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Anticipatory robot control for efficient human-robot collaboration
Abstract
Efficient collaboration requires collaborators to monitor the behaviors of their partners, make inferences about their task intent, and plan their own actions accordingly. To work seamlessly and efficiently with their human counterparts, robots must similarly rely on predictions of their users' intent in planning their actions. In this paper, we present an anticipatory control method that enables robots to proactively perform task actions based on anticipated actions of their human partners. We implemented this method into a robot system that monitored its user's gaze, predicted his or her task intent based on observed gaze patterns, and performed anticipatory task actions according to its predictions. Results from a human-robot interaction experiment showed that anticipatory control enabled the robot to respond to user requests and complete the task faster-2.5 seconds on average and up to 3.4 seconds-compared to a robot using a reactive control method that did not anticipate user intent. Our findings highlight the promise of performing anticipatory actions for achieving efficient human-robot teamwork.
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“Prior research in the domain of human-robot collaboration has demonstrated how a robot can anticipate its human collaborator's task needs based on the observation of the collaborator's eye gaze and provide anticipatory robot assistance [29].”
From Understanding User Reliance on AI in Assisted Decision-Making (2022) · cites this paper as [29]
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BackgroundMapping Technological Profile of Collaborative Robots by Patent Analysis
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BackgroundeyeNotate: Interactive Annotation of Mobile Eye Tracking Data Based on Few-Shot Image Classification
Barz, M., Bhatti, O. S., Alam, H., Nguyen, D. M. H., Altmeyer, K., Malone, S., & Sonntag, D. (2025) · Journal of Eye Movement Research · doi:10.3390/jemr18040027 · Cited by 2
“This bears the potential to boost the efficiency in research based on eye tracking by automating the time-consuming and expensive data annotation process [16] and to facilitate novel real-time adaptive human–computer interaction [21,22].”
Gaze-Language Alignment for Zero-Shot Prediction of Visual Search Targets from Human Gaze Scanpaths
Mondal, S., Sendhilnathan, N., Zhang, T., Liu, Y., Proulx, M. J., Iuzzolino, M. L., Qin, C., & Jonker, T. R. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00263 · Cited by 3
“We sub-* Work done during an internship at Meta Reality Labs Research”
Accessing Your Navigation Plans! Human-Robot Intention Transfer Using Eye-Tracking Glasses
Teja, C. R., Andreasson, H., Schindler, M., Palm, R., & Lilienthal, A. J. (2018) · Advances in transdisciplinary engineering · doi:10.3233/978-1-61499-902-7-253 · Cited by 2
Towards Adaptive System Behavior and Learning Processes for Active Exoskeletons
Otten, B., Hoffmann, N., & Weidner, R. (2020) · Lecture notes in production engineering · doi:10.1007/978-3-662-62138-7_48 · Cited by 2
Human Behavior Analysis in Human-Robot Cooperation with AR Glasses
Owaki, K., Techasarntikul, N., & Shimonishi, H. (2023) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar59233.2023.00016 · Cited by 2
“For example, Weber et al. [17] and Huang et al. [10] predicted human behavior based on gaze and applied it to robot control.”
BackgroundGaze Supervision for Mitigating Causal Confusion in Driving Agents
Biswas, A., Pardhi, B. A., Chuck, C., Holtz, J., Niekum, S., Admoni, H., & Allievi, A. (2024) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55156.2024.10588498 · Cited by 2
“In robotics, gaze has been used widely from understand people’s preferences over objects [7], [8] for facilitating smoother handovers, to facilitating shared autonomy by inferring goals in an assistive feeding task [9] or the next step in a multi-stage manipulation task [10].”
MethodHayashi, K. (2024) · International Journal of Social Robotics · doi:10.1007/s12369-024-01168-4 · Cited by 2
Zhang, J., You, F., Yang, J., Zhang, J., Wang, P., Wang, H., & Luximon, Y. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2440985 · Cited by 2
Luecha, T., Yeoh, W. L., Yang, Y., Choi, J., Loh, P. Y., & Muraki, S. (2025) · Journal of Robotics · doi:10.1155/joro/4988295 · Cited by 2
Gaze-supported Large Language Model Framework for Bi-directional Human-Robot Interaction
Rüppel, J. V., Rudenko, A., Schreiter, T., Magnusson, M., & Lilienthal, A. J. (2025) · IEEE RO-MAN · doi:10.1109/ro-man63969.2025.11217612 · Cited by 2
“Furthermore, these frameworks are often designed and optimized for a specific task [5], executed by a specific robot, lacking the ability to adapt quickly to different scenarios.”
