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论文arXiv 2610.1060122 天前

教机器狗学新把戏：通过结合强化学习和模仿学习与对抗式任务选择实现多样化四足技能 ​

面向多任务四足技能学习，提出三阶段多教师蒸馏，结合RL/IL和对抗式任务选择，并在22项任务及Unitree B1上验证。

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原文链接：Teaching a Robot Dog New Tricks: Diverse Quadruped Skills via Combined Reinforcement and Imitation Learning with Adversarial Task Selection（arXiv 2610.10601 · 发布日期：2026-10-06）

Teaching a Robot Dog New Tricks: Diverse Quadruped Skills via Combined Reinforcement and Imitation Learning with Adversarial Task Selection

面向多任务四足技能学习，提出三阶段多教师蒸馏，结合RL/IL和对抗式任务选择，并在22项任务及Unitree B1上验证。

核心要点 ​

  • 单任务RL已能支持腿式机器人技能，但农业机器人、空间探索等需要多样技能组合。
  • 方法先用RL训练多个窄任务教师，再以多教师蒸馏训练单一学生策略。
  • 学生训练结合RL与IL目标，并用对抗式任务选择聚焦表现最差的任务。
  • 学生策略用8个教师完成22项任务，较PPO和先蒸馏后微调基线更好保持运动质量和跟踪指令。

验证范围与局限：验证范围为22项任务评估和一台Unitree B1四足机器人真机部署。

英文摘要 (Abstract) ​

Reinforcement Learning (RL) has enabled legged robots to perform a range of skills in single-task settings. However, applications such as farm robotics or space exploration require diverse skills such as locomotion, digging, or close-range surveying. Training an end-to-end policy to address this problem remains difficult due to challenges such as sample inefficiency and gradient conflict between tasks in multi-task learning. We propose a three-stage method that trains a single policy to perform distinct tasks such as walking, digging, and hopping, and compose them into novel behaviors such as crawling. First, multiple teacher policies are trained using RL on narrowly defined tasks. Then, two additional stages train a student policy with a multi-teacher distillation setup that uses a combined RL and Imitation Learning (IL) objective under an adversarial task selection process that focuses training on the worst-performing task. With this method, we train a student policy that performs 22 tasks using 8 teachers. Evaluations show our method preserves motion quality and tracks commands more accurately than PPO and distill-then-finetune baselines, and in some cases generalizes to new tasks without explicit training. Finally, we demonstrate real-world robustness by deploying the resulting policy on a Unitree B1 quadruped. Video: https://youtu.be/V9yX04EBcFA

作者：Lemon Foxmere, Anthony Furman, Du, Yizheng, Oliver Chang, Leilani H. Gilpin, Steve McGuire

来源

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  1. arXiv 2610.10601：Teaching a Robot Dog New Tricks: Diverse Quadruped Skills via Combined Reinforcement and Imitation Learning with Adversarial Task Selection访问 2026-10-21