3 accepted papers
The human nervous system exhibits bilateral symmetry, enabling coordinated and balanced movements. However, existing Deep Reinforcement Learning (DRL) methods for humanoid robots neglect morphological symmetry of the robot, leading to uncoordinated and suboptimal behaviors. Inspired by human motor c
Learning policies for complex humanoid tasks remains both challenging and compelling. Inspired by how infants and athletes rely on external support—such as parental walkers or coach-applied guidance—to acquire skills like walking, dancing, and performing acrobatic flips, we propose A2CF: Adaptive As…
Assessing physical properties of the environment with vision helps humans to respond appropriately before entering risky areas. However, equipping robots with such perceptual ability is challenging due to the lack of labeled data. To overcome this challenge, we present the Active Exploration and Onl