Abstract: This paper presents Adaptive Whole-body LocoManipulation, AdaptManip, a fully autonomous framework for humanoid robots to perform integrated navigation, object lifting, and delivery. Unlike prior imitation learning-based approaches that rely on human demonstrations and are often brittle...
Abstract: Robot manipulation succeeds only when perception preserves the aspects of a scene that matter for action. Yet most robot learning pipelines still rely on visual encoders pre-trained for static recognition or vision-language alignment, leaving motion understanding to downstream policies....
Abstract: Egocentric human videos provide a scalable source of manipulation demonstrations; however, deploying them on robots requires active viewpoint control to maintain task-critical visibility, which human viewpoint imitation often fails to provide due to human-specific priors. We propose Ego...