Harshit Sikchi
Research Scientist, OpenAI
hsikchi[at]utexas.edu
News
- 05/09/2025: Fast Adaptation with Behavioral Foundation Models, work from FAIR internship, accepted at RLC 2025.
- 05/02/2025: Proto Successor Measure (Unsupervised RL) accepted at ICML 2025.
- 04/11/2025: CRESTE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance accepted at RSS 2025.
- 01/22/2025: Iterative Dual RL accepted at ICLR 2025.
- 08/01/2024: Research Intern at Meta FAIR, Paris working on Unsupervised RL
- 09/25/2024: Our Scaling laws study of Direct Alignment Algorithms for RLHF is accepted at NeurIPS 2024.
- 09/04/2024: DILO is accepted at CoRL 2024.
- 01/16/2024: Dual-RL, CPL and SMoRe accepted in ICLR 2024.
- 05/01/2023: I'll be starting as a research intern at Meta AI research working on RL.
- 02/16/2023: Our recent work on Dual RL is now public. Check it our for SOTA algos in RL and IL .
- 01/09/2023: rank-game (Unified approach to learning from preferences and imitations) was featured in the Microsoft Research Blog.
- 10/23/2022: Our work FlowPlan awarded best paper at IROS BADUE 2022.
- 03/10/2022: I'll be starting as a research intern at NVIDIA research working on reinforcement learning.
- 11/07/2021: Our work on model-based RL LOOP nominated for Best Paper at CoRL 2021.
About
I am a PhD student in the Computer Science Department at UT Austin co-advised by Prof. Scott Niekum and Prof. Amy Zhang . I am interested in pushing the limits of Interactive Agent Learning: enabling agents to make the most of limited data and make sense of different sources of information present in the world to improve their ability. I am broadly interested in Reinforcement Learning (Theory and Practice) to achieve this goal.
Previously, I was a Master’s student in Computer Science
(2019-20) at the
School of Computer Science, Carnegie Mellon University where I worked at Robot Perceiving and Doing lab advised by Prof. David Held. In the summer of 2020,
I worked
on Imitative Motion Planning at Uber ATG . I
worked on
Reinforcement Learning for large action spaces during my prior internship at NVIDIA and spent some time at ETH Zurich working on Semantic Segmentation.
Prior to this, I received my Bachelor’s degree from the Department of Computer Science at the Indian
Institute of
Technology, Kharagpur. My studies at IIT Kharagpur were supported by the Aditya Birla Scholarship (2015-19).
I spent most of time at IIT Kharagpur
working on
Autonomous cars at the Autonomous Ground Vehicle
Lab under the
supervision of Professor Debashis Chakravarty. I led the perception and planning effort-working on
Lane Detection,
Frenet Planner, Hybrid A* Planner, and Segmentation. I completed my bachelor thesis on Safe Reinforcement
Learning with
Prof. Pabitra Mitra. In my spare
time, I enjoy playing tennis, badminton, skiing, running, hiking, and traveling.