Nikita Sarawgi

I am a robotics and AI engineer at GrayMatter Robotics, working on active perception and decision-making for robots in semi-structured environments for high-mix manufacturing.

I completed my M.S. in Computer Science at USC Viterbi in December 2025, advised by Satyandra K. Gupta and Daniel Seita.

I am interested in how embodied agents can learn and use knowledge in ways that support generalization, reasoning, planning, and robustness in complex physical environments. My research interests span perception, manipulation, and decision-making, particularly questions about what knowledge robotic systems need, how that knowledge should be represented, and how it can support effective behavior across new environments, tasks, and forms of interaction.

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profile photo

Updates
May 2026 I'll be attending ICRA 2026 in Vienna this June. Please reach out if you are around and would like to connect!
February 2026 I joined GrayMatter Robotics full-time as a robotics and AI engineer.
January 2026 Our paper, STEP, was accepted to ICRA 2026. Hope to see you in Vienna!
December 2025 I completed my M.S. in Computer Science from USC Viterbi.
October 2025 I joined GrayMatter Robotics as a robotics intern, working on translating learning-based manipulation research into deployable perception and control systems for high-mix manufacturing.

Publications
binpacking Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing
Nikita Sarawgi, Omey M. Manyar, Fan Wang, Thinh H. Nguyen, Daniel Seita, Satyandra K. Gupta
ICRA, 2026
project page / arXiv / code

An RL policy for online 3D bin packing that chooses both the next item to pack and the face to grasp it on. A preference parameter set at inference time trades off packing density against operation time without retraining.

space-insertion Autonomous Execution of Insertion Operations in Space Assembly Tasks
Abhay Negi, Nikita Sarawgi, Dhanush Penmetsa, Hantao Ye, Omey M. Manyar, Ashtin Cheng, Satyandra K. Gupta
AIAA SciTech Forum (ISAM IV), 2025
paper

An autonomous insertion system for in-space assembly. The robot first estimates a coarse pose to position a wrist-mounted camera near the target, then uses a learned model to estimate fine misalignment, and executes the insertion under impedance control.

Academic Service
  • Reviewer: RA-L, Humanoids 2026, and ICRA 2026.

Website adapted from Jon Barron's website.