TrajWAM: An Efficient Trajectory-Guided World Action Model for Dynamic Robotic Manipulation

Key points

• Dynamic object manipulation requires robots to anticipate target motion while replanning fast enough to avoid executing outdated actions.1
• World Action Models (WAMs) offer a promising alternative by coupling future visual prediction with action generation, but scene-level video objectives may underrepresent target dynamics that are critical for manipulation, while iterative video-action denoising introduces substantial latency.1
• To enable one-step inference without breaking this interface, we introduce a decoupled distillation scheme that combines distribution matching for the video backbone, Trajectory ControlNet realignment, and action rollout distillation to support accurate one-step action prediction conditioned on the compressed future representation.1
• Comprehensive evaluations in simulation and the real world show that TrajWAM achieves state-of-the-art performance on dynamic object manipulation tasks while enabling efficient one-step video-action inference.1

Sources (1)

• [1]TrajWAM: An Efficient Trajectory-Guided World Action Model for Dynamic Robotic Manipulation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 04:21 AM

  We present TrajWAM, a trajectory-guided world action model for efficient dynamic robotic manipulation.

  Dynamic object manipulation requires robots to anticipate target motion while replanning fast enough to avoid executing outdated actions.

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