A reinforcement-learned time optimization policy that adaptively slows upper-body motion clips improves stability and precision of humanoid standing manipulation at a modest time cost.
Real-world humanoid locomotion with reinforcement learning
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TOP: Time Optimization Policy for Stable and Accurate Standing Manipulation with Humanoid Robots
A reinforcement-learned time optimization policy that adaptively slows upper-body motion clips improves stability and precision of humanoid standing manipulation at a modest time cost.