Fast Confidence-Aware Human Prediction via Hardware-accelerated Bayesian Inference for Safe Robot Navigation

2026-03-01

AI Summary

This work addresses the challenge of enabling robots to safely navigate and coexist in spatially constrained, interaction-rich dynamic environments by accurately predicting the trajectories of multiple humans. The authors propose a confidence-aware trajectory prediction method based on particle representations, modeling future states as ensembles of particles and leveraging highly parallelized GPU-accelerated Bayesian inference to achieve high-frequency (125 Hz), fine-grained, long-term multi-target predictions. By explicitly accounting for uncertainty and rapidly updating beliefs, the approach significantly enhances responsiveness to subtle variations in human behavior. Real-world experiments demonstrate that the robot can robustly, safely, and efficiently maneuver through complex crowds characterized by diverse navigation intents.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: State Estimation
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

As robots increasingly integrate into everyday environments, ensuring their safe navigation around humans becomes imperative. Efficient and safe motion planning requires robots to account for human behavior, particularly in constrained spaces such as grocery stores or care homes, where interactions with multiple individuals are common. Prior research has employed Bayesian frameworks to model human rationality based on navigational intent, enabling the prediction of probabilistic trajectories for planning purposes. In this work, we present a simple yet novel approach for confidence-aware prediction that treats future predictions as particles. This framework is highly parallelized and accelerated on an graphics processing unit (GPU). As a result, this enables longer-term predictions at a frequency of 125 Hz and can be easily extended for multi-human predictions. Compared to existing methods, our implementation supports finer prediction time steps, yielding more granular trajectory forecasts. This enhanced resolution allows motion planners to respond effectively to subtle changes in human behavior. We validate our approach through real-world experiments, demonstrating a robot safely navigating among multiple humans with diverse navigational goals. Our results highlight the methods potential for robust and efficient human-robot coexistence in dynamic environments.

Problem

• safe robot navigation
• human behavior prediction
• confidence-aware prediction
• multi-human interaction
• dynamic environments

Innovation

• Bayesian inference
• GPU acceleration
• confidence-aware prediction
• particle-based trajectory forecasting
• human-robot navigation