Yunfeng Zhang
Papers
9
Total Citations
230
H-Index
5
About
Yunfeng Zhang is a robotics researcher whose work spans two dynamic and complementary fields: soft robotics and autonomous robot navigation. His most influential contribution, "Topology Optimized Design, Fabrication, and Characterization of a Soft Cable-Driven Gripper" (2018), has accumulated 144 citations and demonstrates his pioneering approach to automatically designing compliant robotic systems capable of operating in unstructured environments. Alongside this, his work on programmable dielectric elastomer actuators further establishes his expertise in designing sophisticated soft robotic mechanisms capable of large-scale deformation. Beyond soft robotics, Zhang has made substantial contributions to deep reinforcement learning (DRL)-based mapless robot navigation. His IPAPRec framework (2022, 22 citations) addresses critical challenges in generalizing navigation agents across diverse scenarios, while his danger-aware adaptive composition approaches reflect a sustained effort to enhance collision avoidance and reliability in autonomous systems. Notably, his research on dimension-variable navigation agents tackles the practical challenge of deploying trained controllers across robots of varying physical configurations — a significant step toward scalable autonomy. Collectively, Zhang's body of work bridges physical robot design and intelligent control, offering meaningful advancements to the robotics community with a cumulative impact of over 230 citations.
Research Focus
Key Achievements
Top Papers
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- 4Danger-Aware Adaptive Composition of DRL Agents for Self-Navigation16 citations · 2020
- 5Behavior Switch for DRL-based Robot Navigation11 citations · 2019
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- 8Danger-aware Adaptive Composition of DRL Agents for Self-navigation3 citations · 2018
- 9Dimension-Variable Mapless Navigation With Deep Reinforcement Learning2 citations · 2025