667 citations · 2.8k across the 127 of their papers we have counts for
28 papers · 1 filter
A Billion Ways to Grasp: An Evaluation of Grasp Sampling Schemes on a Dense, Physics-based Grasp Data Set
Clemens Eppner, Arsalan Mousavian, Dieter Fox
Robot grasping is often formulated as a learning problem. With the increasing speed and quality of physics simulations, generating large-scale grasping data sets that feed learning…
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…
ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
Mohit Shridhar, Jesse Thomason, Daniel Gordon +5
We present ALFRED (Action Learning From Realistic Environments and Directives), a benchmark for learning a mapping from natural language instructions and egocentric vision to seque…
LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation
Keunhong Park, Arsalan Mousavian, Yu Xiang +1
Current 6D object pose estimation methods usually require a 3D model for each object. These methods also require additional training in order to incorporate new objects. As a resul…
Contextual Reinforcement Learning of Visuo-tactile Multi-fingered Grasping Policies
Visak Kumar, Tucker Hermans, Dieter Fox +2
Using simulation to train robot manipulation policies holds the promise of an almost unlimited amount of training data, generated safely out of harm's way. One of the key challenge…
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…