Animesh Garg
About
Animesh Garg is a Canada CIFAR AI Chair at the Vector Institute, an assistant professor at the department of computer science. He leads the Toronto People, AI, and Robotics (PAIR) research group at the University of Toronto. Garg is also a research scientist at NVIDIA Research in machine learning for robotics.
Garg’s research focuses on machine learning algorithms for perception and control in robotics. He aims to enable generalizable autonomy through efficient robot learning for long-term sequential decision making. The principal technical focus lies in understanding representations and algorithms to enable simplicity and generality of learning for interaction in autonomous agents. He actively works on applications of robot manipulation in industrial and healthcare robotics.
Awards
- Best Paper IEEE International Conference on Robotics and Automation, 2019
- Best Cognitive Paper Finalist IEEE International Conference on Robotics and Systems, 2019
- Best Paper Award at Robot Learning Workshop NeurIPS, 2019
- Best Medical Robotics Finalist IEEE International Conference on Robotics and Automation, 2015
Relevant Publications
- Fang, K., Zhu, Y., Garg, A., Kurenkov, A., Mehta, V., Fei-Fei, L., & Savarese, S. (2020). Learning task-oriented grasping for tool manipulation from simulated self-supervision. The International Journal of Robotics Research, 39(2-3), 202-216.
- Lee, M. A., Zhu, Y., Srinivasan, K., Shah, P., Savarese, S., Fei-Fei, L., ... & Bohg, J. (2019). Making sense of vision and touch: Self-supervised learning of multimodal representations for contact-rich tasks. In 2019 International Conference on Robotics and Automation (ICRA) (pp. 8943-8950). IEEE.
- Huang, D. A., Nair, S., Xu, D., Zhu, Y., Garg, A., Fei-Fei, L., ... & Niebles, J. C. (2019). Neural task graphs: Generalizing to unseen tasks from a single video demonstration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8565-8574).
- Murali, A., Sen, S., Kehoe, B., Garg, A., McFarland, S., Patil, S., ... & Goldberg, K. (2015). Learning by observation for surgical subtasks: Multilateral cutting of 3d viscoelastic and 2d orthotropic tissue phantoms. In 2015 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1202-1209). IEEE.
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