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researcher

Joseph J. Lim

28 papers hereh-index 284.8k citations55 works total

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

author position
  • middle author13
  • last author15

Across the 28 of 28 papers where every author was matched, so the position is known.

fields
  • cs.LG15
  • cs.RO10
  • cs.CV2
  • cs.SE1
same name
  • Joseph J. Lim — 6 papers, h 7
  • Joseph J. Lim — 2 papers, h 14
  • Joseph J. Lim — 2 papers, h 2
  • Joseph J. Lim — 1 paper, h 2

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
20162023
most citedTarget-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning

163 citations · 420 across the 27 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.RO2023★ 1 cited

SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling

Jesse Zhang, Karl Pertsch, Jiahui Zhang +1

Pre-training robot policies with a rich set of skills can substantially accelerate the learning of downstream tasks. Prior works have defined pre-training tasks via natural languag…

cs.RO2023★ 2 cited

FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation

Minho Heo, Youngwoon Lee, Doohyun Lee +1

Reinforcement learning (RL), imitation learning (IL), and task and motion planning (TAMP) have demonstrated impressive performance across various robotic manipulation tasks. Howeve…

cs.SE2023

Hierarchical Neural Program Synthesis

Linghan Zhong, Ryan Lindeborg, Jesse Zhang +2

Program synthesis aims to automatically construct human-readable programs that satisfy given task specifications, such as input/output pairs or demonstrations. Recent works have de…

cs.LG2023★ 1 cited

QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing

Grace Zhang, Ayush Jain, Injune Hwang +2

Multi-task reinforcement learning (MTRL) aims to learn several tasks simultaneously for better sample efficiency than learning them separately. Traditional methods achieve this by…

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