132 citations · 289 across the 15 of their papers we have counts for
16 papers · 1 filter
StereoPose: Category-Level 6D Transparent Object Pose Estimation from Stereo Images via Back-View NOCS
Kai Chen, Stephen James, Congying Sui +3
Most existing methods for category-level pose estimation rely on object point clouds. However, when considering transparent objects, depth cameras are usually not able to capture m…
Real-World Robot Learning with Masked Visual Pre-training
Ilija Radosavovic, Tete Xiao, Stephen James +3
In this work, we explore self-supervised visual pre-training on images from diverse, in-the-wild videos for real-world robotic tasks. Like prior work, our visual representations ar…
Coarse-to-fine Q-attention with Tree Expansion
Stephen James, Pieter Abbeel
Coarse-to-fine Q-attention enables sample-efficient robot manipulation by discretizing the translation space in a coarse-to-fine manner, where the resolution gradually increases at…
Coarse-to-Fine Q-attention with Learned Path Ranking
Stephen James, Pieter Abbeel
We propose Learned Path Ranking (LPR), a method that accepts an end-effector goal pose, and learns to rank a set of goal-reaching paths generated from an array of path generating m…
SafePicking: Learning Safe Object Extraction via Object-Level Mapping
Kentaro Wada, Stephen James, Andrew J. Davison
Robots need object-level scene understanding to manipulate objects while reasoning about contact, support, and occlusion among objects. Given a pile of objects, object recognition…
ReorientBot: Learning Object Reorientation for Specific-Posed Placement
Kentaro Wada, Stephen James, Andrew J. Davison
Robots need the capability of placing objects in arbitrary, specific poses to rearrange the world and achieve various valuable tasks. Object reorientation plays a crucial role in t…