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Bin Li

4 papers hereh-index 6126 citations10 works total

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

author position
  • middle author1
  • last author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI1
  • cs.CV1
  • cs.LG1
  • cs.RO1
same name
  • Bin Li — 36 papers, h 21
  • Bin Li — 29 papers, h 13
  • Bin Li — 28 papers, h 10
  • Bin Li — 25 papers, h 13
  • Bin Li — 21 papers, h 10
  • Bin Li — 16 papers, h 11

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

most citedDecomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning

12 citations · 15 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2022★ 2 cited

TODE-Trans: Transparent Object Depth Estimation with Transformer

Kang Chen, Shaochen Wang, Beihao Xia +3

Transparent objects are widely used in industrial automation and daily life. However, robust visual recognition and perception of transparent objects have always been a major chall…

cs.RO2022

What You See is What You Grasp: User-Friendly Grasping Guided by Near-eye-tracking

Shaochen Wang, Wei Zhang, Zhangli Zhou +5

This work presents a next-generation human-robot interface that can infer and realize the user's manipulation intention via sight only. Specifically, we develop a system that integ…

cs.LG2021★ 1 cited

Context-Based Soft Actor Critic for Environments with Non-stationary Dynamics

Yuan Pu, Shaochen Wang, Xin Yao +1

The performance of deep reinforcement learning methods prone to degenerate when applied to environments with non-stationary dynamics. In this paper, we utilize the latent context r…

cs.AI2021★ 12 cited

Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning

Yuan Pu, Shaochen Wang, Rui Yang +2

Deep reinforcement learning methods have shown great performance on many challenging cooperative multi-agent tasks. Two main promising research directions are multi-agent value fun…

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