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

Key points

• However, applications such as farm robotics or space exploration require diverse skills such as locomotion, digging, or close-range surveying.1
• 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.1
• 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.1
• 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.1

Sources (1)

• [1]Teaching a Robot Dog New Tricks: Diverse Quadruped Skills via Combined Reinforcement and Imitation Learning with Adversarial Task Selection
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 11:16 PM

  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.

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