Steven D. Klee

Personalized Assistance for Dressing Users

In this paper, we present an approach for a robot to provide personalized assistance for dressing a user. In particular, given a dressing task, our approach finds a solution involving manipulator motions and also user repositioning requests. Specifically, the solution allows the robot and user to take turns moving in the same space and is cognizant of the user’s limitations. To accomplish this, a vision module monitors the human’s motion, determines if he is following the repositioning requests, and infers mobility limitations when he cannot. The learned constraints are used during future dressing episodes to personalize the repositioning requests. Our contributions include a turn-taking approach to human-robot coordination for the dressing problem and a vision module capable of learning user limitations. After presenting the technical details of our approach, we provide an evaluation with a Baxter manipulator.

CMDragons 2015: Coordinated Offense and Defense of the SSL Champions

The CMDragons Small Size League SSL team won all of itsi¾ź6 games at RoboCup 2015, scoring a total of 48 goals and conceding 0. This paper presents the core coordination algorithms in offense and defense that enabled such successful performance. We first describe the coordinated plays layer that distributes the teams robots into offensive and defensive subteams. We then describe the offense and defense coordination algorithms to control these subteams. Effective coordination enables our robots to attain a remarkable level of team-oriented gameplay, persistent offense, and reliability during regular gameplay, shifting our strategy away from stopped ball plays. We support these statements and the effectiveness of our algorithms with statistics from our performance at RoboCup 2015.

Graph-Based Task Libraries for Robots: Generalization and Autocompletion

In this paper, we consider an autonomous robot that persists over time performing tasks and the problem of providing one additional task to the robot’s task library. We present an approach to generalize tasks, represented as parameterized graphs with sequences, conditionals, and looping constructs of sensing and actuation primitives. Our approach performs graph-structure task generalization, while maintaining task executability and parameter value distributions. We present an algorithm that, given the initial steps of a new task, proposes an autocompletion based on a recognized past similar task. Our generalization and autocompletion contributions are effective on different real robots. We show concrete examples of the robot primitives and task graphs, as well as results, with Baxter. In experiments with multiple tasks, we show a significant reduction in the number of new task steps to be provided.

Multi-robot task acquisition through sparse coordination

In this paper, we consider several autonomous robots with separate tasks that require coordination, but not a coupling at every decision step. We assume that each robot separately acquires its task, possibly from different providers. We address the problem of multiple robots incrementally acquiring tasks that require their sparse-coordination. To this end, we present an approach to provide tasks to multiple robots, represented as sequences, conditionals, and loops of sensing and actuation primitives. Our approach leverages principles from sparse-coordination to acquire and represent these joint-robot plans compactly. Specifically, each primitive has associated preconditions and effects, and robots can condition on the state of one another. Robots share their state externally using a common domain language. The complete sparse-coordination framework runs on several robots. We report on experiments carried out with a Baxter manipulator and a CoBot mobile service robot.