Human–robot cohabitation in industry
Aßmann, U., Chen, L., Ebert, S., Göhringer, D., Grzelak, D., Hidalgo, D. F. M., Johannsmeier, L., Haddadin, S., Mey, J., & Podlubne, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821343-8.00013-7 · Cited by 2
Recognition and Identification of Intentional Blocking in Social Navigation
Mirsky, R., & Shpiro, E. (2024) · doi:10.1145/3648536.3648549 · Cited by 2
Knowledge Management for Rapidly Extensible Collaborative Robots
Johnson, M., Beane, M., Mindell, D. A., & Ryan, J. C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22660-2_37 · Cited by 2
EAN: Error Attenuation Network for Long-term Human Motion Prediction
Xu, J., Lan, X., Li, J., Chen, X., & Zheng, N. (2019) · China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI) · doi:10.1109/cchi.2019.8901951 · Cited by 2
“In the field of human-robot interaction, predicting human movement can help robots plan responses ahead of schedule [5].”
BackgroundA simulated dataset for proactive robot task inference from streaming natural language dialogues
Xu, H., Li, C., Yuan, X., Tao, Z., & Liu, H. (2025) · Scientific Data · doi:10.1038/s41597-025-05727-w · Cited by 2
Surendran, V., & Wagner, A. R. (2022) · Autonomous Robots · doi:10.1007/s10514-022-10074-5 · Cited by 1
“HuangandMutlutrackedhumangazetodevelopanantici-patory control method enabling robots to proactively perform tasks based on predicted intent (Huang & Mutlu, 2016).”
MethodBenefits of Multi-Objective Trajectory Adaptation in Close-Proximity Human-Robot Interaction
Chuy, O., Sapra, H., Tan, X. Z., Ravichandar, H. C., & Chernova, S. (2023) · IEEE RO-MAN · doi:10.1109/ro-man57019.2023.10309445 · Cited by 1
“Indeed, anticipatory methods are known to be more task efficient than reactive methods [4].”
Background“Bad Idea, Right?” Exploring Anticipatory Human Reactions for Outcome Prediction in HRI
Parreira, M. T., Lingaraju, S. G., Ramirez-Aristizabal, A. G., Bremers, A. W. D., Saha, M., Kuniavsky, M., & Ju, W. G. (2024) · IEEE RO-MAN · doi:10.1109/ro-man60168.2024.10731310 · Cited by 1
“Recent works, however, have been exploring how to leverage humans’ ability to (visibly) react to their environment and improve robotic systems [11, 5, 13, 8].”
BackgroundNan, Z., Jia, L., & Xiao, B. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3512380 · Cited by 1
“…vision [1], [2], [3], cognition [4], psychology [5], and philosophy [6], since it is crucial to explore human intelligence and supports various applications such as intention understanding [7], human-robot cooperation [8], autism diagnosing [9], student distraction monitoring [10], and so on.”
Da, N. L. H., Miura, J., & Hayashi, K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92591-7_2 · Cited by 1
Eye-Tracking-Driven Shared Control for Robotic Arms: Wizard of Oz Studies to Assess Design Choices
Fischer-Janzen, A., Wendt, T. M., Görlich, D., & Laerhoven, K. V. (2026) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3796524 · Cited by 1
“Huang and Mutlu demonstrated an example of such controls, in which ingredients were predicted by gaze patterns [37].”
Rudaz, D., Carreras, B. N., Merlino, S., Due, B. L., & Brown, B. A. T. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791708 · Cited by 2
“Immediately after Laura’s hand appears in front of the smart-phone’s camera, the volunteer utters “feel around” (l.26; see also Figure 4 right).”
