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About this paper
Atsushi Kanehira, Naoki Wake, Kazuhiro Sasabuchi, Jun Takamatsu, Katsushi Ikeuchi
Microsoft researchers develop a real-to-sim-to-real framework that combines reinforcement learning with parameterized grasp skills to generate diverse training data for dexterous manipulation, enabling 100% successful grasps on novel objects when fine-tuning vision-action models with only 40 real demonstrations.
Key insights
- Separating approach and contact phases simplifies the learning problem while maintaining task flexibility
- Residual RL policies can effectively adapt reference trajectories to different object shapes
- Mixing simulated and real data significantly improves generalization compared to either alone
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.