We present a system enabling a modular robot to autonomously build structures in order to accomplish high-level tasks. Building structures allows the robot to surmount large obstacles, expanding the set of tasks it can perform. This addresses a common weakness of modular robot systems, which often struggle to traverse large obstacles. This paper presents the hardware, perception, and planning tools that comprise our system. An environment characterization algorithm identifies features in the environment that can be augmented to create a path between two disconnected regions of the environment. Specially-designed building blocks enable the robot to create structures that can augment the environment to make obstacles traversable. A high-level planner reasons about the task, robot locomotion capabilities, and environment to decide if and where to augment the environment in order to perform the desired task. We validate our system in hardware experiments.

The advantage of modular robot systems lies in their flexibility, but this advantage can only be realized if there exists some reliable, effective way of generating configurations (shapes) and behaviors (controlling programs) appropriate for a given task. In this paper, we present an end-to-end system for addressing tasks with modular robots, and demonstrate that it is capable of accomplishing challenging multi-part tasks in hardware experiments. The system consists of four tightly integrated components: (1) A high-level mission planner, (2) A design library spanning a wide set of functionality, (3) A design and simulation tool for populating the library with new configurations and behaviors, and (4) Modular robot hardware. This paper condenses the material originally presented in Jing et al. 2016 into a shorter format suitable for a broad audience.

Recently, formal methods have been used to transform high-level robot tasks into correct-by-construction controllers. While correctness is guaranteed, these inherently discrete methods often lead to behaviors that are not optimal in the continuous sense, i.e. they induce robot paths that are significantly suboptimal. This paper proposes an algorithm for dynamically reordering the robot goals and connecting them via the shortest path with respect to a given continuous metric. The generated robot trajectories are close-to-optimal while satisfying the task specification in a dynamic environment. This method is implemented and simulation results are shown.

A recent method to obtain correct robot controllers is to automatically synthesize them from high-level robot missions that are specified in temporal logic. In this context, we aim for controllers that are optimal, i.e., do not let the robot take unnecessarily costly paths to reach its goals. Previous work on obtaining optimal synthesized robot controllers either ignored interactions with the environment, or assumed a cooperative environment. In this paper, we solve the problem of obtaining optimal robot controllers for adversarial environments. Our main observation is that the quality of a path to a goal has two dimensions: (1) the number of phases in which the robot waits for the environment to perform some actions and (2) the cost of the robot's actions to reach the goal. Our synthesis algorithm can take any prioritization over the possible cost combinations into account, and computes the optimal strategy in a symbolic manner, despite the fact that the action costs can be non-integer. We show the scalability of the new algorithm by example of a delivery problem.

The Linear Temporal Logic MissiOn Planning (LTLMoP) toolkit is a software package designed to generate a controller that guarantees a robot satisfies a task specification written by the user in structured English. The controller can be implemented on either a simulated or physical robot. This video illustrates the use of LTLMoP to generate a correct-by-construction robot controller. Here, an Aldebaran Nao humanoid robot carries out tasks as a worker in a simplified grocery store scenario.

The Linear Temporal Logic MissiOn Planning (LTLMoP) toolkit is a software package designed to assist in the rapid development, implementation, and testing of high-level robot controllers. In this toolkit, structured English and Linear Temporal Logic are used to write high-level reactive task specifications, which are then automatically transformed into correct robot controllers that can be used to drive either a simulated or a real robot. LTLMoP's modular design makes it ideal for research in areas such as controller synthesis, semantic parsing, motion planning, and human-robot interaction.