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

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Research

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.

News

  • [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

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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

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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)

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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)

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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
(arXiv)

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Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning


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

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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)

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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
(arXiv)

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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
(arXiv)

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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)

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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)

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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)

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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)

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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
(paper)

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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

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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
(arXiv)

Workshop Papers

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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
(arXiv)

Teaching

  • 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