Faculty profile
Animesh Garg
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Research
Latest papers
Accelerating discovery in natural science laboratories with AI and robotics: Perspectives and challenges.
Science robotics · 2025
Integration of Reinforcement Learning in a Virtual Robotic Surgical Simulation.
Surgical innovation · 2023
Learning latent actions to control assistive robots.
Autonomous robots · 2022
Latest funding
- $12,500
Causal Models for Generalizable Robot Learning
NSERC · 2021 · Principal investigator
- $96,000
Causal Models for Generalizable Robot Learning
NSERC · 2021 · Principal investigator
3 publications.
Accelerating discovery in natural science laboratories with AI and robotics: Perspectives and challenges.
Cooper AI, Courtney P, Darvish K, Eckhoff M, Fakhruldeen H, Gabrielli A, Garg A, Haddadin S, Harada K, Hein J, Hübner M, Knobbe D, Pizzuto G, Shkurti F, Shrestha R, Thurow K, Vescovi R, Vogel-Heuser B, Wolf Á, Yoshikawa N, Zeng Y, Zhou Z, Zwirnmann H
Integration of Reinforcement Learning in a Virtual Robotic Surgical Simulation.
Bourdillon AT, Garg A, Wang H, Woo YJ, Pavone M, Boyd J
Learning latent actions to control assistive robots.
Losey DP, Jeon HJ, Li M, Srinivasan K, Mandlekar A, Garg A, Bohg J, Sadigh D
Causal Models for Generalizable Robot Learning
Principal investigators: Garg, Animesh
Causal Models for Generalizable Robot Learning
Principal investigators: Garg, Animesh
Keywords: causal inference; batch reinforcement learning; meta-l learning; imitation learning; robot manipulation; robot learning,; causal discovery; model based reinforcement learning; transfer learning; continual learning
From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.
Frequent collaborators
- Jason Hein and Florian Shkurti: 1 shared paper
- Jason Hein and Animesh Garg: 1 shared paper
- Florian Shkurti and Animesh Garg: 1 shared paper
- Department of Mathematical & Computational Sciences
- Department of Chemistry
Co-authors at University of Toronto, colored by department. Thicker lines mean more shared papers; select anyone to open their profile and their own map.
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