通过硬件加速的贝叶斯推理实现快速置信度感知的人类预测以实现安全的机器人导航
Fast Confidence-Aware Human Prediction via Hardware-accelerated Bayesian Inference for Safe Robot Navigation
AI总结：
本文提出一种基于GPU加速的贝叶斯推理方法，实现高效安全的人类行为预测，支持更精细的轨迹预测，通过实验证明机器人在动态环境中与人类共存的潜力。
摘要：
随着机器人越来越多地融入日常环境，确保其在人类周围安全导航变得至关重要。高效的和安全的运动规划需要机器人考虑人类行为，特别是在如超市或养老院等受限空间中，与多个个体互动的情况很常见。先前的研究采用了贝叶斯框架来建模人类的理性行为，基于导航意图，从而能够预测概率轨迹用于规划。在本工作中，我们提出了一种简单但新颖的置信度感知预测方法，将未来预测视为粒子。该框架高度并行化，并在图形处理单元（GPU）上加速。结果，这使得能够以125 Hz的频率进行长时间预测，并可以轻松扩展用于多人类预测。与现有方法相比，我们的实现支持更细的预测时间步长，产生更细致的轨迹预测。这种增强的分辨率使运动规划者能够有效应对人类行为的细微变化。我们通过现实世界实验验证了我们的方法，展示了机器人在具有不同导航目标的多个人类之间安全导航的能力。我们的结果突显了该方法在动态环境中实现稳健和高效的人机共存的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
