Siddharth Karamcheti
Stanford, CA

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I am a PhD Student in computer science at Stanford University where I'm grateful to be co-advised by Percy Liang and Dorsa Sadigh. I am honored to be supported by the Open Philanthropy Project AI Fellowship.
    Research: I work on robot learning and natural language processing. My research focuses on two axes: Timeline: I recently completed a year-long part-time research internship at Hugging Face 🤗 working on multimodal pretraining with an incredible team of collaborators.
      Before Stanford, I was a resident at Facebook AI Research in New York, where I was lucky to work with Rob Fergus, Douwe Kiela, Jason Weston, and Arthur Szlam on grounded language understanding. Here's a short Q&A I did about my residency. Prior to that, I was lucky to do two summer internships at Bloomberg Research with wonderful mentors Gideon Mann and David Rosenberg.
        I completed my undergraduate degrees in computer science and literary arts at Brown University. While there, I did research in NLP & human-robot interaction advised by Stefanie Tellex and Eugene Charniak.




        Language-Driven Representation Learning for Robotics
        Siddharth Karamcheti, Suraj Nair, Annie S. Chen, Thomas Kollar, Chelsea Finn, Dorsa Sadigh, Percy Liang.
        Preprint, February 2023.
        [pdf] [code - models] [code - evaluation] [project page]

        “No, to the Right” – Online Language Corrections for Robotic Manipulation via Shared Autonomy
        Yuchen Cui*, Siddharth Karamcheti*, Raj Palleti, Nidhya Shivakumar, Percy Liang, Dorsa Sadigh.
        ACM/IEEE International Conference on Human Robot Interaction (HRI), March 2023.
        [pdf] [homepage] [code]


        Eliciting Compatible Demonstrations for Multi-Human Imitation Learning
        Kanishk Gandhi, Siddharth Karamcheti, Madeline Liao, Dorsa Sadigh.
        Conference on Robot Learning (CoRL), December 2022.
        [pdf] [homepage]

        What Makes Representation Learning from Videos Hard for Control?
        Tony Z. Zhao, Siddharth Karamcheti, Thomas Kollar, Chelsea Finn, Percy Liang.
        2nd Workshop on Scaling Robot Learning @ RSS 2022, June 2022.
        Best Paper Finalist

        Shared Autonomy for Robotic Manipulation with Language Corrections
        Siddharth Karamcheti*, Raj Palleti*, Yuchen Cui, Percy Liang, Dorsa Sadigh.
        Workshop on Learning with Natural Language Supervision (NL-Supervision) @ ACL 2022, May 2022.


        ELLA: Exploration through Learned Language Abstraction
        Suvir Mirchandani, Siddharth Karamcheti, Dorsa Sadigh.
        Conference on Neural Information Processing Systems (NeurIPS), December 2021.
        [pdf] [talk] [slides] [code]

        LILA: Language-Informed Latent Actions
        Siddharth Karamcheti*, Megha Srivastava*, Percy Liang, Dorsa Sadigh.
        Conference on Robot Learning (CoRL), November 2021.
        [pdf] [homepage] [code] [poster]

        On the Opportunities and Risks of Foundation Models
        Center for Research on Foundation Models (CRFM) – 100+ authors, directed by Percy Liang.
               - Robotics (§2.3): Siddharth Karamcheti (Lead), Annie Chen, Suvir Mirchandani, Suraj Nair, Krishnan Srinivasan, Kyle Hsu, Jeannette Bohg, Dorsa Sadigh, and Chelsea Finn.
               - Interaction (§2.5): Joon Sung Park, Chris Donahue, Mina Lee, Siddharth Karamcheti, Dorsa Sadigh, Michael Bernstein.
        [pdf] [homepage] [workshop] [press]

        Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering
        Siddharth Karamcheti, Ranjay Krishna, Li Fei-Fei, Christopher D. Manning.
        Annual Meeting of the Association for Computational Linguistics (ACL-IJCNLP), August 2021.
        Outstanding Paper Award
        [pdf] [talk] [slides] [code] [other coverage]

