Siddhant Gangapurwala
Papers
8
Total Citations
182
H-Index
4
About
Siddhant Gangapurwala is a robotics researcher specializing in legged locomotion, reinforcement learning, and optimal control, with a particular focus on enabling quadrupedal robots to navigate complex, unstructured environments. His work sits at the intersection of model-based and data-driven approaches, seeking to combine the reliability of classical control with the adaptability of machine learning. His most influential contribution, "RLOC: Terrain-Aware Legged Locomotion Using Reinforcement Learning and Optimal Control" (2022, 122 citations), introduced a unified framework that leverages proprioceptive and exteroceptive sensing to generate dynamic footstep plans for quadrupeds traversing uneven terrain — a landmark result in the field. Complementing this, his research on real-time trajectory adaptation and low-frequency motion control challenges conventional assumptions, demonstrating that robust locomotion is achievable even at control rates as low as 8 Hz on real hardware such as the ANYmal C robot. His work on disentangled gait representations through VAE-Loco further advances versatile, continuously variable locomotion behaviors. Across his career, Gangapurwala has consistently pushed legged robotics from simulation toward practical real-world deployment, accumulating over 180 citations and establishing himself as a thoughtful contributor to next-generation autonomous mobile robot systems.
Research Focus
Key Achievements
Top Papers
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- 8Rapid Stability Margin Estimation for Contact-Rich Locomotion3 citations · 2021