Monday, July 15, 2024 (TU Delft, Netherlands)
Workshop on Embodiment-Aware Robot Learning (EARL)
at Robotics: Science and Systems (RSS)
This workshop brings together researchers working on co-design of robot embodiment and control algorithms
Speakers
Stanford & Columbia University
Vrije Universiteit Amsterdam
Stanford University
Venue
EARL Workshop at RSS'24
TU Delft, Netherlands
The workshop on Embodiment-Aware Robot Learning (EARL) brings the optimization of robot embodiment into the foreground of robot learning. Artificial Intelligence (AI) in robotics has come to primarily mean intelligent decision making within a fixed embodiment. However, natural intelligence is broader, and it includes optimization of the organism’s body for its ecological niche. We, therefore, seek to emphasize the importance of co-design of the agent’s body and behavior within the field of robot learning and AI, both from a conceptual point of view and in practical terms.
We welcome participants from various sub-communities, working on both hardware and software aspects of robotics, such as bioinspired robotics, field robotics, grasping and manipulation, robot learning, control & dynamics, mechanisms & design, and robot modeling & simulation, among others.
Call for Papers
We invite extended abstracts (2-4 pages excluding references, in RSS paper format) to be presented as lightning talks and posters during the workshop. We welcome submissions that report new results, ongoing research, and broad vision papers. All submissions are double-blind and peer-reviewed. We invite participants from a variety of backgrounds: robotics, machine learning, evolutionary optimization, mechanical design, electrical engineering.
Schedule
08:45-09:00
Opening Remarks
Jan Peters, TU Darmstadt & German Research Center for AI (DFKI)
09:00 - 09:30
Invited Talk : Robotic Design and Interaction
David Howard, Commonwealth Scientific and Industrial Research Organisation
09:30-10:00
Differentiable Frameworks for Contact-aware Robot Design
Pulkit Agrawal, Massachusetts Institute of Technology
Morning Coffee Break 10:00 - 10:30
10:30-11:00
Co-Adaptation of Robot Design & Behavior: A Reinforcement Learning Perspective
Kevin Sebastian Luck, Vrije Universiteit Amsterdam
11:00 - 11:30
Cross-embodiment Inverse Reinforcement Learning
Jeannette Bohg, Stanford University
11:30 - 12:00
Lightning Talks by Workshop Participants
12:00 - 12:30
Poster Session
Lunch Break 12:30 - 14:00
14:00-14:30
Generative Models for Robot Gripper Form Design
Shuran Song, Stanford University
14:30 - 15:00
Invited Talk : Hardware and Policy Co-design with Deep Reinforcement Learning for Robotic Manipulators
Matei Ciocarlie, Columbia University
15:00 - 15:30
Invited Talk : Scalable Co-optimization of Morphology and Control in Embodied Machines
Nick Cheney, University of Vermont
Afternoon Coffee Break 15:30 - 16:00
16:00 - 17:00
Panel Discussion
17:00 - 17:15
