5 accepted papers
Currently, many manipulation tasks for deformable objects focus on activities like folding clothes, handling ropes, and manipulating bags. However, research on contact-rich tasks involving deformable objects remains relatively underdeveloped. When humans use cloth or sponges to wipe surfaces, they r…
Offline reinforcement learning (RL) has shown great potential in many robotic tasks, where doing trial-and-error with the environment is risky, costly, or time-consuming. However, it is still hard to succeed in long-horizon tasks especially when given suboptimal and multimodal offline datasets. Neve
Offline reinforcement learning (RL) provides a promising approach to avoid costly online interaction with the real environment. However, the performance of offline RL highly depends on the quality of the datasets, which may cause extrapolation error in the learning process. In many robotic applicati…
Active voltage control presents a promising avenue for relieving power congestion and enhancing voltage quality, taking advantage of the distributed controllable generators in the power network, such as roof-top photovoltaics. While Multi-Agent Reinforcement Learning (MARL) has emerged as a compelli…
Reinforcement learning (RL) has emerged as a promising approach for optimizing traffic signal control (TSC) to ensure the efficient operation of transportation networks. However, the traditional trial-and-error technique in RL is usually impractical in real-world applications. Offline RL, which trai…