A Sim2Real2Sim learning pipeline enables a real-world dexterous robot to play piano pieces including Happy Birthday and Ode to Joy with an average F1-score of 0.881.
Lessons from learning to spin" pens"
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ETac is a data-driven tactile simulation framework that matches FEM deformation accuracy at high speed, supporting 4096 parallel environments at 869 FPS and yielding 84.45% success in blind grasping across four object types.
FlashSAC improves training speed and final performance of off-policy RL on high-dimensional robot tasks by reducing update frequency, increasing model scale, and bounding norms to limit critic error accumulation.
A unified parameter space and canonical URDF enable cross-embodiment dexterous grasping policies with 81.9% zero-shot success on unseen hands like the 3-finger LEAP Hand.
citing papers explorer
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Learning to Play Piano in the Real World
A Sim2Real2Sim learning pipeline enables a real-world dexterous robot to play piano pieces including Happy Birthday and Ode to Joy with an average F1-score of 0.881.
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ETac: A Lightweight and Efficient Tactile Simulation Framework for Learning Dexterous Manipulation
ETac is a data-driven tactile simulation framework that matches FEM deformation accuracy at high speed, supporting 4096 parallel environments at 869 FPS and yielding 84.45% success in blind grasping across four object types.
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FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control
FlashSAC improves training speed and final performance of off-policy RL on high-dimensional robot tasks by reducing update frequency, increasing model scale, and bounding norms to limit critic error accumulation.
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One Hand to Rule Them All: Canonical Representations for Unified Dexterous Manipulation
A unified parameter space and canonical URDF enable cross-embodiment dexterous grasping policies with 81.9% zero-shot success on unseen hands like the 3-finger LEAP Hand.