4 accepted papers
Existing robot locomotion learning techniques rely heavily on the offline selection of proper reward weighting gains and cannot guarantee constraint satisfaction (i.e., constraint violation) during training. Thus, this work aims to address both issues by proposing Reward-Oriented Gains via Embodied…
While different control approaches have been developed for smooth and safe navigation, they are limited by the needs for model-based assumptions, true training target/reward function, and/or large sample data. To overcome these limitations, this study proposes a model-free neural control architectur…
Adaptive systems enable legged robots to cope with a wide range of environmental settings and unforeseen events. Existing reactive methods adapt either the walking frequency or the amplitude to only simple perturbations. This letter proposes an adaptive mechanism for central pattern generator (CPG)-