• GEneric NEural control for Self-organized emergent behavIor of limbed Systems (GENESIS, Smart motor control software)
  • Flexible, Soft Object Manipulation
  • Online Neural Learning and Adaptation
  • Bio-inspired Robot Structure Design with Hybrid Rigid-Soft Material


Research Highlights


Neural control for autonomous climbing and obstacle avoidance of a gecko-inspired climbing robot on steep slopes.

For more details, see Srisuchinnawong et al., Journal of Intelligent & Robotic Systems, 2021 [Video1, Video2]






Autonomous online adaptation of a walking robot under bioinspired adaptive locomotion control. 

For more details, see Ngamkajornwiwat  et al., IEEE Access, 2020 [Video1, Video2]





Novel hybrid soft-rigid foot with dry adhesive material for a gecko-inspired climbing robot. 

For more details, see Shao et al., IEEE RoboSoft, 2020 [Video]






Small-sized and lightweight quadruped robot serving as a generic robot platform for research and education in the fields of robot locomotion, bionic control, and machine learning.

For more details, see Sun et al., Front. Neurorobot., 2020







Novel fast online learning based on error feedback for adaptive robot motor control. 

For more details, see Thor and Manoonpong, IEEE Transactions on Neural Networks and Learning Systems, 2019






Exploiting neural dynamics of a reservoir computing-based recurrent neural network and haptic feedback to classify multiple terrains. 

For more details, see Borijindakul et al., Lecture Notes in Computer Science,  2019






A bio-inspired climbing robot with flexible pads and claws that can climb on rough walls.

For more details, see Ji et al., J Bionic Eng, 2018





Adaptive neural control for self-organized locomotion and obstacle negotiation of quadruped robots.

For more details, see Sun et al., IEEE International Symposium on Robot and Human Interactive Communication, 2018 [Video1, Video2]