This paper describes a framework for automatically generating optimal action-level behavior for a team of robots based on Temporal Logic mission specifications under resource constraints. The proposed approach optimally allocates separable tasks to available robots, without requiring a-priori an explicit representation of the tasks or the computation of all task execution costs. Instead, we propose an approach for identifying sub-tasks in an automaton representation of the mission specification and for simultaneously allocating the tasks and planning their execution. The proposed framework avoids the need of computing a combinatorial number of possible assignment costs, where each computation itself requires solving a complex planning problem. This can improve computational efficiency compared to classical assignment solutions, in particular for on-demand missions where task costs are unknown in advance. We demonstrate the applicability of the approach with multiple robots in an existing office environment and evaluate its performance in several case study scenarios.

Generating verifiably correct execution strategies from Linear Temporal Logic (LTL) mission specifications avoids the need for manually designed robot behaviors. However, when incorporating a team of robot agents, the additional model complexity becomes a critical issue. Given a single finite LTL mission and a team of robots, we propose an automata-based approach to automatically identify possible decompositions of the LTL specification into sets of independently executable task specifications. Our approach leads directly to the construction of a team model with significantly lower complexity than other representations constructed with conventional methods. Thus, it enables efficient search for an optimal decomposition and allocation of tasks to the robot agents.

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Abstract-We present an efficient approach to plan action sequences for a team of robots from a single finite LTL mission specification. The resulting execution strategy is proven to solve the given mission with minimal team costs, e.g., with shortest execution time. For planning, an established graphbased search method based on the multi-objective shortest path problem is adapted to multi-robot planning and extended to support resource constraints. We further improve planning efficiency significantly for missions which consist of independent parts by using previous results regarding LTL decomposition. The efficiency and practicality of the ROS implementation of our approach is demonstrated in example scenarios.

Team ViGIR and Team Hector participated in the DARPA Robotics Challenge (DRC) Finals, held June 2015 in Pomona, California, along with 21 other teams from around the world. Both teams competed using the same high‐level software, in conjunction with independently developed low‐level software specific to their humanoid robots. On the basis of previous work on operator‐centric manipulation control at the level of affordances, we developed an approach that allows one or more human operators to share control authority with a high‐level behavior controller. This collaborative autonomy decreases the completion time of manipulation tasks, increases the reliability of the human‐robot team, and allows the operators to adjust the robotic system's autonomy on‐the‐fly. This article discusses the technical challenges we faced and overcame during our efforts to allow the human operators to interact with the robotic system at a higher level of abstraction and share control authority with it. We introduce and evaluate the proposed approach in the context of our two teams' participation in the DRC Finals. We also present additional, systematic experiments conducted in the lab afterward. Finally, we present a discussion about the lessons learned while transitioning between operator‐centered manipulation control and behavior‐centered manipulation control during competition.