aiqbench analysis
TrajWAM advances dynamic robotic manipulation by explicitly coupling future visual prediction of object trajectories with action generation, addressing latency issues in existing World Action Models. The method achieves state-of-the-art performance through a decoupled distillation scheme that enables efficient one-step inference without sacrificing the ability to anticipate target motion. This research represents a significant step forward in making AI-driven robots capable of handling real-time, unpredictable environments.
TrajWAM: An Efficient Trajectory-Guided World Action Model for Dynamic Robotic Manipulation
arXiv:2610.25763v1 Announce Type: new Abstract: Dynamic object manipulation requires robots to anticipate target motion while replanning fast enough to avoid executing outdated actions. Existing approaches either rely on fast reactive control without an explicit future target state or encode anticipated motion only implicitly. 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. We present TrajWAM, a trajectory-guided world action model for efficient dynamic robotic manipulation. Our key insight is to explicitly ground future visual imagination in predicted target-object motion and use the resulting future representation to condition action generation, while preserving this anticipation pathway after acceleration. Given a short history of object masks, a history-conditioned motion predictor forecasts future masks encoding the target object's trajectory. A lightweight Trajectory ControlNet then steers a pretrained video model toward trajectory-consistent futures. The resulting future representation conditions an action expert to predict executable action chunks. 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. 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. Project page: https://kevingeng3.github.io/TrajWAM/.
Abstract:Dynamic object manipulation requires robots to anticipate target motion while replanning fast enough to avoid executing outdated actions. Existing approaches either rely on fast reactive control without an explicit future target state or encode anticipated motion only implicitly. 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. We present TrajWAM, a trajectory-guided world action model for efficient dynamic robotic manipulation. Our key insight is to explicitly ground future visual imagination in predicted target-object motion and use the resulting future representation to condition action generation, while preserving this anticipation pathway after acceleration. Given a short history of object masks, a history-conditioned motion predictor forecasts future masks encoding the target object's trajectory. A lightweight Trajectory ControlNet then steers a pretrained video model toward trajectory-consistent futures. The resulting future representation conditions an action expert to predict executable action chunks. 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. 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. Project page: this https URL.
Robotics (cs.RO)
arXiv:2610.25763 [cs.RO]
(or arXiv:2610.25763v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.25763 Focus to learn more arXiv-issued DOI via DataCite
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