Reproduce “Adaptive Diffusion Policy Optimization for Robotic Manipulation” on OpenResearch
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About this paper
Huiyun Jiang, Zhuang Yang
This paper introduces Adam-based Diffusion Policy Optimization (ADPO), an algorithmic framework that integrates an Adaptive Policy Gradient (ADAPG) mechanism to enhance the training speed and stability of diffusion-based policies in robotic control. Experiments demonstrate that ADPO consistently improves performance and accelerates convergence across various locomotion and manipulation tasks when applied to existing diffusion-based RL methods.
Key insights
- Adaptive Policy Gradient (ADAPG) can effectively enhance the optimization of diffusion-based policies in various RL algorithms, leading to faster and more stable convergence.
- The ADAPG mechanism, with its discount factor and Katyusha-inspired momentum scheme, helps to reduce oscillations and avoid local optima during training.
- While the numerical stability constant (ε) is robust across tasks, the momentum parameter (ω) in ADAPG requires task-specific tuning for optimal performance.
Summary by alphaXiv.
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