The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
RRT-connect: An efficient approach to single-query path planning
Kuffner, J. J., & LaValle, S. M. (2000) · Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065) · doi:10.1109/robot.2000.844730 · Cited by 3,555
Mirror neurons and the simulation theory of mind-reading
Gallese, V (1998) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(98)01262-5 · Cited by 3,201
Joint action: bodies and minds moving together
SEBANZ, N., BEKKERING, H., & KNOBLICH, G. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.12.009 · Cited by 1,950
Tomasello, M. (2009) · doi:10.7551/mitpress/8470.001.0001 · Cited by 1,896
The Roles of Vision and Eye Movements in the Control of Activities of Daily Living
Land, M. F., Mennie, N. R., & Rusted, J. M. (1999) · Perception · doi:10.1068/p2935 · Cited by 1,128
Joint attention as social cognition.
Tomasello, M. (1995) · Medical Entomology and Zoology · Cited by 1,004
Eye–Hand Coordination in Object Manipulation
Johansson, R. S., Westling, G., Bäckström, A., & Flanagan, J. R. (2001) · Journal of Neuroscience · doi:10.1523/jneurosci.21-17-06917.2001 · Cited by 873
(2013) · doi:10.1109/hri.2013.6483603 · Cited by 595
Prediction in Joint Action: What, When, and Where
Sebanz, N., & Knoblich, G. (2009) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2009.01024.x · Cited by 571
Perception of Another Person's Looking Behavior
Gibson, J. J., & Pick, A. D. (1963) · The American Journal of Psychology · doi:10.2307/1419779 · Cited by 400
Research works printed but not linked: 1
- seltman, 2012, Experimental Design for Behavioral and Social Sciences
Cited by
Sorted by citations
Items 201–227 of 227
Supporting Adaptive Coordination in Human-Autonomy-Teams: A Conceptual FrameworkPreprint
Nols, T., Ulfert, A., & Gevers, J. (2024) · doi:10.31234/osf.io/ytced
Singh, M., & Arora, N. (2024) · Advances in hospitality, tourism and the services industry (AHTSI) book series · doi:10.4018/979-8-3693-4042-4.ch005
Papanagiotou, D., Manitsaris, S., & Glushkova, A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5061779
3D Gaze Estimation via Binocular Disparity and Feature Refinement
Wang, C., Chen, Q., Tian, Y. Z., & Yang, J. (2024) · International Conference on Big Data Computing and Communications (BigCom) · doi:10.1109/bigcom65357.2024.00021
“For example, human-computer interaction [3]: intelligent devices can be controlled by tracking human eye movement; clinical psychology [4]: observing patient eye movements can help predict their psychological state; marketing [5]: analyzing the products customers view allows for inference of their…”
Hao, C., Rußwinkel, N., Haeufle, D. F. B., & Beckerle, P. (2025) · IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) · doi:10.1109/cogsima64436.2025.11079490
A Modular and Behavioural Framework for Adaptive Robotic Execution and Planning Using NOVAAS
Marques, F., Pamies-teixeira, J. J., Prates, P., Orio, G. D., Faustino, P., Reis, M., Lourenço, A., Maló, P., & Reis, M. J. C. S. (2025) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot65416.2025.00167
“Anticipatory control based on gaze prediction and shared autonomy has also been proposed to improve collaboration fluidity [9].”
André, P., Grand, M., Pellier, D., & Jambon, F. (2025) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3733155.3733191
6D Pose Tracking for Adaptive AR-Mediated Human-Robot Collaboration
Ajikumar, A., Wen, B., & Moghaddam, M. (2025) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11163977
“Instead, most existing methods rely on predefined areas of interest or static gaze-object associations [9], [10], making them less practical in HRC scenarios where objects change position and orientation continuously.”
Jelínek, M., Asadi, A., Bech, C. W., & Fischer, K. (2025) · IEEE RO-MAN · doi:10.1109/ro-man63969.2025.11217723
“Timing also plays an important role in human-robot interactions; for instance, when establishing a referent, the appropriate timing of speech, gaze and gesture can be crucial for participants to identify the referent of an instruction [21], [22], [23].”
GEAR: Gaze-Enabled Human-Robot Collaborative Assembly
Shahid, A. A., Moroncelli, A., Brščić, D., Kanda, T., & Roveda, L. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247310
“Prior studies have shown that gaze cues can improve shared attention and fluency during handovers [18], and provide contextual cues that enhance the robot’s understanding of human intentions [16].”
