{Adaptive Behavior}},

year = 2017,

month = 10,

volume = 25,

number = 5,

pages = {235--271},

publisher = {SAGE},

doi = {10.1177/1059712317726357},

url = {https://iis.uibk.ac.at/public/papers/Zech-2017-AB.pdf}

}

- Added papers requested by Mihai Andries (10/2017)
- Added errata for original Adaptive Behavior Publication (10/2017)
- Added papers requested by Philipp Zech (10/2017)
- Added papers requested by Alexandre Pitti (10/2017)
- Added papers requested by Peter Kaiser (09/2017)
- Added papers requested by Tanis Mar (09/2017)
- Added papers requested by Tamim Asfour (09/2017)
- Added papers requested by Eren Aksoy (09/2017)
- Initial release of this page (08/2017)