arXiv AI
PlayPolicy: Dexterity from Supervised Play
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arXiv AI
Oct 16
Dexterity from Supervised Play
The paper introduces DSP: Dexterity from Supervised Play, a framework where a robot learns a play policy from minutes of human play via behavior cloning. The robot then autonomously uses this policy to gather hours of interaction data, training a world model that guides model‑predictive control during inference. The approach is validated on underactuated pushing and in‑hand pose reorientation tasks, outperforming hindsight behavior cloning by 69.6% in real‑world success rate and matching reinforcement learning performance with 490× less data.
Hugging Face Trending Papers
Oct 15
Dexterity from Supervised Play
The paper introduces DSP: Dexterity from Supervised Play, a framework where a robot learns a play policy from brief human demonstrations via behavior cloning and then autonomously gathers extensive interaction data to train a world model. During inference, the play policy guides model‑predictive control by proposing action sequences evaluated by the learned model, and a self‑play distillation step produces a closed‑loop, goal‑conditioned policy. Experiments on underactuated pushing and in‑hand pose reorientation show DSP outperforms hindsight behavior cloning by 69.6% in success rate and matches reinforcement learning performance while using 490 times less data.
arXiv Computer Vision
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Dex-One2Many: Learning Dexterous Manipulation from a Single Human Demonstration
Dex-One2Many introduces a real-to-sim-to-real framework that learns a generalizable dexterous manipulation policy from a single human video. By abstracting the video into sequential scene graphs, the method guides reinforcement learning with dense rewards and diverse reset states, enabling efficient exploration while preserving broad generalizability. Trained entirely in simulation, Dex-One2Many achieves zero‑shot transfer to a real multi‑fingered hand and outperforms baselines by 6.5% on seen configurations and 71% on unseen scenarios across five tool‑use and manipulation tasks.
arXiv Machine Learning
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Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations
arXiv:2606. 10614v1 Announce Type: cross Abstract: Robotic foundation models pre-trained on human demonstration videos have shown promise, but a significant embodiment gap remains when the resulting policies are deployed on real robots.
arXiv AI
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Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration
arXiv:2607. 00033v1 Announce Type: cross Abstract: Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains challenging.
arXiv Computer Vision
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DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation
DexTouch-WM is an action‑conditioned world model that learns from scalable human touch to predict future RGB observations and bilateral tactile dynamics for dexterous robot manipulation. By using compatible piezoresistive arrays on both human and robot hands and retargeting human motion into the robot action space, the model can be supervised with human interaction data while keeping a fixed amount of real‑robot supervision. Experiments show that adding up to 100 hours of human interaction improves robot‑domain visual, geometric, and contact prediction, and the model can serve as a surrogate environment for policy evaluation and synthetic trajectory generation.