3 accepted papers
Humans instinctively adjust their viewpoints to resolve occlusions and infer spatial relationships, enabling effective perception and navigation in cluttered environments. This capability, however, remains a significant challenge for robotic systems. To address this, we propose GPD-AP, a novel activ…
Catching flying objects with a cushioning process is a skill commonly performed by humans, yet it remains a significant challenge for robots. In this paper, we present a framework that combines optimization and learning to achieve compliant catching on mobile manipulators (CCMM). First, we propose a
Recent advances in robotic manipulation leverage foundation models pre-trained on internet-scale data, where keypoint-based representations have shown promising results in spatial reasoning. However, existing approaches primarily focus on zero-shot generalization or human-collected demonstrations, w