5 papers
OmniShape: Zero-Shot Multi-Hypothesis Shape and Pose Estimation in the Real World
Katherine Liu, Sergey Zakharov, Dian Chen +4
We would like to estimate the pose and full shape of an object from a single observation, without assuming known 3D model or category. In this work, we propose OmniShape, the first…
A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
TRI LBM Team, Jose Barreiros, Andrew Beaulieu +79
Robot manipulation has seen tremendous progress in recent years, with imitation learning policies enabling successful performance of dexterous and hard-to-model tasks. Concurrently…
View-Invariant Policy Learning via Zero-Shot Novel View Synthesis
Stephen Tian, Blake Wulfe, Kyle Sargent +4
Large-scale visuomotor policy learning is a promising approach toward developing generalizable manipulation systems. Yet, policies that can be deployed on diverse embodiments, envi…
ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping
Shun Iwase, Zubair Irshad, Katherine Liu +8
Robotic grasping is a cornerstone capability of embodied systems. Many methods directly output grasps from partial information without modeling the geometry of the scene, leading t…
Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation
Yinshuang Xu, Dian Chen, Katherine Liu +4
Incorporating inductive bias by embedding geometric entities (such as rays) as input has proven successful in multi-view learning. However, the methods adopting this technique typi…