research:projects
Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
|
research:projects [2021/11/22 10:39] Alejandro Agostini |
research:projects [2024/01/25 11:51] Justus Piater [Current Projects] |
||
|---|---|---|---|
| Line 3: | Line 3: | ||
| ===== Current Projects ===== | ===== Current Projects ===== | ||
| - | **ELSA** - Effective Learning of Social Affordances for Human-Robot Interaction (ANR/FWF AAPG, 2022-2026): Affordances are action opportunities directly perceived by an agent to interact with its environment. The concept is gaining interest in robotics, where it offers a rich description of the objects and the environment, focusing on the potential interactions rather than the sole physical properties. In this project, we extend this notion to social affordances. The goal is for robots to autonomously learn not only the physical effects of interactive actions with humans, but also the humansʼ reactions they produce (emotion, speech, movement). For instance, pointing and gazing in the same direction make humans orient towards the pointed direction, while pointing and looking at the finger make humans look at the finger. Besides, scratching the robotʼs chin makes some but not all humans smile. The project will investigate how learning human- general and human-specific social affordances can enrich a robotʼs action repertoire for human-aware task planning and efficient human-robot interaction. | + | [[https://doi.org/10.55776/P36965|{{:research:doi.svg?13|}}]] ** ** - Purposeful Signal-symbol Relations for Manipulation Planning (Austrian Science Fund (FWF), 2023-2026) |
| <html> | <html> | ||
| Line 9: | Line 9: | ||
| </html> | </html> | ||
| - | **SEAROCCO** - Seamless Levels of Abstraction for Robot Cognition (Austrian Science Fund (FWF) - Lise Meitner Project, 2019-2023): The project seeks to develop a robotic cognitive architecture that overcomes the difficulties found when integrating different levels of abstractions (e.g. AI and robotic techniques) for task plan and execution in unstructured scenarios. The backbone of the project is a unified approach that permits searching for feasible solutions for new tasks execution at all the levels of abstractions simultaneously, where symbolic descriptions are no longer disentangled from the physical aspects they represent. | + | [[https://doi.org/10.55776/I5755|{{:research:doi.svg?13|}}]] **ELSA** - Effective Learning of Social Affordances for Human-Robot Interaction (ANR/FWF AAPG, 2022-2026): Affordances are action opportunities directly perceived by an agent to interact with its environment. The concept is gaining interest in robotics, where it offers a rich description of the objects and the environment, focusing on the potential interactions rather than the sole physical properties. In this project, we extend this notion to social affordances. The goal is for robots to autonomously learn not only the physical effects of interactive actions with humans, but also the humansʼ reactions they produce (emotion, speech, movement). For instance, pointing and gazing in the same direction make humans orient towards the pointed direction, while pointing and looking at the finger make humans look at the finger. Besides, scratching the robotʼs chin makes some but not all humans smile. The project will investigate how learning human- general and human-specific social affordances can enrich a robotʼs action repertoire for human-aware task planning and efficient human-robot interaction. |
| <html> | <html> | ||
| Line 15: | Line 15: | ||
| </html> | </html> | ||
| - | **OLIVER** - Open-Ended Learning for Interactive Robots (EUREGIO IPN, 2019-2022): We would like to be able to teach robots to perform a great variety of tasks, including collaborative tasks, and tasks not specifically foreseen by its designers. Thus, the space of potentially-important aspects of perception and action is by necessity extremely large, since every aspect may become important at some point in time. Conventional machine learning methods cannot be directly applied in such unconstrained circumstances, as the training demands increase with the sizes of the input and output spaces. | + | [[https://doi.org/10.55776/M2659|{{:research:doi.svg?13|}}]] **SEAROCO** - Seamless Levels of Abstraction for Robot Cognition (Austrian Science Fund (FWF) - Lise Meitner Project, 2019-2023): The project seeks to develop a robotic cognitive architecture that overcomes the difficulties found when integrating different levels of abstractions (e.g. AI and robotic techniques) for task plan and execution in unstructured scenarios. The backbone of the project is a unified approach that permits searching for feasible solutions for new tasks execution at all the levels of abstractions simultaneously, where symbolic descriptions are no longer disentangled from the physical aspects they represent. |
| - | Thus, a central problem for the robot is to understand which aspects of a demonstrated action are crucial. Such understanding allows a robot to perform robustly even if the scenario and context change, to adapt its strategy, and to judge its success. Moreover, it allows the robot to infer the human intent and task progress with respect to the goal, enabling it to share the task with humans, offer help or ask for help, resulting in natural human-robot cooperative behavior. | + | |
| <html> | <html> | ||
| Line 44: | Line 43: | ||
| {{:research:3rdhand.png?nolink&110 |3rdHand}} | {{:research:3rdhand.png?nolink&110 |3rdHand}} | ||
| - | [[https://cordis.europa.eu/project/rcn/110160/factsheet/en|3rdHand]] (EU FP7-ICT-STREP, 2013-2017) develops a semi-autonomous robot assistant that acts as a third hand of a human worker. It will be straightforward to instruct even by an untrained layman worker, allow for efficient knowledge transfer between tasks, and enable effective collaboration between a human worker with a robot third hand. The main contributions of this project will be the scientific principles of semi-autonomous human-robot collaboration, a new semi-autonomous robotic system that is able to (i) learn cooperative tasks from demonstration, (ii) learn from instruction, and (iii) transfer knowledge between tasks and environments. | + | [[https://cordis.europa.eu/project/id/610878|3rdHand]] (EU FP7-ICT-STREP, 2013-2017) develops a semi-autonomous robot assistant that acts as a third hand of a human worker. It will be straightforward to instruct even by an untrained layman worker, allow for efficient knowledge transfer between tasks, and enable effective collaboration between a human worker with a robot third hand. The main contributions of this project will be the scientific principles of semi-autonomous human-robot collaboration, a new semi-autonomous robotic system that is able to (i) learn cooperative tasks from demonstration, (ii) learn from instruction, and (iii) transfer knowledge between tasks and environments. |
| <html> | <html> | ||
| <div style="clear:both"><br></div> | <div style="clear:both"><br></div> | ||
research/projects.txt · Last modified: 2024/02/19 12:24 by Antonio Rodriguez-Sanchez