Posterior Optimization with Clipped Objective for Bridging Efficiency and Stability in Generative Policy Learning
This addresses the problem of efficient and stable policy learning for robotics, particularly in contact-rich environments, with incremental improvements over existing methods.
The paper tackles the challenge of fine-tuning generative policies for robotic manipulation, which often suffer from instability and sample inefficiency, and introduces POCO, a framework that prevents catastrophic collapse and achieves a 96.7% success rate on real-world tasks.
Expressive generative models have advanced robotic manipulation by capturing complex, multi-modal action distributions over temporally extended trajectories. However, fine-tuning these policies via RL remains challenging due to instability and sample inefficiency. We introduce Posterior Optimization with Clipped Objective (POCO), a principled RL framework that formulates policy improvement as a posterior inference problem tailored for temporal action chunks. Through an Expectation-Maximization procedure, POCO distills a reward-weighted implicit posterior into the policy without likelihood estimation. Furthermore, POCO adopts an offline-to-online paradigm that anchors online exploration to pre-trained priors, and its model-agnostic design scales to fine-tune large VLA models without architectural modifications. Evaluations across 7 simulation benchmarks and 4 contact-rich real-world tasks demonstrate that POCO prevents catastrophic policy collapse, outperforms SOTA baselines, and achieves a 96.7% success rate on real-world tasks. Videos are available at our project website https://cccedric.github.io/poco/.