About
My lab’s research goal is to get robots to generalize and adapt in the messy world we live in. Our research focuses broadly on robot learning and decision making, with an emphasis on large-scale learning (both data and models), representation learning for sensory data, developing algorithms to model actions and behavior, reinforcement learning for adapting to new scenarios, and building open-sourced affordable robots.
Awards
- Fellow, Sloan Foundation, 2025
- NSF Career Award, NSF, 2024
- IEEE RAL Early Career Award in Robotics and Automation, IEEE, 2024
- Fellow, Packard Foundation, 2023
- Innovators under 35 (TR35), MIT TR, 2023
Relevant Publications
- Liu, P., Orru, Y., Vakil, J., Paxton, C., Shafiullah, N. M. M., & Pinto, L. (2024). Ok-robot: What really matters in integrating open-knowledge models for robotics. arXiv preprint arXiv:2401.12202.
- Haldar, S., Pari, J., Rai, A., & Pinto, L. (2023, January). Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations. In Robotics: Science and Systems.
- Pinto, L., & Gupta, A. (2016, May). Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours. In 2016 IEEE international conference on robotics and automation (ICRA) (pp. 3406-3413). IEEE.