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ASENA, an embodied agent system that enables general-purpose coding agents to program humanoid navigation, manipulation, and interaction, is presented and ASENA-VLN, a 4B monocular navigation policy that serves as an optional tool within this programmable system, is introduced.
Manipulation failures can leave scenes in states from which a task policy cannot recover. Learning corrective behaviors requires scalable failure exploration and physical grounding. We present Recova, an agent-guided framework that jointly develops task execution and recovery in a reconstructed digital twin, then verif...
Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of general physical intelligence. Although emerging coding agents can generate code to automate algorithm search, their successes remain largely con...
RoboTTT, a robot model and training recipe that scale visuomotor context to 8K timesteps, three orders of magnitude beyond state-of-the-art policies, without growing inference latency, unlocks new robot capabilities: one-shot in-context imitation from human video demonstrations, on-the-fly policy improvement, robustnes...
Graph-as-Policy (GaP) is introduced, a multi-agent coding harness that generates directed computation graphs with perception, planning, and control nodes from a Modular Open Robot Skill Library (MORSL), and can achieve success rates that significantly outperform baselines.
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