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Abstract-In hands-on robotic surgery, the surgical tool is mounted on the end effector of a robot and is directly manipulated by the surgeon. This simultaneously exploits the strengths of both humans and robots; such that the surgeon directly feels tool-tissue interactions and remains in control of the procedure, while taking advantage of the robot's higher precision and accuracy. A crucial challenge in hands-on robotics for delicate manipulation tasks, such as surgery, is that the user must interact with the dynamics of the robot at the end effector, which can reduce dexterity and increase fatigue. This paper presents a null-space based optimization technique for simultaneously minimizing the mass and friction of the robot that is experienced by the surgeon. By defining a novel optimization technique for minimizing the projection of the joint friction onto the end effector, and integrating this with our previous techniques for minimizing the belted mass/inertia as perceived by the hand, a significant reduction in dynamics felt by the user is achieved. Experimental analyses in both simulation and human user trials demonstrate that the presented method can reduce the user experienced dynamic mass and friction by, on average, 44% and 41% respectively. The results presented robustly demonstrate that optimizing a robots pose can result in a more natural tool motion, potentially allowing future surgical robots to operate with increased usability, improved surgical outcomes and wider clinical uptake.

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We investigate controllers for mobile humanoid robots that maneuver in irregular terrains while performing accurate physical interactions with the environment and with human operators and test them on Dreamer, our new robot with a humanoid upper body (torso, arm, head) and a holonomic mobile base (triangularly arranged Omni wheels). All its actuators are torque controlled, and the upper body provides redundant degrees of freedom. We developed new dynamical models and created controllers that stabilize the robot in the presence of slope variations, while it compliantly interacts with humans.This paper considers underactuated free-body dynamics with contact constraints between the wheels and the terrain. Moreover, Dreamer incorporates a biarticular mechanical transmission that we model as a force constraint. Using these tools, we develop new compliant multiobjective skills and include selfmotion stabilization for the highly redundant robot.

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