Bryan Lim

I am a PhD student at the Adaptive and Intelligent Robotics Lab, part of the Department of Computing at Imperial College London, where I work on robotics and machine learning. My PhD advisor is Dr. Antoine Cully.

Previously, I completed my MEng year at MIT, where I worked with Prof. Alberto Rodriguez and Prof. Sangbae Kim. I have an MEng in Mechanical Engineering from Imperial College London.

 /   /   /   /  Google Scholar

profile photo


My research is at the intersection of machine learning and robotics. I am interested in ways to enable robots to be general-purpose. I believe this requires robots to learn effectively and adapt directly in the real-world. I believe open-ended learning systems, where agents can continually adapt to constantly evolving environments have the potential to make this possible. To enable such systems, I study Quality-Diversity algorithms which encourages novelty and diversity as a key component in the process.


  • [12/2023] Our work on accounting for policy reproducibility when evaluating RL algorithms was accepted at AAAI!
  • [6/2023] Check out our work on using RL for behavioural diversity of a quadruped, including dynamic and agile three-legged locomotion behaviour. Video here! Interestingly similar athletic intelligence and adaptability to that of animals!
  • [4/2023] GDAQD accepted at ALIFE 2023!
  • [3/2023] Two papers and a poster accepted at GECCO 2023. Going to be a fun summer week in Lisbon!
  • [1/2023] Our work studying QD-based neuroevolution and unsupervised deep RL methods was accepted at ICLR 2023! Grateful to be able to work with such a great team of collaborators on this one!
  • [1/2023] One paper accepted at ICRA 2023. See you in London!
  • [12/2022] Will be presenting our work on scaling model-based QD with gradient updates at the NeurIPS Deep Reinforcement Learning Workshop!
  • [7/2022] We officially released QDax, an open-source library in JAX for Quality-Diversity and Population-based algorithms. Check it out!
  • [3/2022] I was awarded the Imperial College Robotics Forum - Amazon PhD Prize for Outstanding Achievement in Robotics!
  • [3/2022] Will be presenting 2 papers at the Agent Learning in Open-Endedness Workshop at ICLR 2022. We will present QDax in a spotlight talk!
  • [3/2022] 1 paper and 1 poster accepted to GECCO 2022! Excited to be heading back to Boston!
  • [1/2022] DA-QD accepted at ICRA 2022! My first paper since strating the PhD!

Conference Papers

project image

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

M.Flageat*, Bryan Lim*, A. Cully
AAAI Conference on Artificial Intelligence (AAAI), 2023

project image

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

M.Flageat*, Bryan Lim*, A. Cully
Pre-print, 2023
(arXiv) (project page)

project image

Quality-Diversity Optimization on a Physical Robot through Dynamics-Aware and Reset-free Learning

S. Smith, Bryan Lim, H. Janmohamed, A. Cully
The Genetic and Evolutionary Computation Conference (GECCO) - Poster, 2023
(arXiv) (video)

project image

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

L.Grillotti*, M.Flageat*, Bryan Lim, A. Cully
The Genetic and Evolutionary Computation Conference (GECCO), 2023

project image

Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning

Bryan Lim*, M.Flageat*, A. Cully
The Genetic and Evolutionary Computation Conference (GECCO), 2023

project image

Efficient Exploration using Model-Based Quality-Diversity with Gradients

Bryan Lim*, M.Flageat*, A. Cully
Conference on Artificial Life (ALIFE), 2023
Deep Reinforcement Learning Workshop, NeurIPS, 2022
(arXiv) (video)

project image

Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery

F. Chalumeau*, R. Boige*, Bryan Lim, V. Macé, M. Allard, A. Flajolet, A. Cully, T. Pierrot
International Conference on Learning Representations (ICLR) (Spotlight), 2023

project image

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

Shikha Surana*, Bryan Lim*, Antoine Cully
International Conference on Robotics and Automation (ICRA), 2023

project image

Accelerated Quality-Diversity through Massive Parallelism

Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully
Transactions on Machine Learning Research (TMLR), 2023
Agent Learning in Open-Endedness Workshop, ICLR (Spotlight), 2022
The Genetic and Evolutionary Computation Conference (GECCO) - Poster, 2022
(arXiv) (code)

project image

Learning to Walk Autonomously via Reset-Free Quality-Diversity

Bryan Lim*, Alexander Reichenbach*, Antoine Cully
The Genetic and Evolutionary Computation Conference (GECCO), 2022
Agent Learning in Open-Endedness Workshop, ICLR, 2022
(arXiv) (project page)

project image

Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires

Bryan Lim, Luca Grillotti, Lorenzo Bernasconi, Antoine Cully
International Conference on Robotics and Automation (ICRA), 2022
(arXiv) (code) (project page)

project image

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

M Bauza, E Valls, Bryan Lim, T Sechopoulos, A Rodriguez
Conference on Robot Learning (CoRL), 2020
(arXiv) (video) (project page)

project image

Robust Autonomous Navigation of a Small-Scale Quadruped Robot in Real-World Environments

T Dudzik, M Chignoli, G Bledt, Bryan Lim, A Miller, D Kim, S Kim
International Conference on Intelligent Robots and Systems (IROS), 2020

project image

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

D Kim, D Carballo, J Di Carlo, B Katz, G Bledt, Bryan Lim, S Kim
International Conference on Robotics and Automation (ICRA), 2020
(video) (paper)

Journal Papers

project image

Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity

M. Allard, S. C. Smith, K. Chatzilygeroudis, Bryan Lim, A. Cully
ACM Transactions on Evolutionary Learning and Optimization (TELO), 2023

Workshop Papers

project image

Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning

M. Flageat*, Bryan Lim*, L. Grillotti, M. Allard, S. C. Smith, A. Cully
Quality Diversity Algorithm Benchmarks Workshop, GECCO, 2022


  • C70050 Introduction to Machine Learning, TA
  • C70028 Reinforcement Learning, TA
  • C70067 Robot Learning and Control, TA

Design and source code from Jon Barron's website and Leonid Keselman's Jekyll fork