Generalizable 3D part segmentation is important but challenging in vision and robotics.
Training deep models via conventional supervised methods requires large-scale 3D …

Generalizable manipulation skills, which can be composed to tackle long-horizon and
complex daily chores, are one of the cornerstones of Embodied AI. However, existing …

Point clouds are a widely available and canonical data modality which convey the 3D
geometry of a scene. Despite significant progress in classification and segmentation from …

To enable general-purpose robots, we will require the robot to operate daily articulated
objects as humans do. Current robot manipulation has heavily relied on using a parallel …

Learning generalizable visual representations from Internet data has yielded promising
results for robotics. Yet prevailing approaches focus on pre-training 2D representations …

The ability for robots to comprehend and execute manipulation tasks based on natural
language instructions is a long-term goal in robotics. The dominant approaches for …

Understanding the continuous states of objects is essential for task learning and planning in
the real world. However, most existing task learning benchmarks assume discrete (eg …

Robots rely heavily on sensors, especially RGB and depth cameras, to perceive and interact
with the world. RGB cameras record 2D images with rich semantic information while missing …

Executing contact-rich manipulation tasks necessitates the fusion of tactile and visual
feedback. However, the distinct nature of these modalities poses significant challenges. In …

Recent studies on visual reinforcement learning (visual RL) have explored the use of 3D
visual representations. However, none of these work has systematically compared the …