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Reinforcement Learning for Diffusion Policies in Robotics: A Survey and State-Based Locomotion Reproduction
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DOI：10.3390/robotics15080147.png)
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Abstract
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Diffusion policies model multimodal robot action sequences, but behavioral cloning does not directly optimize task return. We present a structured scoping review of reinforcement learning for generative robot policies and a bounded state-based locomotion reproduction. Four documented routes yielded 178 records, 162 unique candidates, and an 84-study evidence map. Hierarchical rules distinguish 41 direct reward-driven studies from 32 adjacent robotic, eight alternative-generator, and three non-robotic studies; a five-axis taxonomy codes initialization/data, interaction regime, optimized object, credit assignment, and generator. Under a fixed-final evaluation protocol on the Datasets for Deep Data-Driven Reinforcement Learning (D4RL) 1.1 Hopper benchmark, five diffusion policy policy optimization (DPPO) fine-tuning seeds improved over their run-recorded behavior-cloning initializations by a mean of 1261.2 return, with a seed-level standard deviation of 125.5 and a 95% confidence interval of 1105.3–1417.1; the five runs link to two recorded behavior-cloning checkpoints. A Gaussian-policy control also improved after proximal policy optimization, so the gain was not diffusion-specific. A full-chain backpropagation adaptation exhibited clear seed-dependent variation, a matched action-divergence intervention did not establish causal critical timesteps, and reducing denoiser evaluations from 20 to 2 lowered A100 latency from 30.97 to 3.85 ms while substantially reducing normalized score. The experiments are limited to state-based locomotion and do not validate visual manipulation.
Keywords:
diffusion policy
reinforcement learning
policy fine-tuning
state-based locomotion
empirical reproduction
inference acceleration
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