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Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning
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Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning
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Learning-based methods have proven useful at generating complex motions for robots, including humanoids. Reinforcement learning (RL) has been used to learn locomotion policies, some of which leverage a periodic reward formulation. This work extends the periodic reward formulation of locomotion to skateboarding for the REEM-C robot. Brax/MJX is used to implement the RL problem to achieve fast training. Initial results in simulation are presented with hardware experiments in progress.
Forward citations
Cited by 3 Pith papers
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Phase-Aware Policy Learning for Skateboard Riding of Quadruped Robots via Feature-wise Linear Modulation
PAPL uses phase-conditioned FiLM layers in RL networks to create a unified policy for quadruped robots to ride skateboards by capturing phase-dependent behaviors while sharing knowledge across phases.
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Learning Roller-Skating Motions of Humanoid Robots Based on Adversarial Motion Priors
Independent AMP-PPO pipelines from retargeted mocap learn Pump Glide and Push Glide on a passive-wheel humanoid, with simulation metrics and real-robot trials.
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A Survey of Legged Robotics in Non-Inertial Environments: Past, Present, and Future
A literature survey summarizing modeling, state estimation, control methods, applications, and open challenges for legged robots operating in non-inertial environments where the ground moves or accelerates.
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