Samrat Sahoo
I am currently a Master’s student at Stanford University, where I’ve been fortunate enough to work on robot learning with Jeannette Bohg and Dorsa Sadigh. I have also had the opportunity to collaborate with Tom Silver at Princeton PRPL.
My research is interested in answering (1) how can we achieve large-scale robot data collection with limited human supervision and (2) how can we use our collected data to train policies that generalize reliably to real-world execution? Before Stanford, I studied computer science at Georgia Tech, where I specialized in artificial intelligence and low-level systems.
I am applying to PhD programs in Robotics for Fall 2027.
Research
* equal contribution
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Under review at ICRA 2027
TANDEM: Task and Motion Planning with As-Needed Demonstrations for Efficient Vision-Language-Action Model Fine-tuning
Combines task and motion planning with on-demand human teleoperation, so people only demonstrate the steps the planner can’t do.
- Website
- Paper · coming soon
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Under review at ICRA 2027
DATAFARM: Distribution-Aligned Task and Motion Planning for Fine-Tuning Vision-Language-Action Models
Aligns planner-generated demonstrations with a VLA’s pretraining distribution, making task and motion planning data nearly as useful as human teleoperation for fine-tuning.
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Under review at ICRA 2027
One Demonstration, Many Objects: Generalizing Manipulation via Local Contact Geometry
DemoMimic learns dexterous manipulation from a single human demonstration, with one policy per task that transfers across objects of different shape, scale, mass, and friction.
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IEEE International Conference on Blockchain · 2024
Scatter Protocol: An Incentivized and Trustless Protocol for Decentralized Federated Learning
A blockchain protocol for decentralized federated learning that uses staking, incentives, and penalties to deter malicious participants.
