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About

Mayank Mittal is a robotics researcher whose work spans robot learning, legged locomotion, manipulation, and surgical simulation. His research is unified by a drive to bridge the gap between simulation and real-world deployment, developing frameworks and algorithms that enable robots to acquire complex, physically grounded skills. Among his most notable contributions is Orbit-Surgical (2024, 27 citations), a physics-based simulation framework designed to accelerate learning for surgical robots — a domain where robust simulation has historically lagged behind other fields. His work on Pedipulate (2024, 26 citations) creatively repurposes a quadruped's own leg as a manipulation tool, reducing hardware complexity while expanding robot capability. In ViPlanner (2024, 26 citations), he tackled real-time outdoor navigation using visual-semantic learning, pushing beyond purely geometric approaches. His earlier transfer learning work on dexterous TriFinger manipulation (2021, 15 citations) demonstrated effective sim-to-real transfer using NVIDIA's IsaacGym, and his research on symmetry in reinforcement learning (2024, 15 citations) addresses a fundamental challenge in learning natural, artifact-free robot behaviors. Collectively, Mittal's contributions reflect a sophisticated, systems-level approach to making robots more capable, generalizable, and deployable across demanding real-world scenarios.