Rishabh Jangir
Founding Robotics Engineer at Skild AI
Pittsburgh, Pennsylvania, United States
Summary
👤
Senior🎓
Top SchoolRishabh 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
5 years of employment as a software developer
Master of Science - MS Intelligent Systems Robotics and Control, Master of Science - MS Intelligent Systems Robotics and Control at UC San Diego Jacobs School of EngineeringBachelor of Technology (B.Tech.) Engineering Physics/Applied Physics, Bachelor of Technology (B.Tech.) Engineering Physics/Applied Physics at Indian Institute of Technology, Guwahati
Hindi, English, Spanish
Stackoverflow
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Github Skills (64)
webots10
inverse10
gazebosim10
openai-gym10
reinforcement-learning10
robot10
deep-reinforcement-learning10
gazebo-simulator10
robotics9
benchmark9
simulation9
simulation-framework9
robot-simulator9
mujoco9
autonomous-robots9
Programming languages (4)
Github contributions (5)
jangirrishabh/Overcoming-exploration-from-demos
Jun 2018 - Oct 2021
Implementation on 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
pybulletreinforcement-learningdeep-reinforcement-learningopenaibaselines
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
toyreinforcement-learningdeep-reinforcement-learninginverse-reinforcement-learninglearning-algorithm
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