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Jitendra Malik

UC Berkeley

40 papers hereh-index 139115.9k citations397 works total

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

author position
  • middle author15
  • last author25

Across the 40 of 40 papers where every author was matched, so the position is known.

fields
  • cs.CV24
  • cs.LG9
  • cs.RO4
  • cs.AI2
  • cs.GR1
affiliations
  • UC Berkeley
  • Facebook
Homepage
same name
  • Jitendra Malik — 35 papers
  • Jitendra Malik — 3 papers, h 15
  • Jitendra Malik — 3 papers
  • Jitendra Malik — 1 paper
  • Jitendra Malik — 1 paper

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
20152022
most citedVisual Semantic Role Labeling

331 citations · 831 across the 12 of their papers we have counts for

collaborators
Showing cs.ROShow all

4 papers · 1 filter

cs.RO2022★ 31 cited

Legged Locomotion in Challenging Terrains using Egocentric Vision

Ananye Agarwal, Ashish Kumar, Jitendra Malik +1

Animals are capable of precise and agile locomotion using vision. Replicating this ability has been a long-standing goal in robotics. The traditional approach has been to decompose…

cs.RO2021★ 6 cited

Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robots

Zipeng Fu, Ashish Kumar, Jitendra Malik +1

Legged locomotion is commonly studied and expressed as a discrete set of gait patterns, like walk, trot, gallop, which are usually treated as given and pre-programmed in legged rob…

cs.RO2019

Learning Navigation Subroutines from Egocentric Videos

Ashish Kumar, Saurabh Gupta, Jitendra Malik

Planning at a higher level of abstraction instead of low level torques improves the sample efficiency in reinforcement learning, and computational efficiency in classical planning.…

cs.RO2018

More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch

Roberto Calandra, Andrew Owens, Dinesh Jayaraman +5

For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus can…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.