Papers
13
Total Citations
451
H-Index
10
About
Kunpeng Yao is a robotics researcher whose work spans tactile sensing, robotic manipulation, and the emerging intersection of large language models (LLMs) with autonomous planning. His most influential contributions lie in tactile-based robotic frameworks, where he pioneered methods enabling robots to detect slips, regulate grasping forces, and manipulate deformable objects with dynamic centers of mass — work that has garnered over 80 citations. Yao further advanced the field by developing probabilistic frameworks for active object exploration and discrimination using multimodal robotic skin, accumulating over 130 combined citations across related studies. His research on bimanual manipulation of semi-deformable objects, inspired by real-world applications in watchmaking and automotive assembly, established valuable benchmarking protocols for the community. More recently, Yao has turned his attention to LLM-driven task planning, with his ISR-LLM framework for long-horizon sequential planning attracting 56 citations since 2024, signaling a productive pivot toward foundation model integration in robotics. His survey on transfer learning in robotics and work on adaptive action contextualization further demonstrate his commitment to building truly generalizable, intelligent robotic systems — making him a compelling figure at the frontier of embodied AI research.
Research Focus
Key Achievements
Top Papers
- 1Tactile-based manipulation of deformable objects with dynamic center of mass83 citations · 2016
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- 5Benchmark for Bimanual Robotic Manipulation of Semi-Deformable Objects37 citations · 2020
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- 7Exploiting Kinematic Redundancy for Robotic Grasping of Multiple Objects33 citations · 2023
- 8Tactile-based object center of mass exploration and discrimination33 citations · 2017
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