Abstract-A key requirement for seamless human-robot collaboration is for the robot to make its intentions clear to its human collaborator. A collaborative robot's motion must be legible, or intent-expressive. Legibility is often described in the literature as and effect of predictable, unsurprising, or expected motion. Our central insight is that predictability and legibility are fundamentally different and often contradictory properties of motion. We develop a formalism to mathematically define and distinguish predictability and legibility of motion. We formalize the two based on inferences between trajectories and goals in opposing directions, drawing the analogy to action interpretation in psychology. We then propose mathematical models for these inferences based on optimizing cost, drawing the analogy to the principle of rational action. Our experiments validate our formalism's prediction that predictability and legibility can contradict, and provide support for our models. Our findings indicate that for robots to seamlessly collaborate with humans, they must change the way they plan their motion.

Abstract-This paper addresses the challenge of enabling nonexpert users to command robots to perform complex high-level tasks using natural language. It describes an integrated system that combines the power of formal methods with the accessibility of natural language, providing correct-by-construction controllers for high-level specifications that can be implemented, and easy-to-understand feedback to the user on those that cannot be achieved. This is among the first works to close this feedback loop, enabling users to interact with the robot in order to identify a succinct cause of failure and obtain the desired controller. The supported language and logical capabilities are illustrated using examples involving a robot assistant in a hospital.

In this paper, we explore a class of teleoperation problems where a user controls a sophisticated device (e.g. a robot) via an interface to perform a complex task. Teleoperation interfaces are fundamentally limited by the indirectness of the process, by the fact that the user is not physically executing the task. In this work, we study intelligent and customizable interfaces: these are interfaces that mediate the consequences of indirectness and make teleoperation more seamless. They are intelligent in that they take advantage of the robot's autonomous capabilities and assist in accomplishing the task. They are customizable in that they enable the users to adapt the retargetting function which maps their input onto the robot. Our studies support the advantages of such interfaces, but also point out the challenges they bring. We make three key observations. First, although assistance can greatly improve teleperation, the decision on how to provide assistance must be contextual. It must depend, for example, on the robot's confidence in its prediction of the user's intent. Second, although users do have the ability to provide intent-expressive input that simplifies the robot's prediction task, this ability can be hindered by kinematic differences between themselves and the robot. And third, although interface customization is important, it must be robust to poor examples from the user.

We propose the demonstration of SP4, a software-based programmable packet processing platform that supports (1) stateful packet processing useful for analyzing traffic flows with session semantics, (2) uses a task-stealing architecture that automatically leverages multi-core processing capabilities in a load-balanced manner without the need for explicit performance profiling, and (3) a declarative language for rapidly specifying and composing new packet processing functionalities from reusable modules. Our demonstration showcases the use of SP4 for performing high-throughput analysis of traffic traces for a variety of applications, such as filtering out unwanted traffic and detection of DDoS attacks using machine learning based analysis.