MODIP: New Framework for Efficient Fine-Tuning of Diffusion Policies in Robot Learning
Researchers propose MODIP, a framework that improves diffusion policies—a type of neural network used in robot learning—by combining world models and model predictive control to guide policy adaptation. Diffusion policies have been successful with imitation learning but have struggled with direct reinforcement learning fine-tuning due to their multi-step denoising process. The method shows competitive or superior performance compared to existing approaches on standard benchmarks, potentially advancing robot learning capabilities.
MODIP addresses a key challenge in robot learning by proposing an efficient approach to fine-tune diffusion policies beyond simple behavioral cloning. Rather than directly applying reinforcement learning to diffusion policies—which is computationally difficult because actions are generated through iterative denoising—MODIP uses a learned world model to guide adaptation while maintaining the stability of supervised learning. The framework employs model predictive control (MPC) to generate high-quality trajectories within the world model, which then serve as training targets for the diffusion policy. To improve efficiency, MODIP uses a terminal state value function instead of policy-dependent value estimates for MPC planning, and trains critics with policy-independent temporal difference targets. Experiments on standard benchmarks (D4RL with MuJoCo and Kitchen environments, plus RoboMimic tasks) demonstrate that MODIP improves performance beyond behavioral cloning and is competitive with or outperforms existing diffusion policy RL methods and strong model-based baselines like TD-MPC2.
What's missing
The paper does not discuss computational requirements or wall-clock time comparisons with baseline methods, limiting practical assessment of efficiency gains. Additionally, generalization to real-world robotic systems beyond simulation is not addressed, and the approach's sensitivity to world model quality is not thoroughly analyzed.
What different sources said
- arXiv cs.LGCenter
MODIP: Efficient Model-Based Optimization for Diffusion Policies
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