Search Results for author:
Found 5 papers, 1 papers with code
Learning to Efficiently Plan Robust Frictional Multi-Object Grasps
In physical experiments, we find a 13. 7% increase in success rate, a 1. 6x increase in picks per hour, and a 6. 3x decrease in grasp planning time compared to prior work on multi-object grasping.
Multi-Object Grasping in the Plane
In physical grasping experiments comparing performance with a single-object picking baseline, we find that the frictionless multi-object grasping system achieves 13. 6\% higher grasp success and is 59. 9\% faster, from 212 PPH to 340 PPH.
Occlusion-Aware Search for Object Retrieval in Clutter
Solving this task requires reasoning over the likely locations of the target object.
Human-like Planning for Reaching in Cluttered Environments
From this, we devised a qualitative representation of the task space to abstract the decision making, irrespective of the number of obstacles.
O2A: One-shot Observational learning with Action vectors
The distance between the action vectors from the observed third-person demonstration and trial robot executions is used as a reward for reinforcement learning of the demonstrated task.
