roto 2.0 provides a standardized benchmark for end-to-end blind tactile RL on 16-24 DOF robots, with open-sourced baselines achieving 13 Baoding ball rotations in 10 seconds.
In-Hand Object Rotation via Rapid Motor Adaptation,
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
PTLD distills real privileged tactile data into a state estimator to boost sim-to-real performance of proprioceptive dexterous manipulation policies, yielding 182% improvement on in-hand rotation and 57% on reorientation tasks.
GD2P generates and learns dexterous hand poses for nonprehensile pushing and pulling by combining contact-guided sampling, physics-based filtering, and a geometry-conditioned diffusion model, demonstrated on Allegro and LEAP hands in real-world tests.
citing papers explorer
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roto 2.0: The Robot Tactile Olympiad
roto 2.0 provides a standardized benchmark for end-to-end blind tactile RL on 16-24 DOF robots, with open-sourced baselines achieving 13 Baoding ball rotations in 10 seconds.
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PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation
PTLD distills real privileged tactile data into a state estimator to boost sim-to-real performance of proprioceptive dexterous manipulation policies, yielding 182% improvement on in-hand rotation and 57% on reorientation tasks.
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Learning Geometry-Aware Nonprehensile Pushing and Pulling with Dexterous Hands
GD2P generates and learns dexterous hand poses for nonprehensile pushing and pulling by combining contact-guided sampling, physics-based filtering, and a geometry-conditioned diffusion model, demonstrated on Allegro and LEAP hands in real-world tests.