A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
Yunhai Feng, Natalie Leung, Jiaxuan Wang, Lujie Yang, Haozhi Qi, Preston Culbertson
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Key claim
REGRIND enables effective sim-to-real transfer for dexterous tasks.
In plain English
Humanoid robots struggle with dexterous manipulation due to the complexity of contact-rich tasks. Current methods often fail to effectively transfer learned behaviors from simulations to real-world applications. REGRIND addresses this by using a minimalist retargeting-guided reinforcement learning pipeline that learns from a single human demonstration. Builders might find this approach valuable as it simplifies the training process and enhances the performance of robots in practical tool-use scenarios.
Introduces a novel retargeting-guided RL approach for dexterous manipulation.
Demonstrates solid results in hardware experiments with clear analysis.
Deep reliability assessment
The methodology supports the claim that interaction-preserving retargeting can improve sim-to-real transfer for dexterous manipulation, but the generalizability across different tasks and environments is not fully explored.
Reproducibility
Yes, the paper mentions that code and data are available at https://yunhaifeng.com/REGRIND.
Key figure
Figure 1 illustrates the dexterous tool use tasks evaluated in the study, showing different task-hand settings across two robot hands.