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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.

Prehensile robotic grasping of a target object in clutter is challenging because, in such conditions, the target touches other objects, resulting to the lack of collision free grasp affordances. To address this problem, we propose a modular reinforcement learning method which uses continuous actions to totally singulate the target object from its surrounding clutter. A high level policy selects between pushing primitives, which are learned separately. Prior knowledge is effectively incorporated into learning, through action primitives and feature selection, increasing sample efficiency. Experiments demonstrate that the proposed method considerably outperforms the state-of-theart methods in the singulation task. Furthermore, although training is performed in simulation the learned policy is robustly transferred to a real environment without a significant drop in success rate. Finally, singulation tasks in different environments are addressed by easily adding a new primitive and by retraining only the high level policy.The research leading to these results has received funding from the European Community's Framework Programme Horizon 2020 under grant agreement No 871704, project BACCHUS.

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Active constraint enforcement in robotic-assisted surgery is critical for reducing the intra-operative risk of unintentionally damaging sensitive tissues by the surgical instrument. This work considers surgical instruments which can be circumscribed by a geometric capsule and forbidden regions which can be approximated by point clouds in order to produce a repulsive wrench by the control action to guarantee manipulation within safe regions. This work details the control scheme which is based on barrier artificial potentials when considering the whole tool extending our previous results on the tool point. A proof of the control system's passivity and non constraint violation is provided together with experimental results using a 7-dof KUKA LWR4+ manipulator as a master device in a virtual surgical scene in order to demonstrate the effectiveness of the proposed scheme.