Importance Sampling Model-Based Diffusion for Trajectory Optimization
Seth Golembeski, Anirban Mazumdar
AI summary
Problem
Model-based diffusion offers a data-free approach for optimizing trajectories in high-dimensional, nonlinear robotic systems, but its reliance on standard Gaussian sampling makes it computationally prohibitive for long-horizon or large-input tasks.
Approach
The method integrates a cross-entropy adaptive importance sampler into the diffusion process, iteratively learning and refining the sampling distribution to focus computational effort on high-reward trajectory regions.
Key results
- Up to 13x improvement in sample efficiency across long-horizon tasks
- Consistently outperforms standard MBD and MPPI/MPOPI-CE baselines in car racing and MuJoCo environments
- Demonstrates robust convergence without requiring prior training datasets
- Validates adaptive importance sampling effectively shapes diffusion distributions for faster optimization
Why it matters
Provides a computationally efficient, data-free planning framework that enables real-time trajectory optimization for complex, high-dimensional robotic systems.
Abstract
Trajectory optimization for robotic systems remains a challenging problem. This is especially true for robotic systems featuring nonlinear dynamics and many degrees of freedom. Data-based or model-free diffusion has recently been popularized in the fields of artificial intelligence and trajectory optimization. Model-Based Diffusion provides a data-free method of trajectory optimization, trained at runtime on a system dynamics model, suitable for high-dimensional models. This paper examines how importance sampling can enhance the performance of Model- Based Diffusion for trajectory optimization. We quantify the benefits of importance sampling across three long horizon plan- ning tasks. These results show as much as a 13x improvement in sample efficiency depending on environment and optimization parameters.