Articulated object manipulation is a fundamental yet challenging task in robotics. Due to
significant geometric and semantic variations across object categories, previous …

Existing 3D scene understanding methods are heavily focused on 3D semantic and instance
segmentation. However identifying objects and their parts only constitutes an intermediate …

We address the challenge of generating 3D articulated objects in a controllable fashion.
Currently modeling articulated 3D objects is either achieved through laborious manual …

We introduce MultiScan, a scalable RGBD dataset construction pipeline leveraging
commodity mobile devices to scan indoor scenes with articulated objects and web-based …

Humans can easily understand a single image as depicting multiple potential objects
permitting interaction. We use this skill to plan our interactions with the world and accelerate …

We address the problem of building digital twins of unknown articulated objects from two
RGBD scans of the object at different articulation states. We decompose the problem into …

Representing and reasoning about uncertainty is crucial for autonomous agents acting in
partially observable environments with noisy sensors. Partially observable Markov decision …

Robot training often takes place in simulated environments, particularly with reinforcement
learning. Therefore, multiple training environments are generated using domain …

We present Cart, a new approach towards articulated-object manipulations by human
commands. Beyond the existing work that focuses on inferring articulation structures, we …

We propose an end-to-end trainable, cross-category method for reconstructing multiple man-
made articulated objects from a single RGBD image, focusing on part-level shape …