5 accepted papers
Autonomous exploration in large-scale and complex environments is a challenging task. As the size of the environment increases, the significant overhead of exploration algorithms could overwhelm the computational capability of mobile platforms, prohibiting timely response to environmental changes. M
Compared with using only a single type of robot, the use of drones and ground vehicles to jointly explore unknown areas can bring efficiency improvements. However, due to the difficulty of ground-aerial loop detection and especially the lack of ground-air datasets in large outdoor scenes, there is n
In this letter, we propose a heterogeneous deep metric learning pipeline for ground and aerial point cloud-based place recognition in large-scale environments. The pipeline extracts local features from ground and aerial raw point clouds by a sparse convolution module. The local features are processe
In this paper, we approach the challenging problem of motion planning for knot tying. We propose a hierarchical approach in which the top layer produces a topological plan and the bottom layer translates this plan into continuous robot motion. The top layer decomposes a knotting task into sequences…
We demonstrate model-based, visual robot manipulation of deformable linear objects. Our approach is based on a state-space representation of the physical system that the robot aims to control. This choice has multiple advantages, including the ease of incorporating physics priors in the dynamics mod