DexForce: Extracting Force-informed Actions from Kinesthetic Demonstrations for Dexterous Manipulation
Journal:
arXiv
Published Date:
Jan 17, 2025
Abstract
Imitation learning requires high-quality demonstrations consisting of
sequences of state-action pairs. For contact-rich dexterous manipulation tasks
that require dexterity, the actions in these state-action pairs must produce
the right forces. Current widely-used methods for collecting dexterous
manipulation demonstrations are difficult to use for demonstrating contact-rich
tasks due to unintuitive human-to-robot motion retargeting and the lack of
direct haptic feedback. Motivated by these concerns, we propose DexForce.
DexForce leverages contact forces, measured during kinesthetic demonstrations,
to compute force-informed actions for policy learning. We collect
demonstrations for six tasks and show that policies trained on our
force-informed actions achieve an average success rate of 76% across all tasks.
In contrast, policies trained directly on actions that do not account for
contact forces have near-zero success rates. We also conduct a study ablating
the inclusion of force data in policy observations. We find that while using
force data never hurts policy performance, it helps most for tasks that require
advanced levels of precision and coordination, like opening an AirPods case and
unscrewing a nut.
