让机器人自适应调整控制频率，提升效率与鲁棒性。
TARC: Time-Adaptive Robotic Control
- 策略联合决定动作和持续时间，动态调节控制频率。
- 实测显示控制频率显著降低，性能媲美或超过固定频率方法。
- 无需额外训练即可跨平台迁移，适合真实场景应用。
机器人传统固定频率控制在效率与鲁棒性间存在权衡，而生物系统无此限制。本文提出一种强化学习方法，使策略同时选择控制动作及其持续时间，实现机器人根据任务需求自主调节控制频率。我们在两种不同硬件平台上进行了零样本的仿真到现实迁移实验：高速遥控车与四足机器人。结果表明，该方法在奖励指标上达到或超越固定频率基线，同时显著降低控制频率，并在真实环境中展现出自适应频率控制能力。
原文摘要 · Abstract (English)
Fixed-frequency control in robotics imposes a trade-off between the efficiency of low-frequency control and the robustness of high-frequency control, a limitation not seen in adaptable biological systems. We address this with a reinforcement learning approach in which policies jointly select control actions and their application durations, enabling robots to autonomously modulate their control frequency in response to situational demands. We validate our method with zero-shot sim-to-real experiments on two distinct hardware platforms: a high-speed RC car and a quadrupedal robot. Our method matches or outperforms fixed-frequency baselines in terms of rewards while significantly reducing the control frequency and exhibiting adaptive frequency control under real-world conditions.
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