Authors: Toru Lin, Kartik Sachdev, Linxi Fan, Jitendra Malik, Yuke Zhu
Abstract: Reinforcement learning has delivered promising results in achieving human- or
even superhuman-level capabilities across diverse problem domains, but success
in dexterous robot manipulation remains limited. This work investigates the key
challenges in applying reinforcement learning to solve a collection of
contact-rich manipulation tasks on a humanoid embodiment. We introduce novel
techniques to overcome the identified challenges with empirical validation. Our
main contributions include an automated real-to-sim tuning module that brings
the simulated environment closer to the real world, a generalized reward design
scheme that simplifies reward engineering for long-horizon contact-rich
manipulation tasks, a divide-and-conquer distillation process that improves the
sample efficiency of hard-exploration problems while maintaining sim-to-real
performance, and a mixture of sparse and dense object representations to bridge
the sim-to-real perception gap. We show promising results on three humanoid
dexterous manipulation tasks, with ablation studies on each technique. Our work
presents a successful approach to learning humanoid dexterous manipulation
using sim-to-real reinforcement learning, achieving robust generalization and
high performance without the need for human demonstration.
Source: http://arxiv.org/abs/2502.20396v1
