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We aim to control a robot to physically behave in the real world followingany high-level language command like "cartwheel" or "kick. " Although humanmotion datasets exist, this task remains particularly challenging sincegenerative models can produce physically unrealistic motions, which will bemore severe for robots due to different body structures and physicalproperties. In addition, to control a physical robot to perform a desiredmotion, a control policy must be learned. We develop LAnguage-Guided mOtioncONtrol (LAGOON), a multi-phase method to generate physically realistic robotmotions under language commands. LAGOON first leverages a pre-trained model togenerate human motion from a language command. Then an RL phase is adopted totrain a control policy in simulation to mimic the generated human motion.Finally, with domain randomization, we show that our learned policy can besuccessfully deployed to a quadrupedal robot, leading to a robot dog that canstand up and wave its front legs in the real world to mimic the behavior of ahand-waving human.

Language-Guided Generation of Physically Realistic Robot Motion and Control