Stephen James
Leading the Dyson Robot Learning Lab, London.
Dyson
Robot Learning Lab
London / Singapore
I lead the Dyson Robot Learning Lab in London, UK. Our lab investigates how to get robots to learn practical skills and behaviours in a data-driven manner, without explicitly needing to hard-code steps for each new task. Our work lies on the intersection of reinforcement learning, imitation learning, and unsupervised representation learning. Previously, I was a postdoctoral fellow at UC Berkeley, advised by Pieter Abbeel, and prior to that, I completed my PhD at Imperial College London, under the supervision of Andrew Davison. I serve as area chair for NeurIPS, CVPR, ICML, and ICLR. For a formal bio, please see here.
selected publications
- Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic ManipulationConference on Computer Vision and Pattern Recognition, 2024
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- Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via DiscretisationConference on Computer Vision and Pattern Recognition, 2022
- RLBench: The Robot Learning Benchmark & Learning EnvironmentIEEE Robotics and Automation Letters, 2020
- Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation NetworksConference on Computer Vision and Pattern Recognition, 2019
- Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage TaskConference on Robot Learning, 2017