让轨迹预测符合物理规律，提升行人行为预判准确性
Physical Plausibility-aware Trajectory Prediction via Locomotion Embodiment
- 用可微分物理仿真评估动作的合理性，显式约束轨迹生成
- 在多个数据集上显著提升主流方法性能，尤其在复杂场景下
- 适合需要高可信度轨迹预测的自动驾驶与机器人应用
人类能仅凭短暂观察就预测他人未来轨迹，依赖于姿态等线索。但以往的人类轨迹预测（HTP）方法隐式利用姿态信息，导致预测结果常不切实际。为此，我们提出运动具身（Locomotion Embodiment）框架，通过物理法则驱动的运动生成显式评估预测轨迹的物理合理性。该框架使用不可微物理模拟器学习运动合理性，再以可微的运动价值函数替代，实现数据驱动训练。特别地，提出的具身运动损失有助于高效训练带多头的随机性HTP网络；同时引入运动价值过滤器，在推理阶段剔除不合理轨迹。实验表明，该方法在多种数据集和任务设置下均显著提升现有先进方法的表现。代码已开源：https://github.com/ImIntheMiddle/EmLoco。
原文摘要 · Abstract (English)
Humans can predict future human trajectories even from momentary observations by using human pose-related cues. However, previous Human Trajectory Prediction (HTP) methods leverage the pose cues implicitly, resulting in implausible predictions. To address this, we propose Locomotion Embodiment, a framework that explicitly evaluates the physical plausibility of the predicted trajectory by locomotion generation under the laws of physics. While the plausibility of locomotion is learned with an indifferentiable physics simulator, it is replaced by our differentiable Locomotion Value function to train an HTP network in a data-driven manner. In particular, our proposed Embodied Locomotion loss is beneficial for efficiently training a stochastic HTP network using multiple heads. Furthermore, the Locomotion Value filter is proposed to filter out implausible trajectories at inference. Experiments demonstrate that our method enhances even the state-of-the-art HTP methods across diverse datasets and problem settings. Our code is available at: https://github.com/ImIntheMiddle/EmLoco.
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