Workshop on Embodiment-Aware Robot Learning (EARL)

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.

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 10:00 - 10:30

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

Lunch Break 12:30 - 14:00

Self-Adaptive Robots

Shuran Song, Stanford University

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