About

Abhishek Sarkar is a roboticist whose work spans humanoid locomotion, soft robotics, and reinforcement learning. His most influential contribution, a 2017 paper on deep reinforcement learning for dynamically stable inverse kinematics of humanoid robots (58 citations), pioneered a real-time method for generating joint-space trajectories that maintain balance—a critical challenge for bipedal systems. Sarkar’s research on compliant biped robots, including an 8-DoF design with compliant links (22 citations) and optimal trajectory generation for inclined ground (19 citations), has advanced stable walking on varied terrains. He has also contributed to specialized systems, such as a modular OmniCrawler in-pipe climbing robot (9 citations) and bio-inspired soft robotic grippers (9 citations), demonstrating versatility across rigid and soft platforms. His work on modified FABRIK for manipulators and multi-task reinforcement learning with shared actions further highlights his focus on practical, efficient control. With over 140 total citations, Sarkar’s research integrates theoretical innovation with real-world deployment, offering foundational insights for students and researchers in humanoid robotics, compliant mechanisms, and learning-based control.

Research Focus

Key Achievements

6
H-Index
17
Papers
158
Total Citations
9
Avg Citations/Paper
🏆 Most Cited Paper
A deep reinforcement learning approach for dynamically stable inverse kinematics of humanoid robots
58 citations · 2017
📈 Most Prolific Year: 2018 (4 Papers)
🤝 Key Collaborators: 24
🏛 Institutions: Indian Institute of Technology Hyderabad, Indian Institute of Technology Kanpur, Robotics Research (United States), International Institute of Information Technology, Hyderabad, KIIT University, Birla Institute of Technology and Science, Pilani

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago