Rishabh Jangir
San Diego, California, United States
Summary
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SeniorRishabh Jangir is a Founding Robotics Engineer with a decade of hands-on experience building end-to-end deep learning and vision-based control systems for autonomous agents. He combines academic rigor—MS in Intelligent Systems from UC San Diego and publications at ICRA, ICLR, and RA-L—with production experience from warehouse automation at Nimble Robotics, where he developed sim2real pipelines, high-fidelity Mujoco simulations, and multi-robot systems in C++. A pragmatic problem solver who believes in failing fast, he repeatedly ships complete projects from prototyping to real-robot deployment, including novel representation learning and RL approaches for dynamic bin packing and cloth manipulation. Now at Skild AI, he continues to bridge research and productization, applying expertise in Deep RL, simulation, and perception to industrial robotics challenges. An underappreciated strength is his fluency across the stack—C++, Python, simulators, and real hardware—which lets him iterate quickly between algorithm and system.
10 years of coding experience
Github Skills (42)
algorithm10
artificial-intelligence10
robotics10
python10
openai-gym10
reinforcement-learning10
pymunk10
pygame10
actor-critic10
ros10
py9
openai9
simulation-framework8
algorithms8
mujoco7
Programming languages (4)
Github contributions (5)
jangirrishabh/toyCarIRL
Jun 2016 - Oct 2021
Implementation of Inverse Reinforcement Learning Algorithm on a toy car in a 2D world problem, (Apprenticeship Learning via Inverse Reinforcement Learning Abbeel & Ng, 2004)
Contributions:29 commits, 12 pushes, 2 branches in 5 years 4 months
reinforcement-learninginverse-reinforcement-learningapprenticeship-learningpymunkpygame
jangirrishabh/Overcoming-exploration-from-demos
Jun 2018 - Oct 2021
Implementation of the paper "Overcoming Exploration in Reinforcement Learning with Demonstrations" Nair et al. over the HER baselines from OpenAI
Contributions:59 commits, 3 PRs, 41 pushes in 3 years 5 months
openaireinforcement-learningreinforcement-learning-agentlearning-from-demonstrationrobotics
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