Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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High Level Robot Behaviors
Automated Feedback for Unachievable High-Level Robot BehaviorsOne of the main challenges in robotics is the generation of controllers for autonomous, high-level robot behaviors comprising a non-trivial sequence of actions. Recently, formal methods have emerged as a powerful tool for automatically generating autonomous robot controllers that guarantee desired behaviors expressed by a class of temporal logic specifications. However, when there is no controller that fulfills the specification, these approaches do not provide the user with a source of failure, making the troubleshooting of specifications an unstructured and time-consuming process. In this paper, we describe a procedure for analyzing an unsynthesizable specification to identify causes of failure. We also provide an interactive game for exploring possible causes of failure, in which the user attempts to fulfill the robot specification against an adversarial environment. Our approach is implemented within the LTLMoP toolkit for robot mission planning.
Backtracking Temporal Logic Synthesis for Uncertain EnvironmentsThis paper considers the problem of synthesizing correct-by-construction robotic controllers in environments with uncertain but fixed structure. "Environment" has two notions in this work: a map or "world" in which some controlled agent must operate and navigate (i.e., evolve in a configuration space with obstacles); and an adversarial player that selects continuous and discrete variables to try to make the agent fail (as in a game). Both the robot and the environment are subjected to behavioral specifications expressed as an assume-guarantee linear temporal logic (LTL) formula. We then consider how to efficiently modify the synthesized controller when the robot encounters unexpected changes in its environment. The crucial insight is that a portion of this problem takes place in a metric space, which provides a notion of nearness. Thus if a nominal plan fails, we need not resynthesize it entirely, but instead can "patch" it locally. We present an algorithm for doing this, prove soundness (correctness of output), and demonstrate it on an example gridworld.
On the Revision Problem of Specification AutomataOne of the important challenges in robotics is the automatic synthesis of provably correct controllers from high level specifications. One class of such algorithms operates in two steps: (i) high level discrete controller synthesis and (ii) low level continuous controller synthesis. In this class of algorithms, when phase (i) fails, then it is desirable to provide feedback to the designer in the form of revised specifications that can be achieved by the system. In this paper, we address the minimal revision problem for specification automata. That is, we construct automata specifications that are as ``close" as possible to the initial user intent, by removing the minimum number of constraints from the specification that cannot be satisfied. We prove that the problem is computationally hard and we encode it as a satisfiability problem. Then, the minimal revision problem can be solved by utilizing efficient SAT solvers.
LTL Robot Motion Control Based on Automata Learning of Environmental DynamicsWe develop a technique to automatically generate a control policy for a robot moving in an environment that includes elements with partially unknown, changing behavior. The robot is required to achieve an optimal surveillance mission, in which a certain request needs to be serviced repeatedly, while the expected time in between consecutive services is minimized. We define a fragment of Linear Temporal Logic (LTL) to describe such a mission and formulate the problem as a temporal logic game. Our approach is based on two main ideas. First, we extend results in automata learning to detect patterns of the partially unknown behavior of the elements in the environment. Second, we employ an automata-theoretic method to generate the control policy. We show that the obtained control policy converges to an optimal one when the unknown behavior patterns are fully learned. We implemented the proposed computational framework in MATLAB. Illustrative case studies are included.
Towards Formal Synthesis of Reactive Controllers for Dexterous Robotic ManipulationIn robotic finger gaiting, fingers continuously manipulate an object until joint limitations or mechanical limitations periodically force a switch of grasp. Current approaches to gait planning and control are slow, lack formal guarantees on correctness, and are generally not reactive to changes in object geometry. To address these issues, we apply advances in formal methods to model a gait subject to external perturbations as a two-player game between a finger controller and its adversarial environment. High-level specifications are expressed in linear temporal logic (LTL) and low-level control primitives are designed for continuous kinematics. Simulations of planar manipulation with our synthesized correct-by-construction gait controller demonstrate the benefits of this approach.
Sequential Composition of Robust Controller SpecificationsWe present a general notion of robust controller specification and a mechanism for sequentially composing them. These specifications form tubular abstractions of the trajectories of a system in different control modes, and are motivated by the techniques available for certifying the performance of low-level controllers. The notion of controller specification provides a rigorous interface for connecting a planner and lower-level controllers that are designed and refined independently. With this approach, the planning layer does not integrate the closed-loop system dynamics and does not require the knowledge of how the controllers operate, but relies only on the specifications of the output tracking performance achieved by these controllers. The control layer aims at satisfying specifications that account quantitatively for robustness to unmodeled dynamics and various sources of disturbance and sensor noise, so that this robustness does not need to be revalidated at the planning level. As an illustrative example, we describe a randomized planner that composes different controller specifications from a given database to guarantee that any corresponding sequence of control modes steers a robot to a given region while avoiding obstacles.