Shivakanth

Deep RL Researcher at ARAYA. Mila alum.

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Hello to my small corner of the internet!

Update: Looking for full-time research engineer and research scientist roles in 2026!

I’m a Deep RL Researcher at ARAYA in Tokyo, where I build post-training and deployment systems for vision-language-action models in assistive robotics. My work sits between embodied AI research and systems engineering: I develop reinforcement-learning methods, then profile and optimize the training and inference stack so they can run on real robots.

I completed a Research MSc in Computer Science at Mila, supervised by Prof. Samira Ebrahimi Kahou, with a thesis on sample-efficient reinforcement learning for real-world systems. I was also a research intern with John Langford at Microsoft Research NYC, working on reachability-aware representations for efficient planning.

My research spans offline and online RL, embodied foundation models, representation learning, and human-robot interaction. I completed my undergraduate degree in Instrumentation and Control Engineering at NIT Trichy. Feel free to reach out if you’d like to talk about reinforcement learning, robotics, or making research systems fast enough to be useful.

You can read my CV online or download the PDF.

news

Jul 29, 2026 Recent result: standard offline actor-critic post-training took a billion-scale flow-matching VLA from a 4% behavior-cloning floor to 94% task success in simulation—without a flow-specific actor objective.
Jan 15, 2026 Our study of shared autonomy in multi-user brain-robot interfaces was published in Frontiers in Human Neuroscience! 📣
Sep 25, 2025 Our M4Bench paper—a multi-user, multi-robot, multi-goal, multi-device HRI benchmark—was published in Frontiers in Robotics and AI! 📣
Jan 24, 2025 Presented our work on augmenting low-cost GELLO teleoperation with force feedback at SII 2025! 🤖
Jul 08, 2024 Our paper “PcLast: Discovering Plannable Continuous Latent States” was published at ICML 2024! 📣

selected publications

  1. NeurIPS
    Learning Robust Dynamics through Variational Sparse Gating
    Arnav Kumar Jain, Shivakanth Sujit, Shruti Joshi, and 3 more authors
    Advances in Neural Information Processing Systems, 2022
  2. NeurIPS
    Prioritizing Samples in Reinforcement Learning with Reducible Loss
    Shivakanth Sujit, Somjit Nath, Pedro Braga, and 1 more author
    Advances in Neural Information Processing Systems, 2023
  3. TMLR
    Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
    Shivakanth Sujit, Pedro Braga, Jorg Bornschein, and 1 more author
    Transactions on Machine Learning Research, 2023
  4. ICML
    PcLast: Discovering Plannable Continuous Latent States
    Anurag Koul, Shivakanth Sujit, Shaoru Chen, and 11 more authors
    In Proceedings of the 41st International Conference on Machine Learning, 2024
    Anurag Koul and Shivakanth Sujit contributed equally.
  5. SII
    Improving Low-Cost Teleoperation: Augmenting GELLO with Force
    Shivakanth Sujit, Luca Nunziante, Dan Ogawa Lillrank, and 2 more authors
    In 2025 IEEE/SICE International Symposium on System Integration, 2025
  6. Frontiers
    A multi-user multi-robot multi-goal multi-device human-robot interaction manipulation benchmark
    Akito Yoshida, Rousslan Fernand Julien Dossa, Marina Di Vincenzo, and 3 more authors
    Frontiers in Robotics and AI, 2025
  7. Frontiers
    Levels of shared autonomy in brain-robot interfaces: enabling multi-robot multi-human collaboration for activities of daily living
    Hannah Douglas, Marina Di Vincenzo, Rousslan Fernand Julien Dossa, and 3 more authors
    Frontiers in Human Neuroscience, 2026