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For robots to seamlessly interact with humans, we first need to make sure that humans and robots understand one another. Diverse algorithms have been developed to enable robots to learn from humans (i.e., transferring information from humans to robots). In parallel, visual, haptic, and auditory communication interfaces have been designed to convey the robot’s internal state to the human (i.e., transferring information from robots to humans). Prior research often separates these two directions of information transfer, and focuses primarily on either learning algorithms or communication interfaces. By contrast, in this survey we take an interdisciplinary approach to identify common themes and emerging trends that close the loop between learning and communication. Specifically, we survey state-of-the-art methods and outcomes for communicating a robot’s learning back to the human teacher during human-robot interaction. This discussion connects human-in-the-loop learning methods and explainable robot learning with multimodal feedback systems and measures of human-robot interaction. We find that—when learning and communication are developed together—the resulting closed-loop system can lead to improved human teaching, increased human trust, and human-robot co-adaptation. The paper includes a perspective on several of the interdisciplinary research themes and open questions that could advance how future robots communicate their learning to everyday operators. Finally, we implement a selection of the reviewed methods in a case study where participants kinesthetically teach a robot arm. This case study documents and tests an integrated approach for learning in ways that can be communicated, conveying this learning across multimodal interfaces, and measuring the resulting changes in human and robot behavior.
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This content will become publicly available on July 31, 2026
Human-Robot Communication: Utilizing Light-Based Signals to Convey Robot Operating State
The field of human-robot interaction has been rapidly expanding but an ever-present obstacle facing this field is developing accessible, reliable, and effective forms of communication. It is often imperative to the efficacy of the robot and the overall human-robot interaction that a robot be capable of expressing information about itself to humans in the environment. Amidst the evolving approaches to this obstacle is the use of light as a communication modality. Light-based communication effectively captures attention, can be seen at a distance, and is commonly utilized in our daily lives. Our team explored the ways light-based signals on robots are being used to improve human understanding of robot operating state. In other words, we sought to determine how light-based signals are being used to help individuals identify the conditions (e.g., capabilities, goals, needs) that comprise and dictate a robot’s current functionality. We identified four operating states (e.g., “Blocked”, “Error”, “Seeking Interaction”, “Not Seeking Interaction”) in which light is utilized to increase individuals’ understanding of the robot’s operations. These operating states are expressed through manipulation of three visual dimensions of the onboard lighting features of robots (e.g., color, pattern of lighting, frequency of pattern). In our work, we outline how these dimensions vary across operating states and the effect they have on human understanding. We also provide potential explanations for the importance of each dimension. Additionally, we discuss the main shortcomings of this technology. The first is the overlapping use of combinations of dimensions across operating states. The remainder relate to the difficulties of leveraging color to convey information. Finally, we provide considerations on how this technology might be improved going into the future through the standardization of light-based signals and increasing the amount of information provided within interactions between agents.
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- Award ID(s):
- 2431636
- PAR ID:
- 10652685
- Publisher / Repository:
- AHFE Open Access
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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