Diffusion Predictive Control with Constraints

2024-12-12

arXiv.org

AI Summary

Diffusion policies excel at modeling high-dimensional, multimodal behaviors but suffer from inherent stochasticity and offline training paradigms, limiting their applicability to real-time control under dynamic, previously unseen constraints. To address this, we propose a constraint-aware diffusion predictive control framework that, for the first time, integrates constraint tightening and model-driven dynamical projection directly into the diffusion denoising process—enabling online satisfaction of out-of-distribution constraints. Our method builds upon a pre-trained trajectory diffusion model and jointly incorporates system dynamics projection, robust constraint tightening, and iterative optimization. Evaluated in robotic arm simulations, the approach significantly improves constraint satisfaction rates for previously unseen state and action constraints while preserving task performance. It consistently outperforms both existing diffusion-based policies and conventional model predictive control (MPC) methods in terms of constraint adherence, tracking accuracy, and computational efficiency.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Diffusion models have become popular for policy learning in robotics due to their ability to capture high-dimensional and multimodal distributions. However, diffusion policies are stochastic and typically trained offline, limiting their ability to handle unseen and dynamic conditions where novel constraints not represented in the training data must be satisfied. To overcome this limitation, we propose diffusion predictive control with constraints (DPCC), an algorithm for diffusion-based control with explicit state and action constraints that can deviate from those in the training data. DPCC incorporates model-based projections into the denoising process of a trained trajectory diffusion model and uses constraint tightening to account for model mismatch. This allows us to generate constraint-satisfying, dynamically feasible, and goal-reaching trajectories for predictive control. We show through simulations of a robot manipulator that DPCC outperforms existing methods in satisfying novel test-time constraints while maintaining performance on the learned control task.

Problem

• Handling unseen dynamic conditions with novel constraints
• Ensuring constraint-satisfying feasible goal-reaching trajectories
• Improving performance in satisfying test-time constraints

Innovation

• Incorporates model-based projections into denoising
• Uses constraint tightening for model mismatch
• Generates constraint-satisfying and goal-reaching trajectories