OGPO: New Algorithm Improves Sample Efficiency in Robot Learning with Generative Control Policies
Researchers introduced Off-policy Generative Policy Optimization (OGPO), an algorithm designed to efficiently finetune generative control policies for robot learning tasks. The method uses off-policy critic networks and modified PPO objectives to maximize data reuse while propagating policy gradients through the full generative process. OGPO achieves state-of-the-art results on manipulation tasks and can improve poorly-initialized policies without expert data, advancing the field of sample-efficient robot learning.
OGPO is a new sample-efficient algorithm for finetuning generative control policies (GCPs)—such as diffusion- and flow-based policies—used in robot learning. The algorithm maintains off-policy critic networks to maximize data reuse and propagates policy gradients through the full generative process using a modified PPO objective with critics as terminal rewards. The researchers demonstrated state-of-the-art performance across multiple manipulation tasks, including multi-task settings, high-precision insertion, and dexterous control. Notably, OGPO is reported to be the only method capable of finetuning poorly-initialized behavior cloning policies to near full task-success without expert data in the online replay buffer, while requiring minimal task-specific hyperparameter tuning. The work includes practical stabilization techniques such as success-buffer regularization, two-sided conservative advantages, and Q-variance reduction to prevent critic over-exploitation in both state- and pixel-based settings.
What's missing
The paper does not discuss computational costs, wall-clock training times, or scalability to real-world robotic systems beyond simulation environments. Additionally, limitations regarding generalization to novel tasks or robot morphologies outside the tested domains are not explicitly addressed in the abstract.
What different sources said
- arXiv cs.LGCenter
OGPO: Sample Efficient Full-Finetuning of Generative Control Policies
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