38 citations · 73 across the 5 of their papers we have counts for
11 papers · 1 filter
Motion Policy Networks
Adam Fishman, Adithyavairan Murali, Clemens Eppner +3
Collision-free motion generation in unknown environments is a core building block for robot manipulation. Generating such motions is challenging due to multiple objectives; not onl…
DefGraspSim: Physics-based simulation of grasp outcomes for 3D deformable objects
Isabella Huang, Yashraj Narang, Clemens Eppner +5
Robotic grasping of 3D deformable objects (e.g., fruits/vegetables, internal organs, bottles/boxes) is critical for real-world applications such as food processing, robotic surgery…
DefGraspSim: Simulation-based grasping of 3D deformable objects
Isabella Huang, Yashraj Narang, Clemens Eppner +4
Robotic grasping of 3D deformable objects (e.g., fruits/vegetables, internal organs, bottles/boxes) is critical for real-world applications such as food processing, robotic surgery…
Alternative Paths Planner (APP) for Provably Fixed-time Manipulation Planning in Semi-structured Environments
Fahad Islam, Chris Paxton, Clemens Eppner +3
In many applications, including logistics and manufacturing, robot manipulators operate in semi-structured environments alongside humans or other robots. These environments are lar…
ACRONYM: A Large-Scale Grasp Dataset Based on Simulation
Clemens Eppner, Arsalan Mousavian, Dieter Fox
We introduce ACRONYM, a dataset for robot grasp planning based on physics simulation. The dataset contains 17.7M parallel-jaw grasps, spanning 8872 objects from 262 different categ…
Object Rearrangement Using Learned Implicit Collision Functions
Michael Danielczuk, Arsalan Mousavian, Clemens Eppner +1
Robotic object rearrangement combines the skills of picking and placing objects. When object models are unavailable, typical collision-checking models may be unable to predict coll…