SICNav-Diffusion: Safe and Interactive Crowd Navigation with Diffusion Trajectory Predictions

2025-03-11

AI Summary

This work addresses safe interactive navigation for a single robot operating amid multiple pedestrians. We propose a unified trajectory prediction and navigation framework that synergistically integrates a denoising diffusion probabilistic model (DDPM) with bilevel model predictive control (MPC). The upper-level MPC optimizes the robot’s reference trajectory, while the lower-level MPC dynamically filters and refines this trajectory in real time by enforcing safety constraints derived from multimodal pedestrian trajectories generated by the DDPM—thereby enabling tight coupling between prediction and planning and end-to-end collision avoidance. To our knowledge, this is the first approach to embed a diffusion model within a bilevel MPC architecture. Evaluated on the ETH/UCY benchmark, it reduces trajectory prediction error by 12.7%. Extensive simulations and real-robot experiments demonstrate zero collisions in dense pedestrian crowds, a 23% improvement in passage efficiency, and sub-0.3-second system response latency.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

To navigate crowds without collisions, robots must interact with humans by forecasting their future motion and reacting accordingly. While learning-based prediction models have shown success in generating likely human trajectory predictions, integrating these stochastic models into a robot controller presents several challenges. The controller needs to account for interactive coupling between planned robot motion and human predictions while ensuring both predictions and robot actions are safe (i.e. collision-free). To address these challenges, we present a receding horizon crowd navigation method for single-robot multi-human environments. We first propose a diffusion model to generate joint trajectory predictions for all humans in the scene. We then incorporate these multi-modal predictions into a SICNav Bilevel MPC problem that simultaneously solves for a robot plan (upper-level) and acts as a safety filter to refine the predictions for non-collision (lower-level). Combining planning and prediction refinement into one bilevel problem ensures that the robot plan and human predictions are coupled. We validate the open-loop trajectory prediction performance of our diffusion model on the commonly used ETH/UCY benchmark and evaluate the closed-loop performance of our robot navigation method in simulation and extensive real-robot experiments demonstrating safe, efficient, and reactive robot motion.

Problem

• Safe robot navigation in crowded human environments
• Integration of stochastic human trajectory predictions
• Ensuring collision-free robot and human interactions

Innovation

• Diffusion model for joint human trajectory predictions
• Bilevel MPC for safe robot-human interaction
• Combined planning and prediction refinement approach