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.
General embodied agents should perceive, predict, act, evaluate, and improve within a unified system. World models have shown great promise in building such agents, yet existing models typically appen...
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.
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.
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:2609.27656v1 Announce Type: cross Abstract: Physical intelligence requires more than predicting how the world may evolve: predictions must remain actionable as the world continues to change. We...
arXiv:2608. 09298v1 Announce Type: cross Abstract: Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data generation.
The survey "World-Action Models for Robot Learning and Control" reviews recent advances in coupling future world prediction with executable action generation for robots in open environments. It clarifies the scope of World-Action Models (WAMs) relative to conventional world models, model-based RL, and Vision‑Language‑Action policies, and organizes existing methods through a unified taxonomy covering representations, transition modeling, action interfaces, architectures, training pipelines, data modalities, and scaling strategies. The paper also surveys applications in manipulation, navigation, and autonomous driving, summarizes datasets, benchmarks, and metrics, and discusses key challenges such as action alignment, spatial consistency, long‑horizon memory, and efficient inference.
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...
arXiv:2609.21449v1 Announce Type: new Abstract: World Action Models bring the predictive capabilities of video models into robot action generation, providing a rich foundation for modeling future vis...
arXiv:2609.37398v1 Announce Type: new Abstract: World-Action Models (WAMs) couple action generation with predictions of how physical interactions unfold. However, current post-deployment learning par...
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.
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.