By feeding a policy the remaining time to a speed-based deadline and clipping per-step rotation reward, the authors train in-hand reorientation policies that match target speeds from 0.25 to 1.5 rad/s and reach up to 2.0 rad/s.
Robot dribbling using a high-speed multifingered hand and a high-speed vision system,
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Learning Time-Optimal and Speed-Adjustable Tactile In-Hand Manipulation
By feeding a policy the remaining time to a speed-based deadline and clipping per-step rotation reward, the authors train in-hand reorientation policies that match target speeds from 0.25 to 1.5 rad/s and reach up to 2.0 rad/s.