        Targeted Data Acquisition for Evolving Negotiation Agents
        Minae Kwon, Siddharth Karamcheti, Mariano-Florentino Cuéllar, Dorsa Sadigh.
        International Conference on Machine Learning (ICML), July 2021.
        [pdf] [talk] [slides]

        Learning Visually Guided Latent Actions for Assistive Teleoperation
        Siddharth Karamcheti, Albert J. Zhai, Dylan P. Losey, Dorsa Sadigh.
        Learning for Dynamics and Control (L4DC), June 2021.
        [pdf] [talk] [slides] [code] [poster]


        Learning Adaptive Language Interfaces through Decomposition
        Siddharth Karamcheti, Dorsa Sadigh, Percy Liang.
        Workshop for Interactive and Executable Semantic Parsing (IntEx-SemPar) @ EMNLP 2020, November 2020.
        [pdf] [slides]

        Generating Interactive Worlds with Text
        Angela Fan*, Jack Urbanek*, Pratik Ringshia, Emily Dinan, Emma Qian, Siddharth Karamcheti, Shrimai Prabhumoye, Douwe Kiela, Tim Rocktäschel, Arthur Szlam, and Jason Weston.
        Association for the Advancement of Artificial Intelligence (AAAI), February 2020
        [pdf] [dataset]


        Finding Generalizable Evidence by Learning to Convince Q&A Models
        Ethan Perez, Siddharth Karamcheti, Rob Fergus, Jason Weston, Douwe Kiela, and Kyunghyun Cho.
        Empirical Methods in Natural Language Processing (EMNLP), November 2019
        [pdf] [blog post] [code]

        Learning to Speak and Act in a Fantasy Text Adventure Game
        Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, and Jason Weston.
        Empirical Methods in Natural Language Processing (EMNLP), November 2019
        [pdf] [dataset]

        Improving Grey-Box Fuzzing by Modeling Program Control Flow
        Siddharth Karamcheti, Gideon Mann, and David Rosenberg
        Workshop on Machine Learning for Software Engineering (ML4SE), June 2019
        [pdf] [slides]

        Grounding Natural Language Instructions to Semantic Goal Representations for Abstraction and Generalization
        Dilip Arumugam*, Siddharth Karamcheti*, Nakul Gopalan, Edward C. Williams, Mina Rhee, Lawson L.S. Wong, and Stefanie Tellex
        Autonomous Robots (AuRO), February 2019
        [free-to-read pdf]


        Adaptive Grey-Box Fuzz Testing with Thompson Sampling
        Siddharth Karamcheti, Gideon Mann, and David Rosenberg
        11th ACM Workshop on Artificial Intelligence and Security (AISEC), October 2018
        [pdf] [slides]


        Modeling Latent Attention within Neural Networks
        Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L.S. Wong, and Michael Littman

        A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions
        Siddharth Karamcheti, Edward C. Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L.S. Wong, and Stefanie Tellex
        1st Workshop in Language Grounding for Robotics (RoboNLP) @ ACL, August 2017
        Best Paper Award

        Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities
        Dilip Arumugam*, Siddharth Karamcheti*, Nakul Gopalan, Lawson L.S. Wong, and Stefanie Tellex
        Robotics: Science and Systems (RSS), June 2017

        Blog Posts

        The Annotated S4 – Efficiently Modeling Long Sequences with Structured State Spaces
        Sasha Rush and Siddharth Karamcheti – January, 2022.
        [ICLR blog track] [code] [original paper]

        Mistral – A Journey towards Reproducible Language Model Training
        Siddharth Karamcheti* and Laurel Orr* – August, 2021.
        Team: Jason Bolton, Tianyi Zhang, Karan Goel, Avanika Narayan, Rishi Bommasani, Deepak Narayanan
        Advisors: Tatsunori Hashimoto, Dan Jurafsky, Christopher D. Manning, Christopher Potts, Christopher Ré, Percy Liang
        [code] [checkpoints] [talk]