Bryan Lim

Bryan Lim

Imperial College London

H-index: 8

Europe-United Kingdom

About Bryan Lim

Bryan Lim, With an exceptional h-index of 8 and a recent h-index of 8 (since 2020), a distinguished researcher at Imperial College London, specializes in the field of Robotics, Machine Learning, Reinforcement Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Qdax: A library for quality-diversity and population-based algorithms with hardware acceleration

Large Language Models as In-context AI Generators for Quality-Diversity

Beyond Expected Return: Accounting for Policy Reproducibility When Evaluating Reinforcement Learning Algorithms

Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning

Online damage recovery for physical robots with hierarchical quality-diversity

Mix-ME: Quality-Diversity for Multi-Agent Learning

Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors

Efficient exploration using model-based quality-diversity with gradients

Bryan Lim Information

University

Position

___

Citations(all)

294

Citations(since 2020)

293

Cited By

10

hIndex(all)

8

hIndex(since 2020)

8

i10Index(all)

7

i10Index(since 2020)

7

Email

University Profile Page

Imperial College London

Google Scholar

View Google Scholar Profile

Bryan Lim Skills & Research Interests

Robotics

Machine Learning

Reinforcement Learning

Top articles of Bryan Lim

Title

Journal

Author(s)

Publication Date

Qdax: A library for quality-diversity and population-based algorithms with hardware acceleration

Journal of Machine Learning Research

Felix Chalumeau

Bryan Lim

Raphael Boige

Maxime Allard

Luca Grillotti

...

2024

Large Language Models as In-context AI Generators for Quality-Diversity

arXiv preprint arXiv:2404.15794

Bryan Lim

Manon Flageat

Antoine Cully

2024/4/24

Beyond Expected Return: Accounting for Policy Reproducibility When Evaluating Reinforcement Learning Algorithms

Proceedings of the AAAI Conference on Artificial Intelligence

Manon Flageat

Bryan Lim

Antoine Cully

2024/3/24

Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning

Bryan Lim

Manon Flageat

Antoine Cully

2023/7/15

Online damage recovery for physical robots with hierarchical quality-diversity

ACM Transactions on Evolutionary Learning

Maxime Allard

Simón C Smith

Konstantinos Chatzilygeroudis

Bryan Lim

Antoine Cully

2023/6/28

Mix-ME: Quality-Diversity for Multi-Agent Learning

Gardar Ingvarsson

Mikayel Samvelyan

Manon Flageat

Bryan Lim

Antoine Cully

...

2023/12/9

Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors

Shikha Surana

Bryan Lim

Antoine Cully

2023/5/29

Efficient exploration using model-based quality-diversity with gradients

Bryan Lim

Manon Flageat

Antoine Cully

2023/7/24

Multiple Hands Make Light Work: Enhancing Quality and Diversity using MAP-Elites with Multiple Parallel Evolution Strategies

arXiv preprint arXiv:2303.06137

Manon Flageat

Bryan Lim

Antoine Cully

2023/3/10

Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning

Simón C Smith

Bryan Lim

Hannah Janmohamed

Antoine Cully

2023/7/15

Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains

Luca Grillotti

Manon Flageat

Bryan Lim

Antoine Cully

2023/7/15

Neuroevolution is a competitive alternative to reinforcement learning for skill discovery

arXiv preprint arXiv:2210.03516

Felix Chalumeau

Raphael Boige

Bryan Lim

Valentin Macé

Maxime Allard

...

2022/10/6

Dynamics-aware quality-diversity for efficient learning of skill repertoires

Bryan Lim

Luca Grillotti

Lorenzo Bernasconi

Antoine Cully

2022/5/23

Learning to Walk Autonomously via Reset-Free Quality-Diversity

Bryan Lim

Alexander Reichenbach

Antoine Cully

2022/4/7

Accelerated Quality-Diversity through Massive Parallelism

Transactions on Machine Learning Research (TMLR)

Bryan Lim

Maxime Allard

Luca Grillotti

Antoine Cully

2022/2/2

Benchmarking quality-diversity algorithms on neuroevolution for reinforcement learning

arXiv preprint arXiv:2211.02193

Manon Flageat

Bryan Lim

Luca Grillotti

Maxime Allard

Simón C Smith

...

2022/11/4

Tactile object pose estimation from the first touch with geometric contact rendering

Maria Bauza Villalonga

Alberto Rodriguez

Bryan Lim

Eric Valls

Theo Sechopoulos

2021/10/4

Robust autonomous navigation of a small-scale quadruped robot in real-world environments

Thomas Dudzik

Matthew Chignoli

Gerardo Bledt

Bryan Lim

Adam Miller

...

2020

Vision aided dynamic exploration of unstructured terrain with a small-scale quadruped robot

D Kim

D Carballo

J Di Carlo

B Katz

G Bledt

...

2020

See List of Professors in Bryan Lim University(Imperial College London)