Human-Centric Active Perception for Autonomous Observation

Kent, David; Chernova, Sonia

doi:10.1109/icra40945.2020.9197201

Cited by 12 publications

(6 citation statements)

References 22 publications

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“…Chernova et al have developed remote manipulation interfaces that are robust to high latency environments, researching how to leverage depth data in remote robot teleoperation interfaces for general object manipulation [43,44] and developing temporal models for robot classification of human interruptibility for modeling availability of collocated human crew members [45].…”

Section: Efficient Interaction Methodsmentioning

confidence: 99%

A Review of NASA Human-Robot Interaction in Space

2021

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Purpose of Review This review provides an overview of the motivation, challenges, state-of-the-art, and recent research for human-robot interaction (HRI) in space. For context, we focus on NASA space missions, use cases, and systems (both flight and research). However, the discussion is broadly applicable to all activities in space that require or make use of human-robot teams. Recent Findings To date, HRI in space has largely been limited to remote interaction between humans on Earth and robots in space. This interaction is associated with telerobotic operations-from direct (manual) control to intermittent, supervisory control. Recent work, however, has begun to address a wide range of human-robot arrangements (co-located, remote, 1:1, groups, etc.). In addition, researchers have been studying human-robot teaming theory and system design, efficient interaction methods, and human-robot communication. Summary We begin by describing NASA's use of robots in space for both deep space science and human exploration. We then describe several aspects of HRI that are important for space missions, with emphasis on factors that are critical or unique for the space environment. Next, we provide a brief overview of HRI associated with space systems, including technology demonstrations. Finally, we conclude with a short survey of recent research, which will affect human-robot interaction for both Artemis missions and future missions to Mars.

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Section: Efficient Interaction Methodsmentioning

confidence: 99%

A Review of NASA Human-Robot Interaction in Space

2021

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“…In [15], a trajectory smoothing algorithm is proposed based on the weighted sum of the competing objectives: trajectory length, smoothness, and obstacle distances. The authors of [16] minimize the weighted trade-off between mission completion time and communication outage duration in the navigation of cellular-connected UAVs, while in [17], linear scalarization is used to optimize robotic limitations and observation rewards for use in autonomous human activity tracking.…”

Section: Related Workmentioning

confidence: 99%

Error-Bounded Approximation of Pareto Fronts in Robot Planning Problems

Botros

Sadeghi²,

Wilde³

et al. 2022

Springer Proceedings in Advanced Robotics

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Many problems in robotics seek to simultaneously optimize several competing objectives under constraints. A conventional approach to solving such multi-objective optimization problems is to create a single cost function comprised of the weighted sum of the individual objectives. Solutions to this scalarized optimization problem are Pareto optimal solutions to the original multi-objective problem. However, finding an accurate representation of a Pareto front remains an important challenge. Using uniformly spaced weight vectors is often inefficient and does not provide error bounds. Thus, we address the problem of computing a finite set of weight vectors such that for any other weight vector, there exists an element in the set whose error compared to optimal is minimized. To this end, we prove fundamental properties of the optimal cost as a function of the weight vector, including its continuity and concavity. Using these, we propose an algorithm that greedily adds the weight vector least-represented by the current set, and provide bounds on the error. Finally, we illustrate that the proposed approach significantly outperforms uniformly distributed weights for different robot planning problems with varying numbers of objective functions.

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“…A common approach to multi-objective optimization is linear scalarization, i.e., using the weighted sum of the individual objective functions to pose a single optimization problem [9]. The set of Pareto-optimal solutions is then approximated by exploring different scalarization weights [5], [8], [28]- [30]. However, finding useful weights is often challenging [8], [9], [31], [32].…”

Section: B Related Workmentioning

confidence: 99%

Online Multi-Robot Task Assignment with Stochastic Blockages

Wilde

Alonso–Mora

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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We study the problem of finding statistically distinct plans for stochastic planning and task assignment problems such as online multi-robot pickup and delivery (MRPD) when facing multiple competing objectives. In many real-world settings robot fleets do not only need to fulfil delivery requests, but also have to consider auxiliary objectives such as energy efficiency or avoiding human-centered work spaces. We pose MRPD as a multiobjective optimization problem where the goal is to find MRPD policies that yield different trade-offs between given objectives. There are two main challenges: 1) MRPD is computationally hard, which limits the number of trade-offs that can reasonably be computed, and 2) due to the random task arrivals, one needs to consider statistical variance of the objective values in addition to the average. We present an adaptive sampling algorithm that finds a set of policies which i) are approximately optimal, ii) approximate the set of all optimal solutions, and iii) are statistically distinguishable. We prove completeness and adapt a state-of-the-art MRPD solver to the multi-objective setting for three example objectives. In a series of simulation experiments we demonstrate the advantages of the proposed method compared to baseline approaches and show its robustness in a sensitivity analysis. The approach is general and could be adapted to other multi-objective task assignment and planning problems under uncertainty.

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Human-Centric Active Perception for Autonomous Observation

Cited by 12 publications

References 22 publications

A Review of NASA Human-Robot Interaction in Space

A Review of NASA Human-Robot Interaction in Space

Error-Bounded Approximation of Pareto Fronts in Robot Planning Problems

Online Multi-Robot Task Assignment with Stochastic Blockages

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