3 accepted papers
Pneumatic soft robots offer outstanding flexibility and safety in human-robot interactions. However, achieving full integration requires air sources that are not only highly flexible but also lightweight. Existing soft air pumps are often constrained by structural limitations and inadequate output p
In recent years, Contrastive learning has shown great potential in traffic flow prediction tasks. However, existing contrastive learning methods have difficulties in dealing with missing data and noise, and it is difficult to fully capture local and global correlations by relying on a single contras
Pneumatic soft robots are becoming a research hotspot because to their high flexibility, compliance, and adaptability, and they typically use external air pumps as their air supply. However, being the “heart” of pneumatic soft robots, these air pumps are extremely large and heavy, inevitably limitin