DRIS improves zero-shot sim-to-real transfer for reactive catching by maintaining and acting on sets of randomized dynamics instances instead of single instances per episode.
Iterative residual policy: for goal-conditioned dynamic manipulation of deformable objects.The International Journal of Robotics Research, 43(4):389–404
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A new GPU-accelerated deformable simulation framework trains manipulation policies in minutes using only synthetic data, achieving robust zero-shot transfer to physical robots.
citing papers explorer
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Zero-Shot Sim-to-Real Robot Learning: A Dexterous Manipulation Study on Reactive Catching
DRIS improves zero-shot sim-to-real transfer for reactive catching by maintaining and acting on sets of randomized dynamics instances instead of single instances per episode.
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FLASH: Fast Learning via GPU-Accelerated Simulation for High-Fidelity Deformable Manipulation in Minutes
A new GPU-accelerated deformable simulation framework trains manipulation policies in minutes using only synthetic data, achieving robust zero-shot transfer to physical robots.