Papers
11
Total Citations
95
H-Index
6
About
Liding Zhang is an emerging robotics researcher whose work spans motion planning, tactile manipulation, and safe autonomy for robotic systems. His most significant contribution lies in advancing sampling-based motion planning algorithms, as evidenced by his highly cited 2025 review paper on the topic (41 citations), which has rapidly become a key reference for researchers navigating this complex field. Zhang has developed several novel planners — including FDIT*, APT*, and adaptive informed search strategies — that address critical challenges in high-dimensional pathfinding, combining efficiency, optimality, and obstacle-awareness in innovative ways. Beyond planning theory, Zhang's research extends into tactile manipulation and force-domain learning, with TacDiffusion demonstrating how diffusion models can enable precise robotic assembly. His work on safety-critical control for mobile robots in dynamic environments and user-guided adaptive planning frameworks reflects a commitment to real-world applicability. Further contributions to deformable object manipulation and non-prehensile transportation highlight his broad technical range. With a publication record concentrated primarily in 2024–2025 and already accumulating nearly 100 citations across ten papers, Zhang represents a rapidly rising voice in robotics research, blending algorithmic rigor with practical robotic intelligence.
Research Focus
Key Achievements
Top Papers
- 1Motion planning for robotics: A review for sampling-based planners41 citations · 2025
- 2TacDiffusion: Force-Domain Diffusion Policy for Precise Tactile Manipulation10 citations · 2025
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10Trajectory Planning for Non-Prehensile Object Transportation1 citations · 2024