Kavli Affiliate: Xiang Zhang
| First 5 Authors: Shuqi Zhao, Ke Yang, Yuxin Chen, Chenran Li, Yichen Xie
| Summary:
Dexterous manipulation has seen remarkable progress in recent years, with
policies capable of executing many complex and contact-rich tasks in
simulation. However, transferring these policies from simulation to real world
remains a significant challenge. One important issue is the mismatch in
low-level controller dynamics, where identical trajectories can lead to vastly
different contact forces and behaviors when control parameters vary. Existing
approaches often rely on manual tuning or controller randomization, which can
be labor-intensive, task-specific, and introduce significant training
difficulty. In this work, we propose a framework that jointly learns actions
and controller parameters based on the historical information of both
trajectory and controller. This adaptive controller adjustment mechanism allows
the policy to automatically tune control parameters during execution, thereby
mitigating the sim-to-real gap without extensive manual tuning or excessive
randomization. Moreover, by explicitly providing controller parameters as part
of the observation, our approach facilitates better reasoning over force
interactions and improves robustness in real-world scenarios. Experimental
results demonstrate that our method achieves improved transfer performance
across a variety of dexterous tasks involving variable force conditions.
| Search Query: ArXiv Query: search_query=au:”Xiang Zhang”&id_list=&start=0&max_results=3