Metrics for evaluating the quality of a human-robot interface are introduced. The autonomy of a robot is measured by its neglect time. The robot attention demand metric measures how much of the user’s attention is involved with instructing a robot. The free-time and fan-out metrics are two ways to measure this demand. Each of them leads to estimates of the interaction effort. Reducing interaction effort without diminishing task effectiveness is the goal of human-robot interaction design.

/pdf/metrics-for-evaluating-human-robot-interactions-6ics25y5tu.pdf

Metrics for Evaluating Human-Robot Interactions

This paper presents a summary of the space of commonly-used HCI prototyping methods (low-fidelity to high-fidelity) and asserts that with a better understanding of this space, HCI practitioners will be better equipped to direct scarce prototyping resources toward an effort likely to yield specific results It presents a set of five dimensions along which prototypes can be planned and characterized The paper then describes an analysis of this space performed by members of the NASA Ames Human-Computer Interaction Group when considering prototyping approaches for a new set of tools for Mars mission planning and scheduling tools A description is presented of a prototype that demonstrates design solutions that would have been particularly difficult to test given conventional low- or mid- fidelity prototyping methods The prototype created was "mixed-fidelity," that is, high-fidelity on some dimensions and low-fidelity on others The prototype is compared to a preexisting tool being redesigned and to a tool that has been developed using the prototype Experimental data are presented that show the prototype to be a good predictor of eventual user performance with the final application Given the relative cost of developing prototypes, it is critical to better characterize the space of fidelity in order to more precisely allocate design and development resources

Breaking the fidelity barrier: an examination of our current characterization of prototypes and an example of a mixed-fidelity success

While even STRIPS planners must search for plans of unbounded length, temporal planners must also cope with the fact that actions may start at any point in time. Most temporal planners cope with this challenge by restricting action start times to a small set of decision epochs, because this enables search to be carried out in state-space and leverages powerful state-based reachability heuristics, originally developed for classical planning. Indeed, decision-epoch planners won the International Planning Competition's Temporal Planning Track in 2002, 2004 and 2006.

However, decision-epoch planners have a largely unrecognized weakness: they are incomplete. In order to characterize the cause of incompleteness, we identify the notion of required concurrency, which separates expressive temporal action languages from simple ones. We show that decisionepoch planners are only complete for languages in the simpler class, and we prove that the simple class is 'equivalent' to STRIPS! Surprisingly, no problems with required concurrency have been included in the planning competitions. We conclude by designing a complete state-space temporal planning algorithm, which we hope will be able to achieve high performance by leveraging the heuristics that power decision epoch planners.

/pdf/when-is-temporal-planning-really-temporal-4mnuldxjas.pdf

When is temporal planning really temporal

When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior. Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where the humans have domain and task models that differ significantly from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a "model reconciliation problem" (MRP), where the AI system in effect suggests changes to the human's model, so as to make its plan be optimal with respect to that changed human model. We will study the properties of such explanations, present algorithms for automatically computing them, and evaluate the performance of the algorithms.

Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy

Robotic sampling is attractive in many field robotics applications that require persistent collection of physical samples for ex-situ analysis. Examples abound in the earth sciences in studies involving the collection of rock, soil, and water samples for laboratory analysis. In our test domain, marine ecosystem monitoring, detailed understanding of plankton ecology requires laboratory analysis of water samples, but predictions using physical and chemical properties measured in real-time by sensors aboard an autonomous underwater vehicle AUV can guide sample collection decisions. In this paper, we present a data-driven and opportunistic sampling strategy to minimize cumulative regret for batches of plankton samples acquired by an AUV over multiple surveys. Samples are labeled at the end of each survey, and used to update a probabilistic model that guides sampling during subsequent surveys. During a survey, the AUV makes irrevocable sample collection decisions online for a sequential stream of candidates, with no knowledge of the quality of future samples. In addition to extensive simulations using historical field data, we present results from a one-day field trial where beginning with a prior model learned from data collected and labeled in an earlier campaign, the AUV collected water samples with a high abundance of a pre-specified planktonic target. This is the first time such a field experiment has been carried out in its entirety in a data-driven fashion, in effect ?closing the loop? on a significant and relevant ecosystem monitoring problem while allowing domain experts marine ecologists to specify the mission at a relatively high level.

