Combined Constrained Sampling and Reinforcement Learning for Robotic Manipulation
For roboticists tackling contact-rich manipulation, this work offers a practical method to improve RL exploration and policy learning, though it is an incremental combination of existing ideas.
The paper proposes a constrained state sampler that boosts reinforcement learning for contact-rich robotic manipulation, outperforming baselines in training universal, non-prehensile, and dynamic policies.
Training non-prehensile manipulation policies in contact-rich settings is a core challenge in robotics. While Reinforcement Learning (RL) has demonstrated its strength in such settings, it may struggle to sufficiently explore and discover complex manipulation strategies. To address this, we combine two basic ideas: First, designing appropriate reset strategies (the start state distribution of episodes) has shown promise in improving RL exploration and effectiveness. Second, while model-based approaches to finding trajectories through manipulation are hard, recent work showed that model-based approaches to sampling states on constrained manifolds can be highly efficient. Based on these observations, we propose a novel state sampler that boosts the performance of goal-conditioned RL in complex contact-rich manipulation tasks. Our sampler explicitly takes into account the structure of contact in order to provide a rich covering of diverse contact modes. By combining constrained sampling resets with projected interpolation and curriculum learning, our novel approach outperforms RL without constrained sampling and alternative reset methods, and effectively trains universal, non-prehensile, and dynamic manipulation policies in contact-rich settings. See https://www.user.tu-berlin.de/mtoussai/26-CSRL/ for supplementary material.