Learning-Based Safety-Aware Task Scheduling for Efficient Human-Robot Collaboration
Faroni, M., Spanò, A., Zanchettin, A. M., & Rocco, P. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3648605
“Existing methods to addressthischallengehavefocusedonoptimizingrobotschedul-ing[2],[3],human-awarepathplanning[4],[5],ortrajectory adjustments based on operator intent [6], [7].”
Lightweight HRI Task Classification from Synthetic Logs
Qiang, Y. (2025) · IEEE International Conference on Big Data and Artificial Intelligence (BDAI) · doi:10.1109/bdai66031.2025.11325211
“Precise task classification can give rise to a new generation of intelligent applications, such as proactive safety interventions, context-aware assistance based on predicting user intent [4], and dynamic workflow optimization [5].”
Vorhersage menschlicher Bewegungen in der kollaborativen Mensch-Roboter-Montage
Karbouj, B., Feikes, S., & Krüger, J. (2026) · Zeitschrift für wirtschaftlichen Fabrikbetrieb · doi:10.1515/zwf-2026-1001
“Da-mit diese Potenziale ausgeschöpft werden können, muss der Roboter in der Lage sein, menschliche Bewegungen nicht nur zu er - kennen, sondern möglichst frühzeitig zu antizipieren, um Reaktionszeiten zu ver-kürzen und Unterbrechungen im Arbeits-ablauf zu minimieren [1].”
Bultmann, S., Memmesheimer, R., Nogga, J., Hau, J., & Behnke, S. (2026) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii64115.2026.11404618
“Huang and Mutlu [2] present a system that proactively performs actions to assist a human in a collaborative task based on the user’s gaze patterns.”
Predicting and Sharing of Multi-Human Intentions in Multi-Robot Systems: A Bayesian Approach
Verhees, E., van der Vorst, S., Elfring, J., Reniers, M., van de Molengraft, R., & Torta, E. (2026) · International Journal of Social Robotics · doi:10.1007/s12369-026-01382-2
Wen, J., Zhang, X., & Chew, J. Y. (2026) · Advanced Robotics · doi:10.1080/01691864.2026.2626369
“Action anticipation is critical for applications like human-robot collaboration [1] and robot motion generation [2].”
Dermy, O. (2018) · theses.fr (ABES) · doi:10.70675/66c4dacezf631z43bez873cz81160a1d5dda
“l’interaction [221], ou encore afin d’anticiper l’action du partenaire d’un robot, comme dans [107], où le robot utilise cette anticipation pour effectuer une tâche de manière “proactive”.”
Cao, H. J., Chen, M., Peng, W., & Lee, H. R. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791602
“Given the growing importance of human–machine collaboration [9, 42, 48, 66, 88, 89, 102, 124, 130], this paper adopts a perspective from organization studies to examine potential power dynamics between humans and technologies and to foster more genuine and effective collaboration.”
TOMINE, T., & Hayashi, K. (2026) · OSF Preprints (OSF Preprints)
Kenny, J. P., Bourgault, S., & Mitterberger, D. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812995
“Huang and Mutlu, on the other hand, use AruCo markers to coordinate an AR glasses environment with the physical world so they can accurately track human gaze and enable a robot to anticipate user intent [27].”
Collaborative Robot Technologies for Industrial Tasks
Li, W., Zaatari, S. E., Marei, M., Usman, Z., & Pham, D. T. (2026) · Springer series in advanced manufacturing · doi:10.1007/978-3-032-20817-0_2
Solving Temporally Structured Decision Problems in Anticipatory Agent Networks
Skulimowski, A. M. J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36226-1_35
HINT-Bench: Human INTention Recognition Benchmark for Social Robotics
Bo, V., Garrell, A., & Sanfeliu, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-30156-7_28
LLM-Assisted Planning Failure Resolution in Human-Robot Collaboration
Raggioli, L., Muto, E. C., Esposito, R., & Rossi, S. (2026) · IEEE RO-MAN · doi:10.1109/ro-man72286.2026.11714833
“This extends beyond simply parsing instructions to inferring human intent and predicting future actions [19].”
Human–Robot Interaction and Ergonomics
Heinold, E., Rosen, P. H., & Wischniewski, S. (2026) · Springer tracts in advanced robotics · doi:10.1007/978-3-032-19374-2_1
Situated proactive task generation for embodied agents
Xu, H., Li, C., Yuan, X., & Liu, H. (2026) · C2 Science and Technology · doi:10.26599/c2st.2026.9700006
Manzardo, M., Ciaghi, D., Mezentseva, A., de Angelis, M., Fraboni, F., Vidoni, R., & Gualtieri, L. (2026) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2026.103455
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
