70 citations · 269 across the 38 of their papers we have counts for
8 papers · 1 filter
6-DOF Grasping for Target-driven Object Manipulation in Clutter
Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner +2
Grasping in cluttered environments is a fundamental but challenging robotic skill. It requires both reasoning about unseen object parts and potential collisions with the manipulato…
Motion Reasoning for Goal-Based Imitation Learning
De-An Huang, Yu-Wei Chao, Chris Paxton +5
We address goal-based imitation learning, where the aim is to output the symbolic goal from a third-person video demonstration. This enables the robot to plan for execution and rep…
Online Replanning in Belief Space for Partially Observable Task and Motion Problems
Caelan Reed Garrett, Chris Paxton, Tomás Lozano-Pérez +2
To solve multi-step manipulation tasks in the real world, an autonomous robot must take actions to observe its environment and react to unexpected observations. This may require op…
Conditional Driving from Natural Language Instructions
Junha Roh, Chris Paxton, Andrzej Pronobis +2
Widespread adoption of self-driving cars will depend not only on their safety but largely on their ability to interact with human users. Just like human drivers, self-driving cars…
Collaborative Behavior Models for Optimized Human-Robot Teamwork
Adam Fishman, Chris Paxton, Wei Yang +3
Effective human-robot collaboration requires informed anticipation. The robot must anticipate the human's actions, but also react quickly and intuitively when its predictions are w…
"Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer
Andrew Hundt, Benjamin Killeen, Nicholas Greene +4
Current Reinforcement Learning (RL) algorithms struggle with long-horizon tasks where time can be wasted exploring dead ends and task progress may be easily reversed. We develop th…