Sanjiban Choudhury

sanjibanc at cornell dot edu

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Assistant Professor

Cornell Computer Science

I am an Assistant Professor at Cornell Computer Science where I lead the People and Robot Teaching and Learning (PoRTaL) group. I am also a Research Scientist at Aurora.

My research focuses on advancing imitation learning and decision making in robotics and AI systems. My group works on both theory and algorithms for efficiently training agents with applications to home robots, multi-agent games, language models and self-driving.

Prospective students and undergraduate researchers see this note.

Selected Papers

2024_mosaic

2024_mosaic_skills

MOSAIC: A Modular System for Assistive and Interactive Cooking
Yuki Wang, Kushal Kedia, Juntao Ren, Rahma Abdullah, ..., Sanjiban Choudhury

paper / website

MOSAIC combines large pre-trained models for general tasks with task-specific modules to enable collaborative cooking.


2023_moment_matching

The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
Anirudh Vemula, Yuda Song, Aarti Singh, J. Andrew Bagnell, Sanjiban Choudhury
International Conference on Machine Learning (ICML), 2023
paper / talk

We propose a completely new in Model-based Reinforcement Learning (MBRL) that solves two long-standing challenges: computational efficiency and mismatched objectives.


2023_demo2code

Demo2Code: From Summarizing Demonstrations to Synthesizing Code via Extended Chain-of-Thought
Yuki Wang, Gonzalo Gonzalez-Pumariega, Yash Sharma, Sanjiban Choudhury
Neural Information Processing Systems (NeurIPS), 2023
paper / website

Demo2Code leverages LLMs to convert demonstrations to robot task code. It recursively summarizes both down to a task specification, then recursively expands the specification to an executable task code.


2023_manicast

ManiCast: Collaborative Manipulation with Cost-Aware Human Forecasting
Kushal Kedia, Prithwish Dan, Atiksh Bhardwaj, Sanjiban Choudhury
Conference on Robot Learning (CoRL), 2023
paper / website

ManiCast trains transformer models to predict human motion using cost-aware losses. These forecasts are then used by MPC for collaborative manipulation tasks.