In this work, a methodology to find the best view of a grape stem and approach angle in order to crop it is proposed. The control scheme is based only on a classified point cloud obtained by the in-hand camera attached to the robot’s end effector without continuous stem tracking. It is shown that the proposed controller finds and reaches the optimal view point and subsequently the stem fast and efficiently, accelerating the overall harvesting procedure. The proposed control scheme is evaluated through experiments in the lab with a UR5e robot with an in-hand RealSense camera on a mock-up vine.

Kinesthetic teaching allows the direct skill transfer from the human to the robot and has been widely used to teach single arm tasks intuitively. In the bi-manual case, simultaneously moving both end-effectors is challenging due to the high physical and cognitive load imposed to the user. Thus, previous works on bi-manual task teaching resort to less intuitive methods by teaching each arm separately. This in turn requires motion synthesis and synchronization before execution. In this work, we leverage knowledge from the relative task space to facilitate a kinesthetic demonstration by guiding both end-effectors which is more human-like and intuitive way for performing bi-manual tasks. Our method utilizes the notion of virtual fixtures and inertia minimization in the null space of the task. The controller is experimentally validated in a bi-manual task which involves the drawing of a preset line on a workpiece utilizing two KUKA IIWA7 R800 robots. Results from ten participants were compared with a gravity compensation scheme demonstrating improved performance.

Kinesthetic teaching allows the direct skill transfer from the human to the robot through physical human-robot interaction. However, it is heavily affected by the robot’s dynamics and the control scheme utilized for the physical interaction. In this work, we aim at assisting the human-teacher by reducing her/his physical and cognitive load. To this aim, we propose a controller with virtual fixtures and inertia optimization for assisting kinesthetic teaching, exploiting knowledge of the task geometry and the robot redundancy. Experimental results utilizing a KUKA LWR4+ robot for the teaching of a brush painting motion on a curved surface validate the method and demonstrate its performance in comparison with a gravity compensation scheme and the utilization of virtual fixtures alone. The system is proved to be passive under the exertion of a human force.

In this work, a passive physical human-robot interaction (pHRI) controller is proposed to intraoperatively ensure that sensitive tissues will not be damaged by the robot's tool. The proposed scheme uses the point cloud of the restricted region's surface as constraint definition and Artificial Potential fields for constraint enforcement. The controller is proven to be passive with respect to the interaction force and to guarantee constraint satisfaction in all cases. The proposed methodology is experimentally validated by the kinesthetic guidance of a KUKA LWR4+ robot's end-effector driving a virtual slave KUKA in the vicinity of a 3D point-cloud of a kidney and its adjacent vessels.

Bimanual assembly of two parts require that a relative target pose is reached prior to the joining operation. Rather than utilizing one arm as a fixture for holding one of the parts while the other performs the assembly, motion generation in the relative end-effector frame is proposed that involves both arms. The proposed approach considers bimanual motion in a dynamic and uncertain environment addressing avoidance of collision with obstacles as well as the robot itself and the environment. Moreover, configurations that optimize the motion and force capabilities for the sucessful and efficient completion of the task are taken into account. A task priority strategy is adopted achieving online performance. Experimental results on the YuMi bimanual robot using the Stack-Of- Tasks hierarchical solver validate the performance of the proposed approach in a folding assembly task.