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E. Mitchell

21 papers hereh-index 2220.5k citations36 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author7
  • middle author12

Across the 19 of 21 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.CL5
  • cs.RO4
  • cs.AI3
  • cs.CV2
same name
  • E. Mitchell — 6 papers, h 11
  • E. Mitchell — 4 papers, h 4
  • E. Mitchell — 2 papers, h 15
  • E. Mitchell — 2 papers, h 7
  • E. Mitchell — 1 paper, h 4
  • E. Mitchell — 1 paper, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192023
most citedOn the Opportunities and Risks of Foundation Models

2.3k citations · 2.9k across the 20 of their papers we have counts for

collaborators
Showing cs.ROShow all

4 papers · 1 filter

cs.RO2022★ 5 cited

Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding

William Chen, Siyi Hu, Rajat Talak +1

Abstract semantic 3D scene understanding is a problem of critical importance in robotics. As robots still lack the common-sense knowledge about household objects and locations of a…

cs.RO2021★ 12 cited

Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation

Suraj Nair, Eric Mitchell, Kevin Chen +3

We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. In order to accomplish this, humans need easy and eff…

cs.RO2019

Higher Order Function Networks for View Planning and Multi-View Reconstruction

Selim Engin, Eric Mitchell, Daewon Lee +2

We consider the problem of planning views for a robot to acquire images of an object for visual inspection and reconstruction. In contrast to offline methods which require a 3D mod…

cs.RO2019

Pixels to Plans: Learning Non-Prehensile Manipulation by Imitating a Planner

Tarik Tosun, Eric Mitchell, Ben Eisner +6

We present a novel method enabling robots to quickly learn to manipulate objects by leveraging a motion planner to generate "expert" training trajectories from a small amount of hu…

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