100 citations · 136 across the 14 of their papers we have counts for
24 papers · 1 filter
Leveraging Fully Observable Policies for Learning under Partial Observability
Hai Nguyen, Andrea Baisero, Dian Wang +2
Reinforcement learning in partially observable domains is challenging due to the lack of observable state information. Thankfully, learning offline in a simulator with such state i…
Grasp Learning: Models, Methods, and Performance
Robert Platt
Grasp learning has become an exciting and important topic in robotics. Just a few years ago, the problem of grasping novel objects from unstructured piles of clutter was considered…
SEIL: Simulation-augmented Equivariant Imitation Learning
Mingxi Jia, Dian Wang, Guanang Su +4
In robotic manipulation, acquiring samples is extremely expensive because it often requires interacting with the real world. Traditional image-level data augmentation has shown the…
Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection
Haojie Huang, Dian Wang, Xupeng Zhu +2
Given point cloud input, the problem of 6-DoF grasp pose detection is to identify a set of hand poses in SE(3) from which an object can be successfully grasped. This important prob…
Efficient and Accurate Candidate Generation for Grasp Pose Detection in SE(3)
Andreas ten Pas, Colin Keil, Robert Platt
Grasp detection of novel objects in unstructured environments is a key capability in robotic manipulation. For 2D grasp detection problems where grasps are assumed to lie in the pl…
Tactile Pose Estimation and Policy Learning for Unknown Object Manipulation
Tarik Kelestemur, Robert Platt, Taskin Padir
Object pose estimation methods allow finding locations of objects in unstructured environments. This is a highly desired skill for autonomous robot manipulation as robots need to e…