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In many future joint-action scenarios, humans and robots will have to interact physically in order to successfully cooperate. Ideally, seamless human-robot interaction should not require training for the human, but should be intuitively simple. Nonetheless, seamless interaction and cooperation involve some degree of learning and adaptation. Here, we report on a simple case of physical human-robot interaction, a handover task. Even such a basic task as manually handing over an object from one agent to another requires that both partners agree upon certain basic prerequisites and boundary conditions. While some of them are negotiated explicitly, e.g. by verbal communication, others are determined indirectly and adaptively in the course of the cooperation. In the present study, we compared human-human handover interaction with the same task done by a robot and a human. To evaluate the importance of biological motion, the robot human interaction was tested with two different velocity profiles: a conventional trapezoidal velocity profile in joint coordinates and a minimum-jerk profile of the end-effector. Our results show a significantly shorter reaction time for minimum jerk profiles, which decreased over the first three handovers. The results of our comparison provide the background for implementing effective joint-action strategies in humanoid robot systems.

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Ethernet-based protocols are getting more and more important for Industry 4.0 and the Internet of Things. In this paper, we compare the features, package overhead, and performance of some of the most important protocols in this area. First, we present a general feature comparison of OPC UA, ROS, DDS, and MQTT, followed by a more detailed wire protocol evaluation, which gives an overview over the protocol overhead for establishing a connection and sending data. In the performance tests we evaluate open-source implementations of these protocols by measuring the round trip time of messages in different system states: idle, high CPU load, and high network load. The performance analysis concludes with a test measuring the round trip time for 500 nodes on the same host.

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Computationally efficient motion planning must avoid exhaustive exploration of configuration space. We argue that this can be accomplished most effectively by carefully balancing exploration and exploitation. Exploration seeks to understand configuration space, irrespective of the planning problem, while exploitation acts to solve the problem given the available information obtained by exploration. We present an exploring/exploiting tree (EET) planner that balances its exploration and exploitation behavior. The planner acquires workspace information and subsequently uses this information for exploitation in configuration space. If exploitation fails in difficult regions, the planner gradually shifts its behavior towards exploration. We present experimental results demonstrating that adaptive balancing of exploration and exploitation leads to significant performance improvements compared to other state-of-the-art sampling-based planners. I. INTRODUCTIONSampling-based motion planners routinely solve complex, high-dimensional planning problems. This may seem surprising, considering that the general motion planning problem [1] is PSPACE-hard [2]. However, the configuration spaces of many practical problems contain considerable structure that may help in solving a planning problem. In addition, not all parts of configuration space have to be explored to solve a particular motion planning problem. Today's samplingbased planners leverage these properties of practical motion planning problems to achieve computational efficiency.We cast motion planning as a state space search problem, similar to work in reinforcement learning [3], [4], to gain insights on how to improve the computational efficiency of motion planners. In this formulation, there are two competing goals of planning: exploration and exploitation.Exploration seeks to understand the connectivity of the configuration space, irrespective of a particular motion planning problem. Exploration thus does not assess if a region of configuration space is relevant for a particular task; rather, it explores to improve the planner's understanding of configuration space. A typical example from motion planning is the initial roadmap building phase of the basic PRM planner with uniform random sampling [5].Exploitation strives to determine a valid path for a specific task, leveraging available information. Exploitation thus assumes that the information available suffices to solve the problem and just begins to act. For example, the artificial potential field approach performs pure exploitation [6].Guided exploration, a technique performed by most sampling-based motion planners, improves on pure exploration by leveraging available information to guide exploitation based on characteristics of the underlying state space.

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Favourable results with identical clinical outcomes and a high rate of fusion was seen in both groups. The titanium coating appears to have no negative effects on outcome or safety in the short term. A future study to determine the effect of titanium coating is warranted. Cite this article: 2017;99-B:1366-72.