Hugging Face Trending Papers
Motus2: A Self-Evolving General World Model for Dexterous Manipulation
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arXiv AI
Sep 1
Motus2: A Self-Evolving General World Model for Dexterous Manipulation
Motus2 is a self‑evolving general world model designed for dexterous manipulation. It integrates a shared‑weight model that offers three control interfaces—a policy, a simulator, and an evaluator—forming a closed decision‑and‑learning loop for policy improvement. The system scales both model size and data, progressing from large‑scale monocular egocentric data to synchronized stereo data and robot‑domain adaptation, while also incorporating tactile feedback and a biomimetic platform with dual arms and hands.
arXiv Computer Vision
Sep 18
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.
arXiv AI
Jul 7
Worldscape-MoE: A Unified Mixture-of-Experts World Model for Scalable Heterogeneous Action Control
arXiv:2607. 03964v1 Announce Type: cross Abstract: World models are rapidly becoming a core infrastructure for embodied intelligence and interactive agents: they provide controllable simulators in which agents can perceive, act, forecast, and acquire scalable experience.
Hugging Face Trending Papers
Aug 10
WorldSimProbe: Diagnosing Simulator Faithfulness in Action-Conditioned World Models for Embodied Manipulation
Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data generation. Realizing this promise requires precise action-conditioned transitions rather than merely plausible outputs.
arXiv Computer Vision
Sep 30
WorldLine: Action-Driven Visual Simulation for Robotic Manipulation
arXiv:2609.38059v1 Announce Type: cross Abstract: Real-world robot learning is constrained by the cost of collecting experience and evaluating candidate behaviors. Video generation models offer a sca...
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.