Kavli Affiliate: Xiang Zhang
| First 5 Authors: Joohwan Seo, Nikhil Potu Surya Prakash, Xiang Zhang, Changhao Wang, Jongeun Choi
| Summary:
This paper presents a differential geometric control approach that leverages
SE(3) group invariance and equivariance to increase transferability in learning
robot manipulation tasks that involve interaction with the environment.
Specifically, we employ a control law and a learning representation framework
that remain invariant under arbitrary SE(3) transformations of the manipulation
task definition. Furthermore, the control law and learning representation
framework are shown to be SE(3) equivariant when represented relative to the
spatial frame. The proposed approach is based on utilizing a recently presented
geometric impedance control (GIC) combined with a learning variable impedance
control framework, where the gain scheduling policy is trained in a supervised
learning fashion from expert demonstrations. A geometrically consistent error
vector (GCEV) is fed to a neural network to achieve a gain scheduling policy
that remains invariant to arbitrary translation and rotations. A comparison of
our proposed control and learning framework with a well-known Cartesian space
learning impedance control, equipped with a Cartesian error vector-based gain
scheduling policy, confirms the significantly superior learning transferability
of our proposed approach. A hardware implementation on a peg-in-hole task is
conducted to validate the learning transferability and feasibility of the
proposed approach.
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