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tayalmanan28/README.md

Manan Tayal

Research Scientist at Emergence AI, working on reinforcement learning with verifiable rewards for agentic systems.

I build certificates and safety filters that let you deploy a learned policy without having to trust it: reachability and barrier certificates, offline safe RL, and safety for VLA and agentic systems. Published at ICML, TMLR, RLC, CDC and ACC.

PhD from the Centre for Cyber Physical Systems, IISc Bangalore, advised by Prof. Shishir N. Y. Kolathaya and Prof. Pushpak Jagtap. B.Tech from IIT Bombay.

Recent work with code

  • V-OCBF (TMLR 2026) — learning safety filters from offline data
  • Safe Flow Q-Learning (RLC 2026) — offline safe RL with flow policies
  • PIML-SOC (ICML 2025) — physics-informed safe and optimal control
  • Vision CBF (CDC 2025) — Vision Based CBF for autonomous systems using World Models

Website · Scholar · YouTube · LinkedIn · X · Medium

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  1. MuJoCo-Tutorial MuJoCo-Tutorial Public

    Tutorial on how to get started with MuJoCo Simulation Platform. MuJoCo stands for Multi-Joint dynamics with Contact.

    Jupyter Notebook 371 42

  2. Safe_Reinforcement_Learning Safe_Reinforcement_Learning Public

    Repository containing the code for safe reinforcement learning in two custom environments

    Python 46 9

  3. piml-soc piml-soc Public

    [ICML 2025] A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems

    Python 8

  4. safe-fql safe-fql Public

    [RLC'26] Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies

    Python 3 1

  5. v-ocbf v-ocbf Public

    [TMLR'26] V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functions

    Python 2 1

  6. tau-intelligence/MuJoCo-drones-gym tau-intelligence/MuJoCo-drones-gym Public

    Multi-drone environment for RL with MuJoCo, with GPU vectorization, wind models, domain randomization, and curriculum learning

    Python 48 14