Diffusion-based Virtual Fixtures

For a long time, robotics considered objects in the environment primarily as obstacles and the goal was to avoid contact due to modeling and sensing difficulties. However, the specifying only the target and obstacle regions, a smooth flow trend has shifted towards embracing contact due to increasing field on the tangent space can guide agents to the closest target interest in manipulation, tactile robotics, and surface inspection while avoiding the restricted zones and maintaining contact tasks. Consequently, robots physically interact with their with the surface, as depicted in Figure 1-c. For addressing surrounding environment that can charecterized by curved these challenges, we propose a surface virtual fixture method surfaces, which can also be soft and fragile (e.g., surgical expecting surfaces as possibly noisy and partial point clouds robotics). However, safety in these tasks remains a major collected in runtime using an off-the-shelf camera attached concern during deployment in real-world as they involve to the robot. Next, we segment the point cloud into a set forceful interactions. Considering that a significant percentage of regions with their specified behavior. This segmentation of recent approaches propose learning-based controllers, and can come from learning-based methods using vision [7] or that the majority of shared control and teleoperation tasks geometry [8]. Alternatively, one can use virtual or real-world depend on the operator's expertise or skills, safety takes a expert annotations [5], [6], possibly in combination with more central role in assistive systems.