4 accepted papers
The robotics community is increasingly interested in autonomous aerial transportation. Unmanned aerial vehicles with suspended payloads have advantages over other systems, including mechanical simplicity and agility, but pose great challenges in planning and control. To realize fully autonomous aeri
In this letter, we proposea novel vision-based grasp system for closed-loop 6-degrees of freedom grasping of unknown objects in cluttered environments. The key factor in our system is that we make the most of a geometry-aware scene representation based on a truncated signed distance function (TSDF)
The engineering design of robotic grippers presents an ample design space for optimization towards robust grasping. In this letter, we investigate how learning method can be used to support the design reconfiguration of robotic grippers for grasping using a novel soft structure with omni-directional
Robot learning is widely accepted by academia and industry with its potentials to transform autonomous robot control through machine learning. Inspired by widely used soft fingers on grasping, we propose a method of rigid-soft interactive learning, aiming at reducing the time of data collection. In