Inferring affordable (ie, graspable) parts of arbitrary objects based on human specifications
is essential for robots advancing toward open-vocabulary manipulation. Current grasp …

Large foundation models have shown strong open-world generalization to complex
problems in vision and language, but similar levels of generalization have yet to be …

Scalable learning of humanoid robots is crucial for their deployment in real-world
applications. While traditional approaches primarily rely on reinforcement learning or …

Human demonstrations as prompts are a powerful way to program robots to do long-horizon
manipulation tasks. However, translating these demonstrations into robot-executable actions …

Dense 3D correspondence can enhance robotic manipulation by enabling the
generalization of spatial, functional, and dynamic information from one object to an unseen …

Effectively manipulating articulated objects in household scenarios is a crucial step toward
achieving general embodied artificial intelligence. Mainstream research in 3D vision has …

Robot learning is witnessing a significant increase in the size, diversity, and complexity of
pre-collected datasets, mirroring trends in domains such as natural language processing …

Diffusion-based policies have shown impressive performance in robotic manipulation tasks
while struggling with out-of-domain distributions. Recent efforts attempted to enhance …

Real-world environments require robots to continuously acquire new skills while retaining
previously learned abilities, all without the need for clearly defined task boundaries. Storing …