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David Held

Carnegie Mellon University

25 papers hereh-index 6322.3k citations196 works total

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

author position
  • first author1
  • middle author10
  • last author13

Across the 24 of 25 papers where every author was matched, so the position is known.

fields
  • cs.CV14
  • cs.RO7
  • cs.LG4
affiliations
  • Carnegie Mellon University
Homepage
same name
  • David Held — 11 papers
  • David Held — 4 papers
  • David Held — 1 paper
  • David Held — 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
20172022
most citedConstrained Policy Optimization

112 citations · 314 across the 17 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2022★ 14 cited

DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools

Xingyu Lin, Zhiao Huang, Yunzhu Li +3

We consider the problem of sequential robotic manipulation of deformable objects using tools. Previous works have shown that differentiable physics simulators provide gradients to…

cs.LG2021★ 5 cited

Active Safety Envelopes using Light Curtains with Probabilistic Guarantees

Siddharth Ancha, Gaurav Pathak, Srinivasa G. Narasimhan +1

To safely navigate unknown environments, robots must accurately perceive dynamic obstacles. Instead of directly measuring the scene depth with a LiDAR sensor, we explore the use of…

cs.LG2020★ 8 cited

ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning

Yufei Wang, Gautham Narayan Narasimhan, Xingyu Lin +2

Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning. This leads to inefficient goal sampling a…

cs.LG2017★ 112 cited

Constrained Policy Optimization

Joshua Achiam, David Held, Aviv Tamar +1

For many applications of reinforcement learning it can be more convenient to specify both a reward function and constraints, rather than trying to design behavior through the rewar…

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