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EARL @ RSS 2024
Monday, July 15, 2024 (Auditorium Aula-20, Aula Conference Center, 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
Stanford & Columbia University
Vrije Universiteit Amsterdam
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.
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.
Submission Deadline (Extended): June 14
Acceptance Notification: June 24
Camera-ready Paper Submission Deadline: July 7
Environment and Embodiment-Aware Design Optimisation David Howard, Commonwealth Scientific and Industrial Research Organisation
A Full Stack Approach to Dexterous Manipulation Pulkit Agrawal, Massachusetts Institute of Technology
Morning Coffee Break 10: 0 0 - 1 0 : 3 0
Co-Adaptation of Robot Design & Behavior: A Reinforcement Learning Perspective Kevin Sebastian Luck, Vrije Universiteit Amsterdam
Enabling Cross-Embodiment Policy Learning Jeannette Bohg, Stanford University
Hardware and Policy Co-design with Deep Reinforcement Learning for Robotic Manipulators Matei Ciocarlie, Columbia University
Lightning Talks by Workshop Participants
Lunch Break 1 2 : 3 0 - 14:00
Shuran Song, Stanford University
Structural Priors for Robot Learning Michael Lutter, Boston Dynamics
On the Fragile Co-Adaptation of Brain and Body in Evolved Soft Robots Nick Cheney, University of Vermont
Afternoon Coffee Break 15:30 - 16:00
Closing Remarks & Award Ceremony
TU Darmstadt
alap.kshirsagar@tu-darmstadt.de
German Research Center for AI (DFKI)
boris.belousov@dfki.de
TU Darmstadt & DFKI
mail@jan-peters.net
