Papers
12
Total Citations
141
H-Index
6
About
Utkarsh A. Mishra is a robotics researcher whose work spans cable-driven parallel robots (CDPRs), bipedal locomotion, and robot manipulation, establishing him as a versatile contributor to modern robotics. His foundational contributions to CDPR kinematics include pioneering neural network-based approaches for solving forward kinematics in under-actuated, elastically-cabled systems — work that has garnered over 40 citations and addresses longstanding computational challenges in real-time robot control. Complementing this, his AFG-RRT path planning framework advanced motion planning for CDPRs by simultaneously optimizing wrench capability and dexterity. Mishra has also made significant strides in legged robotics, demonstrating that surprisingly simple linear policies are sufficient for robust bipedal walking on challenging and sloped terrains — a counterintuitive yet impactful finding cited nearly 30 times. More recently, he has ventured into generative AI for robotics, applying diffusion models to object reorientation and long-horizon skill chaining for manipulation tasks, signaling a forward-looking research trajectory. With over 135 total citations across a focused body of work, Mishra's research consistently bridges theoretical rigor with practical deployability, making his contributions particularly valuable for students and practitioners working at the intersection of robot kinematics, learning-based control, and intelligent manipulation.
Research Focus
Key Achievements
Top Papers
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- 4ReorientDiff: Diffusion Model based Reorientation for Object Manipulation13 citations · 2024
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