One late-January day, the director Lila Neugebauer was at a gun range--or an antiseptic, fluorescent-white version of one--tucked inside the Specialists, Ltd., a theatrical-props behemoth in Ridgewood, Queens. Neugebauer, accompanied by two members of her team, had come to discuss a gun for her upcoming production of Anton Chekhov's "Uncle Vanya," at Lincoln Center Theatre. The production is a starry one, with Steve Carell in the title role, alongside Alfred Molina, Alison Pill, Anika Noni Rose, and William Jackson Harper. With a new translation by the playwright Heidi Schreck--who was nominated for a Tony for her women's-rights jeremiad "What the Constitution Means to Me"--this is the first Broadway staging of Chekhov's masterpiece in more than twenty years. Neugebauer is small and quick, with flyaway black hair, straight black brows crossing a narrow face, and intent gray-green-golden eyes, like a fox's.

Current motion planners, such as the ones available in ROS MoveIt, can solve difficult motion planning problems. However, these planners are not practical in unstructured, rapidly-changing environments. First, they assume that the environment is well-known, and static during planning and execution. Second, they do not support temporal constraints, which are often important for synchronization between a robot and other actors. Third, because many popular planners generate completely new trajectories for each planning problem, they do not allow for representing persistent control policy information associated with a trajectory across planning problems.

Chekhov's gun is a dramatic principle stating that every element in a story must be necessary, and irrelevant elements should be removed. This paper presents a new natural language processing task -- Chekhov's gun recognition or (CGR) -- recognition of entities that are pivotal for the development of the plot. Though similar to classical Named Entity Recognition (NER) it has profound differences and is crucial for the tasks of narrative processing, since Chekhov's guns have a profound impact on the causal relationship in a story. The paper presents a new benchmark dataset for the CGR task that includes 5550 descriptions with one or more Chekhov's Gun in each and validates the task on two more datasets available in the natural language processing (NLP) literature. "One must never place a loaded rifle on the stage if it isn't going to go off. It's wrong to make promises you don't mean to keep."

Current motion planners, such as the ones available in ROS MoveIt, can solve difficult motion planning problems. However, these planners are not practical in unstructured, rapidly-changing environments. First, they assume that the environment is well-known, and static during planning and execution. Second, they do not support temporal constraints, which are often important for synchronization between a robot and other actors. Third, because many popular planners generate completely new trajectories for each planning problem, they do not allow for representing persistent control policy information associated with a trajectory across planning problems. We present Chekhov, a reactive, integrated motion planning and execution system that addresses these problems. Chekhov uses a Tube-based Roadmap in which the edges of the roadmap graph are families of trajectories called flow tubes, rather than the single trajectories commonly used in roadmap systems. Flow tubes contain control policy information about how to move through the tube, and also represent the dynamic limits of the system, which imply temporal constraints. This, combined with an incremental APSP algorithm for quickly finding paths in the roadmap graph, allows Chekhov to operate in rapidly changing environments. Testing in simulation, and with a robot testbed has shown improvement in planning speed and motion predictability over current motion planners.