70 citations · 431 across the 35 of their papers we have counts for
7 papers · 1 filter
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…
Reactive Human-to-Robot Handovers of Arbitrary Objects
Wei Yang, Chris Paxton, Arsalan Mousavian +3
Human-robot object handovers have been an actively studied area of robotics over the past decade; however, very few techniques and systems have addressed the challenge of handing o…
Reactive Long Horizon Task Execution via Visual Skill and Precondition Models
Shohin Mukherjee, Chris Paxton, Arsalan Mousavian +3
Zero-shot execution of unseen robotic tasks is important to allowing robots to perform a wide variety of tasks in human environments, but collecting the amounts of data necessary t…
Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation
Yu Xiang, Christopher Xie, Arsalan Mousavian +1
Segmenting unseen objects in cluttered scenes is an important skill that robots need to acquire in order to perform tasks in new environments. In this work, we propose a new method…
Unseen Object Instance Segmentation for Robotic Environments
Christopher Xie, Yu Xiang, Arsalan Mousavian +1
In order to function in unstructured environments, robots need the ability to recognize unseen objects. We take a step in this direction by tackling the problem of segmenting unsee…