100 citations · 164 across the 29 of their papers we have counts for
15 papers · 2 filters
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…
Graph-Structured Policy Learning for Multi-Goal Manipulation Tasks
David Klee, Ondrej Biza, Robert Platt
Multi-goal policy learning for robotic manipulation is challenging. Prior successes have used state-based representations of the objects or provided demonstration data to facilitat…
Visual Foresight With a Local Dynamics Model
Colin Kohler, Robert Platt
Model-free policy learning has been shown to be capable of learning manipulation policies which can solve long-time horizon tasks using single-step manipulation primitives. However…