Kushal Kedia
kk837@cornell.edu
PhD Student
I am a third-year Ph.D. student in the Computer Science Department at Cornell University, advised by Prof. Sanjiban Choudhury. I completed my undergraduate studies from IIT Kharagpur.
The goal of my research is to scale up learning for robotics by utilizing large-scale human data. To this end, I develop transferable algorithms that enable robots to extract skills and knowledge from mismatched human demonstrations. I focus on two core applications:
- Skill Learning via Imitation: Enabling robots to acquire diverse abilities from large-scale human videos.
- Human-Robot Collaboration: Teaching robots to collaborate with humans by learning from human-human teams.
In the past, I have completed research internships at Cruise, working on motion prediction for self-driving cars and at Microsoft Research Lab India, working on multi-lingual LLMs.
Link to CV.
News
| Oct, 2024 | Gave a talk at RobIn Lab, UT Austin on “Transferring Collaborative Behaviors from Human-Human Teams.” |
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| Sep, 2024 | Released preprint on One-Shot Imitation under Mismatched Execution! We present RHyME, enabling robots to replicate long-horizon videos of demonstrators, even when task execution differs significantly between the demonstration and the robot’s actions. |
| May, 2024 | Gave talks at ICRA 2024 workshops on MOSAIC, our year-long work that combines multiple foundation models to design a multi-robot collaborative cooking system. MOSAIC wins the best paper award at the VLMNM workshop and the best poster at MoMa workshop! |
| Oct, 2023 | Gave a talk at RPM Lab, University of Minnesota on “Collaborative Manipulation via Human Motion Prediction”. |
| Sep, 2023 | Excited to release the Collaborative Manipulation Dataset (CoMaD)! CoMaD captures over 6 hours of motion data from 14 unique users involving both human-human and human-robot (Franka robot arm) interactions in a kitchen setting. |
| May, 2023 | First paper of my PhD on Game theoretic forecasting & planning accepted to IROS 2023! Framing forecasting and planning as an adversarial game leads to safer interactions. |
Selected Papers
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One-Shot Imitation under Mismatched Execution1st Workshop on X-Embodiment Robot Learning, CoRL, 2024 -
MOSAIC: A Modular System for Assistive and Interactive Cooking8th Annual Conference on Robot Learning (CoRL), 2024 -
InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot ActionsIEEE International Conference on Robotics and Automation (ICRA), 2024 -
ManiCast: Collaborative Manipulation with Cost-Aware Human Forecasting7th Annual Conference on Robot Learning (CoRL), 2023 -
A Game-Theoretic Framework for Joint Forecasting and Planning2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023
