5 papers
Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning
Arthicha Srisuchinnawong, Poramate Manoonpong
Existing robot locomotion learning techniques rely heavily on the offline selection of proper reward weighting gains and cannot guarantee constraint satisfaction (i.e., constraint…
Growable and Interpretable Neural Control with Online Continual Learning for Autonomous Lifelong Locomotion Learning Machines
Arthicha Srisuchinnawong, Poramate Manoonpong
Continual locomotion learning faces four challenges: incomprehensibility, sample inefficiency, lack of knowledge exploitation, and catastrophic forgetting. Thus, this work introduc…
Bio-Inspired Plastic Neural Networks for Zero-Shot Out-of-Distribution Generalization in Complex Animal-Inspired Robots
Binggwong Leung, Worasuchad Haomachai, Joachim Winther Pedersen +2
Artificial neural networks can be used to solve a variety of robotic tasks. However, they risk failing catastrophically when faced with out-of-distribution (OOD) situations. Severa…
An Interpretable Neural Control Network with Adaptable Online Learning for Sample Efficient Robot Locomotion Learning
Arthicha Srisuchinnawong, Poramate Manoonpong
Robot locomotion learning using reinforcement learning suffers from training sample inefficiency and exhibits the non-understandable/black-box nature. Thus, this work presents a no…
Nature's All-in-One: Multitasking Robots Inspired by Dung Beetles
Binggwong Leung, Stanislav Gorb, Poramate Manoonpong
Dung beetles impressively coordinate their six legs simultaneously to effectively roll large dung balls. They are also capable of rolling dung balls varying in the weight on differ…