Papers
89
Total Citations
3,722
H-Index
30
About
Animesh Garg is a prominent robotics and machine learning researcher whose work spans robot learning, task planning, surgical automation, and the application of large language models to embodied AI. He is perhaps best known for bridging language and robotics: his ProgPrompt framework (2023, 508 citations) demonstrated how large language models can generate situated task plans for robots, while his work on chemistry robotics further extended LLM-driven automation to scientific experimentation. Garg has made foundational contributions to simulation infrastructure through ORBIT (2023, 226 citations), a unified NVIDIA Isaac-powered framework for interactive robot learning. His earlier research tackled surgical robotics with remarkable precision — developing automated suturing and tissue-cutting systems using the da Vinci surgical platform — work that garnered hundreds of citations and highlighted his ability to translate algorithmic advances into high-stakes real-world applications. His Neural Task Programming framework (2018, 153 citations) advanced hierarchical robot learning from demonstration, while his contributions to the Open X-Embodiment collaboration reflect his commitment to large-scale, generalizable robot learning. Across domains from grasping to adversarial policy robustness, Garg's research consistently pushes toward robots that are more capable, adaptable, and deployable in complex, unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1ProgPrompt: Generating Situated Robot Task Plans using Large Language Models508 citations · 2023
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- 6Neural Task Programming: Learning to Generalize Across Hierarchical Tasks153 citations · 2018
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- 9Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter108 citations · 2019
- 10Large language models for chemistry robotics98 citations · 2023