Reproducibility is fundamental to robotics research, but access to appropriate hardware is a major limiting factor of reproducing experiments. This limitation usually arises from custom-built solutions without sufficient documentation and, simply, high costs. We propose that the best way to improve reproducibility is by removing barriers to safely sharing hardware. Towards this, we present a framework for making experimental environments remotely accessible. Modern virtualization tools like Linux containers are leveraged to enable reproducible and isolated access. The basic idea is to run user code in a container and transport all inputs and outputs through proxy programs that monitor for unsafe states. This interface is low-level: proxies can operate across serial lines, TCP, UDP, or HTTP connections. Thus, users are not locked into any particular programming library or application-level messaging system. The framework is shown in four case studies: the social robot Misty, the mobile robot Kobuki (base of the popular TurtleBot 2) with a LiDAR, wireless sensor networks, and Autoware.

Hands-on programming of humanoid robots can be effectively utilized to motivate interest in science, technology, engineering, art, and mathematics for K-12 students. Constrained by pandemic restrictions, our academic and industry team developed and deployed an Internet-accessible capability for K-12 students to remotely program Misty II robots using the Blockly language. Utilizing configurations with both stationary and mobile robots, the students were able to create and run programs on the robots and watch the resulting behaviors via a live video stream. This remote mechanism provides both tracking of accesses and usage of specific programming features as well as protection from accidental or purposeful destructive actions. Initial use of this capability with five groups and a total of 170 3rd grade through high school students demonstrated that the approach can effectively motivate student interest in both robotics and programming. This remote robot access mechanism demonstrates how cost effective access to advanced robots can be provided at a potentially large scale.

Recent proliferation of cyber-physical systems, ranging from autonomous cars to nuclear hazard inspection robots, has exposed several challenging research problems on automated fault detection and recovery. This paper considers how recently developed formal synthesis and model verification techniques may be used to automatically generate information-seeking trajectories for anomaly detection. In particular, we consider the problem of how a robot could select its actions so as to maximally disambiguate between different model hypotheses that govern the environment it operates in or its interaction with other agents whose prime motivation is a priori unknown. The identification problem is posed as selection of the most likely model from a set of candidates, where each candidate is an adversarial Markov decision process (MDP) together with a linear temporal logic (LTL) formula that constrains robot-environment interaction. An adversarial MDP is an MDP in which transitions depend on both a (controlled) robot action and an (uncontrolled) adversary action. States are labeled, thus allowing interpretation of satisfaction of LTL formulae, which have a special form admitting satisfaction decisions in bounded time. An example where a robotic car must discern whether neighboring vehicles are following its trajectory for a surveillance operation is used to demonstrate our approach.

This paper presents a method for optimal trajectory generation for discrete-time nonlinear systems with linear temporal logic (LTL) task specifications. Our approach is based on recent advances in stochastic optimization algorithms for optimal trajectory generation. These methods rely on estimation of the rare event of sampling optimal trajectories, which is achieved by incrementally improving a sampling distribution so as to minimize the cross-entropy. A key component of these stochastic optimization algorithms is determining whether or not a trajectory is collision-free. We generalize this collision checking to efficiently verify whether or not a trajectory satisfies a LTL formula. Interestingly, this verification can be done in time polynomial in the length of the LTL formula and the trajectory. We also propose a method for efficiently re-using parts of trajectories that only partially satisfy the specification, instead of simply discarding the entire sample. Our approach is demonstrated through numerical experiments involving Dubins car and a generic point-mass model subject to complex temporal logic task specifications.

The cost of the great expressivity of motion planning subject to temporal logic formulae is intractability. Recent advances in sampling-based methods seem to be only applicable to “low-level” control. The problem of realizing “high-level” controllers that satisfy a temporal logic specification does not readily admit approximations, unless the notion of correctness is relaxed as might be achieved with probabilistic variants of temporal logics. In this paper, we argue that not all possible environment (uncontrolled) behaviors need to be explicitly planned for, but rather short-time strategies can be generated online while maintaining global correctness. We achieve this by separating feasibility from controller synthesis, using metrics from the underlying continuous state space to ensure short-time strategies chained together provide globally correct behavior.

We present a method for mending strategies for GR(1) specifications. Given the addition or removal of edges from the game graph describing a problem (essentially transition rules in a GR(1) specification), we apply a μ-calculus formula to a neighborhood of states to obtain a “local strategy” that navigates around the invalidated parts of an original synthesized strategy. Our method may thus avoid global resynthesis while recovering correctness with respect to the new specification. We illustrate the results both in simulation and on physical hardware for a planar robot surveillance task.

In 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.

This 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.