262 citations · 430 across the 32 of their papers we have counts for
7 papers · 1 filter
DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality
Ankur Handa, Arthur Allshire, Viktor Makoviychuk +11
Recent work has demonstrated the ability of deep reinforcement learning (RL) algorithms to learn complex robotic behaviours in simulation, including in the domain of multi-fingered…
RGB-Only Reconstruction of Tabletop Scenes for Collision-Free Manipulator Control
Zhenggang Tang, Balakumar Sundaralingam, Jonathan Tremblay +6
We present a system for collision-free control of a robot manipulator that uses only RGB views of the world. Perceptual input of a tabletop scene is provided by multiple images of…
Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions
Yun-Chun Chen, Adithyavairavan Murali, Balakumar Sundaralingam +3
The pipeline of current robotic pick-and-place methods typically consists of several stages: grasp pose detection, finding inverse kinematic solutions for the detected poses, plann…
HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers
Yu-Wei Chao, Chris Paxton, Yu Xiang +6
We introduce a new simulation benchmark "HandoverSim" for human-to-robot object handovers. To simulate the giver's motion, we leverage a recent motion capture dataset of hand grasp…
Correcting Robot Plans with Natural Language Feedback
Pratyusha Sharma, Balakumar Sundaralingam, Valts Blukis +5
When humans design cost or goal specifications for robots, they often produce specifications that are ambiguous, underspecified, or beyond planners' ability to solve. In these case…
Model Predictive Control for Fluid Human-to-Robot Handovers
Wei Yang, Balakumar Sundaralingam, Chris Paxton +4
Human-robot handover is a fundamental yet challenging task in human-robot interaction and collaboration. Recently, remarkable progressions have been made in human-to-robot handover…
DefGraspSim: Physics-based simulation of grasp outcomes for 3D deformable objects
Isabella Huang, Yashraj Narang, Clemens Eppner +5
Robotic grasping of 3D deformable objects (e.g., fruits/vegetables, internal organs, bottles/boxes) is critical for real-world applications such as food processing, robotic surgery…