OGPO: New Algorithm Improves Sample Efficiency in Robot Learning with Generative Control Policies
Researchers have introduced Off-policy Generative Policy Optimization (OGPO), an algorithm designed to efficiently finetune generative control policies (GCPs) used in robot learning. OGPO combines off-policy critic networks with a modified PPO objective to maximize data reuse and propagate gradients through the full generative process. The method claims state-of-the-art performance on robotic manipulation tasks and is reportedly the first capable of recovering poorly-initialized behavior cloning policies to near full task-success without expert data in the replay buffer.
OGPO, presented in a preprint on arXiv, targets a key challenge in robot learning: efficiently finetuning generative control policies such as diffusion- and flow-based models. The algorithm maintains off-policy critic networks to maximize reuse of collected data and propagates policy gradients through the entire generative process using a modified Proximal Policy Optimization (PPO) objective, with critics serving as the terminal reward signal. The authors report state-of-the-art results across multi-task manipulation, high-precision insertion, and dexterous control benchmarks. A notable claim is that OGPO can rescue poorly-initialized behavior cloning policies to near-perfect task success with no expert demonstrations in the online replay buffer, requiring minimal task-specific hyperparameter tuning. To improve stability, the authors introduce several practical techniques: success-buffer regularization, two-sided conservative advantages, and Q-variance reduction, aimed at preventing critic over-exploitation in both state-based and pixel-based settings. Beyond the algorithm itself, the paper presents a systematic empirical study of GCP finetuning, cataloguing stabilizing mechanisms and common failure modes. The work is a preprint and has not yet undergone formal peer review.
What's missing
Real-world robot hardware evaluations are not described; all results appear to be simulation-based. As a preprint, the work has not yet been peer-reviewed.
What different sources said
- arXiv cs.LGCenter
OGPO: Sample Efficient Full-Finetuning of Generative Control Policies
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