Exploring the Impact of Machine-Learned Predictions on Feedback from an Artificial Limb

Parker, Adam S. R.; Edwards, Ann L.; Pilarski, Patrick M.

doi:10.1109/icorr.2019.8779424

Cited by 4 publications

(5 citation statements)

References 21 publications

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“…For instance, recent studies have used vibrotactile feedback in the form of a vibrating sleeve (132), pressure spots in a prosthetic socket (131), or friction modulation on the surface of a touch screen (163). Other studies have explored using vibrotactile feedback to communicate emotion, imitating social touch with patterns of haptic sensations on the arm (164,165), or to communicate learned predictions of upcoming hazards (166,167). Without disturbing the task flow, vibrotactile stimulation and haptic cues can significantly increase the information communicated to the person by the machine, improving the pair's collaborative potential.…”

Section: Multimodal Dialoguementioning

confidence: 99%

Embodied Communication: How Robots and People Communicate Through Physical Interaction

Kalinowska

Pilarski

Murphey

2023

Annu. Rev. Control Robot. Auton. Syst.

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Early research on physical human–robot interaction (pHRI) has necessarily focused on device design—the creation of compliant and sensorized hardware, such as exoskeletons, prostheses, and robot arms, that enables people to safely come in contact with robotic systems and to communicate about their collaborative intent. As hardware capabilities have become sufficient for many applications, and as computing has become more powerful, algorithms that support fluent and expressive use of pHRI systems have begun to play a prominent role in determining the systems’ usefulness. In this review, we describe a selection of representative algorithmic approaches that regulate and interpret pHRI, describing the progression from algorithms based on physical analogies, such as admittance control, to computational methods based on higher-level reasoning, which take advantage of multimodal communication channels. Existing algorithmic approaches largely enable task-specific pHRI, but they do not generalize to versatile human–robot collaboration. Throughout the review and in our discussion of next steps, we therefore argue that emergent embodied dialogue—bidirectional, multimodal communication that can be learned through continuous interaction—is one of the next frontiers of pHRI. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 14 is May 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Multimodal Dialoguementioning

confidence: 99%

Embodied Communication: How Robots and People Communicate Through Physical Interaction

Kalinowska

Pilarski

Murphey

2023

Annu. Rev. Control Robot. Auton. Syst.

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“…The impact of feedback from an adaptive prosthetic is quantified in work by Parker et al [81]. In their work, three different kinds of feedback were used to supply a human with information about how best to control the movements of a wearable robot in the form of a supernumerary limb (see Fig.…”

Section: Models Shared Agency and Feedbackmentioning

confidence: 99%

“…The two capacity functions of interest in Parker et al [81] measured: the current drawn by the motors due to impacts with the work space walls, and the number of times the human was able to use the arm to fully traverse the work space in the given time. On different trials, feedback from the device was either absent, delivered mechanistically upon contact with the walls, or delivered proportional to learned predictions about impacts with the walls.…”

Section: Models Shared Agency and Feedbackmentioning

confidence: 99%

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Communicative capital: a key resource for human–machine shared agency and collaborative capacity

Mathewson

Parker

Sherstan

et al. 2022

Neural Comput & Applic

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In this work, we present a perspective on the role machine intelligence can play in supporting human abilities. In particular, we consider research in rehabilitation technologies such as prosthetic devices, as this domain requires tight coupling between human and machine. Taking an agent-based view of such devices, we propose that human–machine collaborations have a capacity to perform tasks which is a result of the combined agency of the human and the machine. We introduce communicative capital as a resource developed by a human and a machine working together in ongoing interactions. Development of this resource enables the partnership to eventually perform tasks at a capacity greater than either individual could achieve alone. We then examine the benefits and challenges of increasing the agency of prostheses by surveying literature which demonstrates that building communicative resources enables more complex, task-directed interactions. The viewpoint developed in this article extends current thinking on how best to support the functional use of increasingly complex prostheses, and establishes insight toward creating more fruitful interactions between humans and supportive, assistive, and augmentative technologies.

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“…When the internal predictions made by a system were made visible in their raw form to a human partner, participants were able to both understand and fruitfully make decisions in the shared environment (Edwards et al, 2016b). In a task where a human collaborator was in direct control of a robotic third arm, when predictions of motor torque (a proxy for impact in a workspace) were mapped to vibration on a human collaborator's arm, the user was better able to perform precise positioning tasks (Parker et al, 2019). Similarly, by using audio cues to communicate a machine's internal prediction of the value of objects in a Virtual Reality environment, humans were able to perform better in a foraging task (Pilarski et al, 2019).…”

Section: Predictions: Communication Of Inner Estimatesmentioning

confidence: 99%

Prediction, Knowledge, and Explainability: Examining the Use of General Value Functions in Machine Knowledge

Kearney¹,

Günther²,

Pilarski³

2022

Front. Artif. Intell.

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Within computational reinforcement learning, a growing body of work seeks to express an agent's knowledge of its world through large collections of predictions. While systems that encode predictions as General Value Functions (GVFs) have seen numerous developments in both theory and application, whether such approaches are explainable is unexplored. In this perspective piece, we explore GVFs as a form of explainable AI. To do so, we articulate a subjective agent-centric approach to explainability in sequential decision-making tasks. We propose that prior to explaining its decisions to others, an self-supervised agent must be able to introspectively explain decisions to itself. To clarify this point, we review prior applications of GVFs that involve human-agent collaboration. In doing so, we demonstrate that by making their subjective explanations public, predictive knowledge agents can improve the clarity of their operation in collaborative tasks.

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Exploring the Impact of Machine-Learned Predictions on Feedback from an Artificial Limb

Cited by 4 publications

References 21 publications

Embodied Communication: How Robots and People Communicate Through Physical Interaction

Embodied Communication: How Robots and People Communicate Through Physical Interaction

Communicative capital: a key resource for human–machine shared agency and collaborative capacity

Prediction, Knowledge, and Explainability: Examining the Use of General Value Functions in Machine Knowledge

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