Papers
9
Total Citations
124
H-Index
7
About
Rohan Singh is a robotics researcher whose work sits at the intersection of humanoid locomotion, deep reinforcement learning, and teleoperation systems. He has made significant contributions to advancing bipedal walking in humanoid robots, tackling one of the field's most persistent challenges: transferring simulation-trained controllers to real hardware. His highly cited papers — "Learning Bipedal Walking for Humanoids With Current Feedback" (32 citations) and "Learning Bipedal Walking on Planned Footsteps for Humanoid Robots" (24 citations) — demonstrate a systematic approach to sim-to-real transfer, enabling robust locomotion across diverse and unpredictable terrains. His 2024 work further extends this to compliant and uneven surfaces, reflecting a commitment to real-world deployment readiness. Beyond locomotion, Singh has contributed meaningfully to humanoid teleoperation, exploring immersive visual feedback systems using SLAM and cybernetic avatar frameworks, accumulating over 30 citations across these efforts. His development of MC-MuJoCo (15 citations) has provided the community with a valuable simulation tool for testing FSM-based robot controllers. With a research portfolio spanning legged locomotion dynamics, tactile sensing, and visual SLAM, Singh represents a versatile and impactful voice in modern humanoid robotics research.
Research Focus
Key Achievements
Top Papers
- 1Learning Bipedal Walking for Humanoids With Current Feedback32 citations · 2023
- 2Learning Bipedal Walking On Planned Footsteps For Humanoid Robots24 citations · 2022
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- 5Mc-Mujoco: Simulating Articulated Robots with FSM Controllers in MuJoCo15 citations · 2023
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- 9Learning to Classify Surface Roughness Using Tactile Force Sensors3 citations · 2024