让双足机器人实时预测跌倒，提前1.1秒预警且零误报。
Standing Tall: Sim to Real Fall Classification and Lead Time Prediction for Bipedal Robots
- 将离线跌倒预测算法升级为实时系统，支持软硬件同步运行。
- 在真实机器人Digit上实现1.1秒平均预警时间，最大误差仅0.03秒。
- 显著提升抗多方向故障能力，恢复率高达97%，适合实际部署。
本文将先前提出的跌倒预测算法扩展至实时（在线）场景，并在硬件与仿真中实现。系统在全尺寸双足机器人Digit上验证，实时版本性能接近离线实现，保持零误报率，平均预警时间（真跌倒时间与预测时间之差）达1.1秒（远超0.2秒最低要求），最大预警时间误差仅为0.03秒。同时，该系统恢复率达0.97，证明其在真实环境中的有效性。此外，本工作识别出原算法在全向故障下的局限性，提出优化策略以增强鲁棒性。改进后算法在各项评估指标上均有提升，平均误报率降低0.05，平均预测预警时间最大误差减少1.19秒。
原文摘要 · Abstract (English)
This paper extends a previously proposed fall prediction algorithm to a real-time (online) setting, with implementations in both hardware and simulation. The system is validated on the full-sized bipedal robot Digit, where the real-time version achieves performance comparable to the offline implementation while maintaining a zero false positive rate, an average lead time (defined as the difference between the true and predicted fall time) of 1.1s (well above the required minimum of 0.2s), and a maximum lead time error of just 0.03s. It also achieves a high recovery rate of 0.97, demonstrating its effectiveness in real-world deployment. In addition to the real-time implementation, this work identifies key limitations of the original algorithm, particularly under omnidirectional faults, and introduces a fine-tuned strategy to improve robustness. The enhanced algorithm shows measurable improvements across all evaluated metrics, including a 0.05 reduction in average false positive rate and a 1.19s decrease in the maximum error of the average predicted lead time.
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