Thing Ethnography in a Factory: Exploring Emergent and Dynamic Relations of Cobots and Workers
Cho, H., Seo, J. A., Sohn, Y., & Lee, H. R. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790576 · Cited by 1
“For instance, studies have shown that robots can improve task efficiency by anticipating human actions in handover situations [53], or that designing object transfers to highlight the handle improves usability and acceptance [83].”
Manitta, M., Jayasuriya, M., & Liu, D. (2023) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case56687.2023.10260582 · Cited by 1
Human Resource Management Insights on Social Robots Integration in the Workplace
Singh, M., & Arora, N. (2024) · Advances in logistics, operations, and management science book series · doi:10.4018/979-8-3693-2849-1.ch007 · Cited by 1
Probabilistic Inference of Human Capabilities from Passive Observations
Tisnikar, P., Canal, G., & Leonetti, M. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801640 · Cited by 1
The Case of the Curious Robot: On the Social Viability of Curious Behavior in Non-Human Agents
Weatherwax, K., Dooley, D., Carstensdóttir, E., & Takayama, L. A. (2024) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3652988.3673920 · Cited by 1
Toward Semi-Autonomous Robotic Arm Manipulation Operator Intention Detection From Force Data
Alharthi, A. S., Tokatlı, O., Pulgarín, E. J. L., & Herrmann, G. (2024) · IEEE Access · doi:10.1109/access.2024.3523325 · Cited by 1
“Building on these advancements, anticipatory control methods have been explored, allowing robots to proactively perform tasks based on predicted human actions observed through gaze patterns [14].”
Guevara, I. (2025) · Social interaction in learning and development · doi:10.1007/978-3-031-87673-8_7 · Cited by 1
Recognition and Anticipation of Human Actions in a Human-Robot Collaborative Assembly Scenario
Zoppi, G., Forlini, M., Palmieri, G., & Neto, P. (2025) · IEEE RO-MAN · doi:10.1109/ro-man63969.2025.11217712 · Cited by 1
“Some studies extend action anticipation to multiple possible actions [16], [17].”
Chang, M. L., & Thomaz, A. L. (2021) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3434074.3446355 · Cited by 1
“Some techniques focus on enabling the robot to understand the human teammate’s intent [16, 17, 20, 25, 30, 37] and others focus on the robot showing its intent [1, 2, 11, 13, 27].”
BackgroundSurendran, V., & Wagner, A. R. (2021) · IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO) · doi:10.1109/arso51874.2021.9541546 · Cited by 1
Attention Analysis for Assistance in Assembly Processes
Paletta, L. (2021) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-72632-4_23
“A typical, currently available application is anticipatory control with human-in-the-loop architecture [3] to enable robots to perform task actions that are specifically based on recently observed gaze patterns to anticipate actions of their human partners according to its predictions.”
Background(2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821343-8.00030-7
Voxel Based Motion Prediction for Efficient HRC Utilizing Latent Space
Spielbauer, N., Reichard, D., Bolano, G., Stelzer, A., Suppa, M., Leske, M., Steinbronn, J., Rothe, D., Roennau, A., & Dillmann, R. (2023) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case56687.2023.10260680
“For example, the tracking of the user’s gaze can be used to predict intents in order to proactively perform task actions based on the prediction [5].”
MethodSistema de interacción humano-robot basado en la mirada para la manipulación de objetos
Menéndez, E., Hernandez-Vicen, J., de la Casa, S. M., Monje, C. A., & Balaguer, C. (2023) · doi:10.17979/spudc.9788497498609.661
Metadata
- Authors
- Huang, C.-M., & Mutlu, B.
- Affiliations
- Yale University (US)
- University of Wisconsin–Madison (US)
- Venue
- 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI)
- Publisher
- IEEE
- Published
- 2016-03
- Pages
- 83-90
- OpenAlex
- W4229726131
- Cited by
- 227
- References
- 31
- Citations by year
- 2026: 17 · 2025: 28 · 2024: 31 · 2023: 27 · 2022: 28 · 2021: 21 · 2020: 24 · 2019: 22 · 2018: 20 · 2017: 8 · 2016: 1
- FWCI
- 28.71
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM/IEEE International Conference on Human-Robot Interaction (HRI) (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- anticipatory control, human-robot collaboration, gaze prediction, human-robot interaction, task completion time
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