  Many manipulation tasks require robots to interact with unknown environments.
In such applications, the ability to adapt the impedance according to different
task phases and environment constraints is crucial for safety and performance.
Although many approaches based on deep reinforcement learning (RL) and learning
from demonstration (LfD) have been proposed to obtain variable impedance skills
on contact-rich manipulation tasks, these skills are typically task-specific
and could be sensitive to changes in task settings. This paper proposes an
inverse reinforcement learning (IRL) based approach to recover both the
variable impedance policy and reward function from expert demonstrations. We
explore different action space of the reward functions to achieve a more
general representation of expert variable impedance skills. Experiments on two
variable impedance tasks (Peg-in-Hole and Cup-on-Plate) were conducted in both
simulations and on a real FANUC LR Mate 200iD/7L industrial robot. The
comparison results with behavior cloning and force-based IRL proved that the
learned reward function in the gain action space has better transferability
than in the force space. Experiment videos are available at
https://msc.berkeley.edu/research/impedance-irl.html.

Robotics