Brennan Sellner

Coordinated Multiagent Teams and Sliding Autonomy for Large-Scale Assembly

Recent research in human-robot interaction has investigated the concept of Sliding, or Adjustable, Autonomy, a mode of operation bridging the gap between explicit teleoperation and complete robot autonomy. This work has largely been in single-agent domains-involving only one human and one robot-and has not examined the issues that arise in multiagent domains. We discuss the issues involved in adapting Sliding Autonomy concepts to coordinated multiagent teams. In our approach, remote human operators have the ability to join, or leave, the team at will to assist the autonomous agents with their tasks (or aspects of their tasks) while not disrupting the teams coordination. Agents model their own and the human operators performance on subtasks to enable them to determine when to request help from the operator. To validate our approach, we present the results of two experiments. The first evaluates the human/multirobot teams performance under four different collaboration strategies including complete teleoperation, pure autonomy, and two distinct versions of Sliding Autonomy. The second experiment compares a variety of user interface configurations to investigate how quickly a human operator can attain situational awareness when asked to help. The results of these studies support our belief that by incorporating a remote human operator into multiagent teams, the team as a whole becomes more robust and efficient

GRACE: an autonomous robot for the AAAI Robot challenge

In an attempt to solve as much of the AAAI Robot Challenge as possible, five research institutions representing academia, industry, and government integrated their research into a single robot named GRACE. This article describes this first-year effort by the GRACE team, including not only the various techniques each participant brought to GRACE but also the difficult integration effort itself.

Attaining situational awareness for sliding autonomy

We are interested in the problems of a human operator who is responsible for rapidly and accurately responding to requests for help from an autonomous robotic construction team. A difficult aspect of this problem is gaining an awareness of the requesting robots situation quickly enough to avoid slowing the whole team down. One approach to speeding the initial acquisition of situational awareness is to maintain a buffer of data, and play it back for the human when their help is needed. We report here on an experiment to determine how the composition and length of this buffer affect the humans speed and accuracy in our multi-robot construction domain. The experiments show that, for our scenario, 5 - 10 seconds of one raw video feed led to the fastest operator attainment of situational awareness, while accuracy was maximized by viewing 10 seconds of three video feeds. These results are necessarily specific to our scenario, but we feel that they indicate general trends which may be of use in other situations. We discuss the interacting effects of buffer composition and length on operator speed and accuracy, and draw several conclusions from this experiment which may generalize to other scenarios.

User Modelling for Principled Sliding Autonomy in Human-Robot Teams

The complexity of heterogeneous robotic teams and the domains in which they are deployed is fast outstripping the ability of autonomous control software to handle the myriad failure modes inherent in such systems. As a result, remote human operators are being brought into the teams as equal members via sliding autonomy to increase the robustness and effectiveness of such teams. A principled approach to deciding when to request help from the human will benefit such systems by allowing them to efficiently make use of the human partner. We have developed a cost-benefit analysis framework and models of both autonomous system and user in order to enable such principled decisions. In addition, we have conducted user experiments to determine the proper form for the learning curve component of the human’s model. The resulting automated analysis is able to predict the performance of both the autonomous system and the human in order to assign responsibility for tasks to one or the other.

Duration prediction for proactive replanning

Proactive replanning attempts to predict scheduling problems or opportunities and adapt to them throughout a schedules execution. By continuously predicting a tasks remaining duration, a proactive replanner is able to accommodate upcoming problems or opportunities before they manifest themselves. We have developed a kernel density estimation-based method for predicting a tasks duration distribution as it executes, and have integrated our prediction algorithm with an existing planner based on heuristic repair. Our predictor allows the planner to anticipate problems, or opportunities, early enough to avoid, or take advantage of, them, resulting in executed schedules that score significantly higher on a number of metrics. We have evaluated a limited form of our approach in simulation, and present the results of our experiments. The addition of duration prediction resulted in a 11.1% improvement in average reward. Compared with an omniscient planner, this is 45.0% of the maximum possible improvement.

Overcoming sensor noise for low-tolerance autonomous assembly

The capability to assemble structures is fundamental to the use of robotics in precursor missions in orbit and on planetary surfaces. We have performed autonomous assembly in neutral buoyancy of elements of a space truss whose mating components require positioning tolerances of the same order of magnitude as the noise in the sensor systems used for the docking. Numerous trade-offs, design decisions, and innovations were made during the development of the assembly system in order to both reduce and compensate for the sensor noise. By using relative positioning, decoupling sensing and manipulation, caching high-quality position estimates, and developing a new waypoint-completion metric, we were able to reduce sensor noise to the sub-millimeter level and autonomously assemble components with millimeter tolerances. In this paper, we discuss our approaches to the problem and report the results of a series of autonomous assembly operations.