MODIP: New Framework for Efficient Fine-Tuning of Diffusion Policies in Robot Learning
Researchers have introduced MODIP, a framework designed to improve diffusion policies for robot learning by combining world models and model predictive control for offline-to-online reinforcement learning fine-tuning. Diffusion policies are expressive tools for robot imitation learning but have proven difficult to fine-tune with RL due to their multi-step action generation process. MODIP addresses this bottleneck and demonstrates competitive or superior performance against existing RL fine-tuning methods and strong model-based baselines.
MODIP (Model-Based Optimization for Diffusion Policies) is a newly proposed framework targeting a known limitation in robot learning: while diffusion policies excel at behavioral cloning, applying reinforcement learning fine-tuning to them is computationally and algorithmically challenging because actions are produced through iterative denoising. The framework sidesteps direct RL application by using a world model to guide policy adaptation, preserving the simplicity and stability of behavioral cloning. Model predictive control (MPC) is employed within the world model to generate high-quality trajectories, which then serve as supervised fine-tuning targets for the diffusion policy. To reduce computational overhead, MODIP replaces policy-dependent state-action value estimates with terminal state values during MPC planning, and trains critics using policy-independent temporal difference targets. Experiments conducted on D4RL benchmarks (MuJoCo and Kitchen environments) and RoboMimic tasks show that MODIP consistently improves upon pure behavioral cloning and is competitive with or outperforms dedicated diffusion policy RL fine-tuning methods as well as strong baselines like TD-MPC2.
What's missing
The paper does not report results on real physical robot hardware, leaving open questions about sim-to-real transfer and practical deployment. Scalability to high-dimensional observation spaces (e.g., raw image inputs) and sensitivity to world model inaccuracies are not fully characterized.
What different sources said
- arXiv cs.LGCenter
MODIP: Efficient Model-Based Optimization for Diffusion Policies
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