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

TL;DR

This paper introduces a GPU-accelerated, confidence-aware human motion prediction framework using particles, enabling real-time, long-term, and fine-grained trajectory forecasts for safe robot navigation in human environments.

Contribution

It presents a novel, parallelized particle-based prediction method that significantly improves prediction speed and resolution for multi-human scenarios in robot navigation.

Findings

Achieves 125 Hz prediction frequency with GPU acceleration.

Supports finer prediction time steps for detailed trajectory forecasting.

Demonstrates safe robot navigation among multiple humans in real-world tests.

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.