Posterior Optimization with Clipped Objective for Bridging Efficiency and Stability in Generative Policy Learning

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• POCO prevents catastrophic policy collapse during fine-tuning of generative models for robotic manipulation.result 85%
• POCO achieves a 96.7% success rate on real-world contact-rich robotic manipulation tasks.result 90%
• POCO outperforms state-of-the-art baselines across 7 simulation benchmarks and 4 real-world tasks.result 85%
• POCO uses an Expectation-Maximization procedure to distill a reward-weighted implicit posterior into the policy without likelihood estimation.method 80%
• POCO adopts an offline-to-online paradigm that anchors online exploration to pre-trained priors.method 85%
• POCO's model-agnostic design scales to fine-tune large VLA models without architectural modifications.technical 80%
• POCO has potential computational complexity and scaling issues in real-time environments.limitation 75%
• POCO relies on pre-trained data which might not always encompass all edge cases encountered during real-world operations.limitation 70%

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