9 accepted papers
Video data provides a rich source beyond expensive action-labeled data for advancing robot learning. Recent approaches have demonstrated promising potential in leveraging video data by learning latent actions for policy training. The latent action tokenizer encodes latent actions between successive
Learning from demonstration is a powerful method for robotic skill acquisition. However, the significant expense of collecting such action-labeled robot data presents a major bottleneck. Video data, a rich data source encompassing diverse behavioral and physical knowledge, emerges as a promising alt…
While autoregressive models demonstrate remarkable success in text and image generation, their application to robot policies suffers from weak holistic comprehension, cumulative errors, and limited multi-modal modeling capabilities, particularly in long-horizon tasks or multi-modal scenarios. This p
In various robotic applications, understanding accurate object poses for robots is essential for high-precision tasks such as factory assembly or daily insertions. Tactile sensing, which compensates for visual information, offers rich texture-based or force-based data for object pose estimation. How
Learning from human demonstrations is an emerging trend for designing intelligent robotic systems. However, previous methods typically regard videos as instructions, simply dividing videos into action sequences for robotic repetition, which pose obstacles to generalization to diverse tasks or object
Image feature matching is a fundamental task in computer vision. Existing local feature matching methods can establish robust correspondences between image pairs. However, these methods heavily rely on dense local image features, making them susceptible to significant perspective differences, charac
Reliable semantic segmentation of open environments is essential for intelligent systems, yet significant problems remain: 1) Existing RGB-T semantic segmentation models mainly rely on low-level visual features and lack high-level textual information, which struggle with accurate segmentation when c
Collaborative perception facilitates a more comprehensive representation of the environment by leveraging complementary information shared among various agents and sensors. However, practical applications often encounter information disturbance which includes perception packet loss and time delays,…
Visual imitation learning (VIL) provides an efficient and intuitive strategy for robotic systems to acquire novel skills. Recent advancements in Vision Language Models (VLMs) have demonstrated remarkable performance in vision and language reasoning capabilities for VIL tasks. Despite the progress, c…