Data-driven robotic sampling for marine ecosystem monitoring

The Mars Exploration Rover mission is one of NASA's most ambitious science missions to date. Launched in the summer of 2003, each rover carries instruments for conducting remote and in site observations to elucidate the planet's past climate, water activity, and habitability. Science is MER's primary driver, so making best use of the scientific instruments, within the available resources, is a crucial aspect of the mission. To address this criticality, the MER project team selected MAPGEN (mixed initiative activity plan generator) as an activity-planning tool. MAPGEN combines two existing systems, each with a strong heritage: the APGEN activity-planning tool from the Jet Propulsion Laboratory and the Europa planning and scheduling system from NASA Ames Research Center. We discuss the issues arising from combining these tools in this mission's context. MAPGEN is the first AI-based system to control a space platform on another planet's surface.

https://ti.arc.nasa.gov/m/pub-archive/542h/0542%20(Ai-Chang)%20v3.pdf

MAPGEN: mixed-initiative planning and scheduling for the Mars Exploration Rover mission

The Mars Exploration Rovers Mars '03 mission is one of NASA's most ambitious science missions to date. The rovers will be launched in the summer of 2003 with each rover carrying instruments to conduct remote and in-situ observation to elucidate the planet's past climate, water activity, and habitability. Science is the primary driver of MER and, as a consequence, making best use of the scientific instruments, within the available resources, is a crucial aspect of the mission. To address this critically, the MER project has selected MAPGEN (Mixed-Initiative Activity Plan GENerator) as an activity planning tool. MAPGEN combines two exiting systems, each with a strong heritage: APGEN the Activity Planning tool from the Jet Propulsion Laboratory and the Europs Planning/Scheduling system from NASA Ames Research Center. This paper discusses the issues arising from combining these tools in the context of this mission.

/pdf/mapgen-mixed-initiative-planning-and-scheduling-for-the-mars-1xyj2krahs.pdf

MAPGEN : mixed initiative planning and scheduling for the Mars '03 MER mission

This document describes the Mixed initiative Activity Plan Generation system MAPGEN. This system is one of the critical tools in the Mars Exploration Rover mission surface operations, where it is used to build activity plans for each of the rovers, each Martian day. The MAPGEN system combines an existing tool for activity plan editing and resource modeling, with an advanced constraint-based reasoning and planning framework. The constraint-based planning component provides active constraint and rule enforcement, automated planning capabilities, and a variety of tools and functions that are useful for building activity plans in an interactive fashion. In this demonstration, we will show the capabilities of the system and demonstrate how the system has been used in actual Mars rover operations. In contrast to the demonstration given at ICAPS 03, significant improvement have been made to the system. These include various additional capabilities that are based on automated reasoning and planning techniques, as well as a new Constraint Editor support tool. The Constraint Editor (CE) as part of the process for generating these command loads, the MAPGEN tool provides engineers and scientists an intelligent activity planning tool that allows them to more effectively generate complex plans that maximize the science return each day. The key to the effectiveness of the MAPGEN tool is an underlying constraint-based planning and reasoning engine.

/pdf/mapgen-planner-mixed-initiative-activity-planning-for-the-4nsetiuqti.pdf

Jennifer Hsu

Papers

MAPGEN: mixed-initiative planning and scheduling for the Mars Exploration Rover mission

MAPGEN : mixed initiative planning and scheduling for the Mars '03 MER mission

MAPGEN Planner: Mixed-Initiative Activity Planning for the Mars Exploration Rover Mission