7 accepted papers
Multimodal data is indispensable for advancing imitation learning, particularly in the context of dexterous hands. However, existing datasets predominantly rely on single-modality inputs, such as RGB images, which inherently lack the capacity to capture the spatial and temporal dynamics essential fo
This work develops a fast mission planning framework named planning decision tree (PDT), that can handle large-scale multi-robot systems with temporal logic specifications in real time. Specifically, PDT builds a tree incrementally to represent the task progress. The system states are modeled by bot
Visual-based robot pose estimation is a fundamental challenge, involving the determination of the camera’s pose with respect to a robot. Conventional methods for camera-to-robot pose calibration rely on fiducial markers to establish keypoint correspondences. However, these approaches exhibit signifi…
Fast motion planning is of great significance, espe-cially when a timely mission is desired. However, the complexity of motion planning can grow drastically with the increase of environment details and mission complexity. This challenge can be further exacerbated if the tasks are coupled with the de…
Learning-based path planning is becoming a promising robot navigation methodology due to its adaptability to various environments. However, the expensive computing and storage associated with networks impose significant challenges for their deployment on low-cost robots. Motivated by this practical…
Transparent objects are widely used in industrial automation and daily life. However, robust visual recognition and perception of transparent objects have always been a major challenge. Currently, most commercial-grade depth cameras are still not good at sensing the surfaces of transparent objects d…
In this letter, we present a transformer-based architecture, namely TF-Grasp, for robotic grasp detection. The developed TF-Grasp framework has two elaborate designs making it well suitable for visual grasping tasks. The first key design is that we adopt the local window attention to capture local c