Animesh Garg

I am an Assistant Professor of Computer Science at University of Toronto and a Faculty Member at the Vector Institute. I direct the UofT People, AI and Robotics (PAIR) group. I am affiliated with Mechanical and Industrial Engineering (courtesy) and UofT Robotics Institute. I am also a Sr. Research Scientist at Nvidia.

I earned M.S. in Computer Science and Ph.D. in Operations Research from UC, Berkeley. I worked with Ken Goldberg at Berkeley AI Research (BAIR). I also worked closely with Pieter Abbeel, Alper Atamturk & UCSF Radiation Oncology. I was later a postdoc at Stanford AI Lab with Fei-Fei Li and Silvio Savarese.

My research vision is to build the Algorithmic Foundations for Generalizable Autonomy, that enables robots to acquire skills, at both cognitive & dexterous levels, and to seamlessly interact & collaborate with humans in novel environments. My group focuses on understanding structured inductive biases and causality on a quest for general-purpose embodied intelligence that learns from imprecise information and achieves flexibility & efficiency of human reasoning.

Research Interests: Robotics, Reinforcement Learning & Optimal Control, Computer Vision
Current Applications: Mobile-Manipulation in Retail/Warehouse, personal, and surgical robotics. Read more at PAIR Website

Link to (reasonably recent) CV.
Contact me: garg@cs.toronto.edu
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Recent Talks

The following two talks cover the progression of research over the last 3 years in my group.

University of Waterloo Artificial Intelligence Institute (April 2021)

MIT Deep Learning Seminar highlighting recent work (January 2020)

Topical Workshop Talks

• Robot Learning with Implicit Representations: Perception, Action, and Simulation
  RSS Workshop on Implicit Representations for Robotic Manipulation, (Jun 2022)

• Paving the Path to Robot Autonomy with Simulation
  ICRA Workshop on Releasing Robots into the Wild, (May 2022)

• Structure in Reinforcement Learning for Robotics, Slides PDF
  ICRA Workshop on Behavior Priors in RL, (May 2022)

• Structured Inductive Bias for Imitation from Videos
  CVPR Workshop on Learning from Instructional Videos, (Jun 2020).

• Unsupervised Representations towards Counterfactual Predictions
  CVPR Workshop on Compositionality in Computer Vision, (Jun 2020).

Potential Applicants

I am accepting new students at all levels. Thanks for your interest in my group.
However, kindly do not contact me directly with regard to Graduate Admissions.
Please see the opportunities on lab website and contact me accordingly.