I lead the Machine Learning Research team at the Toyota Research Institute (TRI) in Los Altos, CA, USA. My research focuses on scaling up ML for Robot Autonomy, spanning Scene and Behavior Understanding, Simulation for Deep Learning, 3D Computer Vision, and Self-Supervised Learning (cf. a short bio below). Outside of work, I spend time with my wife, daughter, and the mountains (I love camping, climbing, and snowboarding). You can find (some of) my publications, talks, and my CV on this site.
- March 2021: 2 papers accepted at CVPR’21! (links coming soon)
- March 2021: Blake Wulfe and I won the NeurIPS 2020 ProcGen RL Competition! Find the full report here.
- February 2021: 1 paper at RoboSoft in collaboration with the brilliant TRI Robotics team on monocular depth estimation inside visuotactile sensors.
- January 2021: 1 paper on distributionally robust control accepted at RA-L/ICRA and another at ICLR on regularization for heteroskedastic and imbalanced Deep Learning. Both on robustness and with great Stanford collaborators from Mac Schwager and Tengyu Ma’s labs.
- November 2020: 1 paper (oral) at 3DV on Neural Ray Surfaces in collaboration with Greg Shakhnarovich at TTIC.
- October 2020: got a “top 10% reviewer award” at NeurIPS!
- October 2020: 2 papers accepted at CoRL 2020, including one oral on self-supervised 3D keypoints and a poster on interpretable trajectory forecasting in collaboration with Marco Pavone’s lab at Stanford
- October 2020: I gave an invited talk at IPAM covering a lot of our recent results across the full AV stack.
- August 2020: we are organizing the ECCV 2020 workshop on Perception for Autonomous Driving (PAD)
- July 2020: we are organizing the ICML 2020 workshop on AI for Autonomous Driving (AIAD)
- July 2020: 2 papers accepted at ECCV 2020, including an oral on trajectory prediction and a poster on differentiable rendering
- July 2020: 5 papers accepted at IROS 2020 and 1 at ITSC on scene flow, imitation learning, game-theoretic planning, risk sensitive control, traffic simulation, and planner testing.
- July 2020: 1 paper with Stanford on hierarchical RL and imitation in near-accidents accepted at RSS 2020
- June 2020: Together with colleagues from PFN and TRI-AD, we have released a survey on Differentiable Rendering.
- June 2020: 4 papers (3 orals!) accepted at CVPR 2020 (PackNet pseudo-lidar, real-time panoptic segmentation, auto-labeling via Differentiable Rendering, spatio-temporal graph distillation). See also our new DDAD dataset for depth estimation!
- February 2020: 1 paper with Stanford on Pedestrian Intent Prediction accepted at RA-L/ICRA 2020. See also our new STIP dataset!
- January 2020: I got promoted to Senior Manager! Super grateful to my team and excited for our next steps together!
- December 2019: 1 paper accepted at ICLR 2020 and 1 (oral) at WACV 2020
- October 2019: 1 paper accepted at the International Journal of Computer Vision (IJCV)
- September 2019: 1 paper accepted at NeurIPS 2019 (also oral at BayLearn 2019) and 2 papers (spotlights) accepted at CoRL 2019
- July 2019: 1 paper accepted (oral) at ICCV 2019
- May 2019: I did an interview with the wonderful Sam Charrington for the TWIML AI podcast!
Adrien Gaidon is the Head of Machine Learning Research at the Toyota Research Institute (TRI) in Los Altos, CA, USA. Adrien’s research focuses on scaling up ML for robot autonomy, spanning Scene and Behavior Understanding, Simulation for Deep Learning, 3D Computer Vision, and Self-Supervised Learning. He received his PhD from Microsoft Research - Inria Paris in 2012, has over 50 publications and patents in ML/CV/AI (cf. Google Scholar), top entries in international Computer Vision competitions, multiple best reviewer awards, international press coverage for his work on Deep Learning with simulation, was a guest editor for the International Journal of Computer Vision, and co-organized multiple workshops on Autonomous Driving at CVPR/ECCV/ICML. You can find him at adriengaidon.com, on linkedin, and Twitter @adnothing.