Gaoliang Peng

About

Dr. Gaoliang Peng is a leading researcher in human-robot collaboration and reconfigurable mobile robotics, with a career spanning foundational work in mechanical design to cutting-edge AI-driven prediction systems. His primary research areas include human action prediction for collaborative assembly, robot climbing and locomotion, and the optimization of reconfigurable mobile robots. Dr. Peng’s most impactful contribution is his 2022 work on "Prediction-Based Human-Robot Collaboration in Assembly Tasks Using a Learning from Demonstration Model," which has garnered 34 citations and addresses a critical need in small-to-medium enterprises by enabling robots to fluidly work alongside human counterparts. He also developed innovative "Grappling claws for a robot to climb rough wall surfaces" (30 citations), demonstrating his versatility in mechanical design and grasping algorithms. His earlier work on mobile robot optimization using Harmony Search methods (2009) laid the groundwork for adaptive, terrain-aware robotics. More recently, he has explored nonlinear stiffness in composite origami metamaterials (2023) and deep reinforcement learning for dimension-variable navigation (2025). With over 90 total citations across his top papers, Dr. Peng’s research is shaping the future of flexible, intelligent robotic systems that can adapt to both human partners and challenging physical environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Prediction-Based Human-Robot Collaboration in Assembly Tasks Using a Learning from Demonstration Model

   34 citations · 2022
2. 2
   Grappling claws for a robot to climb rough wall surfaces: Mechanical design, grasping algorithm, and experiments

   30 citations · 2020
3. 3
   Nonlinear stiffness analysis and programming of a composite origami metamaterial with embedded joint-type metastructures

   12 citations · 2023
4. 4
   Optimization of mobile robot based on projection method and Harmony Search

   7 citations · 2009
5. 5
   Prediction of Human Actions in Assembly Process by a Spatial-Temporal End-to-End Learning Model

   5 citations · 2019
6. 6
   Dimension-Variable Mapless Navigation With Deep Reinforcement Learning

   2 citations · 2025
7. 7
   Prototype optimization of reconfigurable mobile robots based on a modified Harmony Search method

   2 citations · 2010
8. 8
   Trade-offs design of mobile robot based on Multi-Objective Optimization with respect to Terramechanics

   2 citations · 2009
9. 9
   An innovative reconfigurable mobile robot with multi-maneuver modes

   2 citations · 2009

Key Collaborators

Contact & Links