Welcome to my website!
Leonel Rozo
Lead Research scientist
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I love machine learning and robotics!
When I started my studies in Colombia, I got excited about how the future in AI and robotics would look like. Passion, endurance and great mentors have led me to work on what I love: research the intersection of machine learning and robotics. I envision a world where smart robots augment our capabilities at physical, cognitive, and social levels! Robots that can easily assist, help, and empower people in a large diversity of scenarios and tasks. To make this dream come true, I spend my days working on machine learning tools that allow robots to naturally and smartly interact with their environment and humans. Day by day, I hit the books and investigate machine learning methods, robot control, differential geometry and optimization. |
News and updates!
Our Robot Learning group is opening several PhD positions shortly, if you would like to join us to carry out fascinating research at the intersection of differential geometry, robot control, machine learning and optimization, drop me an e-mail!
We are also offering PhD sabbatical positions as well as internship/MSc. projects!
We are also offering PhD sabbatical positions as well as internship/MSc. projects!
[September, 2023] NeurIPS, we are back! Our paper on "Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies" got accepted at NeurIPS'23. This paper proposes an RL method that accounts for the Riemannian geometry of the Wasserstein space of Gaussian Mixtures, making it very sample efficient.
[August, 2023] Yay! Our paper "The e-Bike Motor Assembly: Towards Advanced Robotic Manipulation for Flexible Manufacturing" just got accepted in RCMI. This paper introduces a systems architecture for teaching robots to perform industrial tasks as challenging as assembling parts of an e-Bike motor.
[June, 2023] Congrats to the amazing Hadi Beik-Mohammadi, who just got his paper on "Reactive motion generation on learned Riemannian manifolds" accepted in IJRR. The idea is to formulate reactive motion planning as Riemannian metric learning and shaping that build on learning from demonstrations, VAEs, and IK.
[February, 2023] We released all the talks of our tutorial on Riemannian manifolds applied in robot learning, optimization a control at IROS2022. Check our YouTube playlist!
[October, 2022] We will be organising a tutorial on Riemannian manifolds applied in robot learning, optimization a control at IROS2022. I am so happy to organize this with very smart colleagues who are very passionate about leveraging differential geometry in different robotics domains!
[September, 2022] CoRL news! We got a paper and a very cool workshop accepted! The paper is about “Learning Stable Dynamical Systems on Riemannian Manifolds”, and our workshop is gonna be about “inductive bias in robot learning”. More details soon!
[August, 2023] Yay! Our paper "The e-Bike Motor Assembly: Towards Advanced Robotic Manipulation for Flexible Manufacturing" just got accepted in RCMI. This paper introduces a systems architecture for teaching robots to perform industrial tasks as challenging as assembling parts of an e-Bike motor.
[June, 2023] Congrats to the amazing Hadi Beik-Mohammadi, who just got his paper on "Reactive motion generation on learned Riemannian manifolds" accepted in IJRR. The idea is to formulate reactive motion planning as Riemannian metric learning and shaping that build on learning from demonstrations, VAEs, and IK.
[February, 2023] We released all the talks of our tutorial on Riemannian manifolds applied in robot learning, optimization a control at IROS2022. Check our YouTube playlist!
[October, 2022] We will be organising a tutorial on Riemannian manifolds applied in robot learning, optimization a control at IROS2022. I am so happy to organize this with very smart colleagues who are very passionate about leveraging differential geometry in different robotics domains!
[September, 2022] CoRL news! We got a paper and a very cool workshop accepted! The paper is about “Learning Stable Dynamical Systems on Riemannian Manifolds”, and our workshop is gonna be about “inductive bias in robot learning”. More details soon!
