靈活而安全：學習無碰撞高速腿部運動

摘要

在擁擠環境中航行的四足機器人必須具有靈活性，以有效執行任務並確保安全，避免與障礙物或人類發生碰撞。現有研究要麼開發保守的控制器（<1.0 m/s）以確保安全，要麼專注於靈活性而不考慮潛在致命的碰撞。本文介紹了一種名為靈活但安全（ABS）的基於學習的控制框架，可讓四足機器人實現靈活且無碰撞的運動。ABS包括一個靈活策略，用於在障礙物中執行靈活的運動技能，以及一個恢復策略，用於防止失敗，共同實現高速和無碰撞的導航。ABS中的策略切換由一個學習的控制理論達避值網絡控制，同時將恢復策略作為一個客觀函數引導，從而在閉環中保護機器人。訓練過程包括在模擬環境中學習靈活策略、達避值網絡、恢復策略和外感知表示網絡。這些訓練過的模塊可以直接部署在現實世界中，具有機載感知和計算，實現在受限的室內和室外空間中高速且無碰撞的導航，應對靜態和動態障礙物。

English

Legged robots navigating cluttered environments must be jointly agile for efficient task execution and safe to avoid collisions with obstacles or humans. Existing studies either develop conservative controllers (< 1.0 m/s) to ensure safety, or focus on agility without considering potentially fatal collisions. This paper introduces Agile But Safe (ABS), a learning-based control framework that enables agile and collision-free locomotion for quadrupedal robots. ABS involves an agile policy to execute agile motor skills amidst obstacles and a recovery policy to prevent failures, collaboratively achieving high-speed and collision-free navigation. The policy switch in ABS is governed by a learned control-theoretic reach-avoid value network, which also guides the recovery policy as an objective function, thereby safeguarding the robot in a closed loop. The training process involves the learning of the agile policy, the reach-avoid value network, the recovery policy, and an exteroception representation network, all in simulation. These trained modules can be directly deployed in the real world with onboard sensing and computation, leading to high-speed and collision-free navigation in confined indoor and outdoor spaces with both static and dynamic obstacles.

靈活而安全：學習無碰撞高速腿部運動

Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion

摘要

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