The DOI matches its Crossref record; 21 of the 22 references it cites were found in a registry or an index.
Programmable self-assembly in a thousand-robot swarm
Abstract
Large-scale robotic self-assembly: When individuals swarm, they must somehow communicate to direct collective motion. Swarms of robots need to deal with outliers, such as robots that move more slowly than the rest. Rubenstein et al. created a large swarm of programmed robots that can form collaborations using only local information. The robots could communicate only with nearby members, within about three times their diameter. They were able to assemble into complex preprogrammed shapes. If the robots' formation hit snags when they bumped into one another or because of an outlier, additional algorithms guided them to rectify their collective movements. Science , this issue p. 795
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Whitesides, G. M., & Grzybowski, B. A. (2002) · Science · doi:10.1126/science.1070821 · Cited by 7,550
Folding DNA to create nanoscale shapes and patterns
Rothemund, P. W. K. (2006) · Nature · doi:10.1038/nature04586 · Cited by 7,474
Range-free localization schemes for large scale sensor networks
He, T., Huang, C., Blum, B. M., et al. (2003) · Proceedings of the 28th Annual International Conference on Mobile Computing And Networking · doi:10.1145/938985.938995 · Cited by 2,658
The Rotary Motor of Bacterial Flagella
Berg, H. C. (2003) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.72.121801.161737 · Cited by 1,437
Coordinating Hundreds of Cooperative, Autonomous Vehicles in Warehouses
Wurman, P. R., D'Andrea, R., & Mountz, M. (2007) · The AI Magazine · doi:10.1609/aimag.v29i1.2082 · Cited by 1,151
Organizing a Global Coordinate System from Local Information on an Ad Hoc Sensor Network
Nagpal, R., Shrobe, H. E., & Bachrach, J. (2003) · Lecture notes in computer science · doi:10.1007/3-540-36978-3_22 · Cited by 759
Self-citationCoordinating hundreds of cooperative, autonomous vehicles in warehouses
Wurman, P. R., & Mountz, M. (2008) · Cited by 566
The making of a fly; The genetics of animal design
(1992) · International Journal of Biochemistry · doi:10.1016/0020-711x(92)90084-e · Cited by 407
Autonomous Self-Assembly in Swarm-Bots
Groβ, R., Michael Bonani, C., Francesco Mondada, C., & Dorigo, M. (2006) · IEEE Transactions on Robotics · doi:10.1109/tro.2006.882919 · Cited by 351
Goldstein, S. C., Campbell, J. D., & Mowry, T. C. (2005) · Computer · doi:10.1109/mc.2005.198 · Cited by 327
Flagged first, then by citations
1 of the 22 references couldn’t be matched to a known paper.
Research works printed but not linked: 8
- S. Camazine et al . Self-Organization in Biological Systems (Princeton Univ. Press Princeton NJ 2003).
- M. Kirschner J. Gerhart The Plausibility of Life: Resolving Darwin’s Dilemma (Yale Univ. Press New Haven CT 2005).
- E. William G Mermoud A. Martinoli in Proceedings of the 2010 International Conference on Robotics and Automation (Anchorage AK May 2010) pp. 1438–1445.
- M. Rubenstein C. Ahler R. Nagpal in Proceedings of the 2012 IEEE International Conference on Robotics and Automation (St. Paul MN May 2012) pp. 3293–3298.
- P. Vartholomeos E. Papadopoulos in Proceedings of the 2006 IEEE International Conference on Robotics and Automation (Orlando FL May 2006) pp. 649–654.
- D. Moore J. Leonard D. Rus S. Teller in The 2nd International Conference on Embedded Networked Sensor Systems (Baltimore November 2004) pp. 50–61.
- J. Werfel Y. Bar-Yam D. Rus R. Nagpal in Proceedings of the 2006 IEEE International Conference on Robotics and Automation (Orlando FL May 2006) pp. 2787–2794.
- M. De Rosa S. Goldstein P. Lee J. Campbell P. Pillai in Proceedings of the 2006 IEEE International Conference on Robotics and Automation (Orlando FL May 2006) pp. 1462–1468.
Cited by
Sorted by citations
Items 701–800 of 1,262
Adaptive phototaxis of a swarm of mobile robots using positive and negative feedback self-alignment
Mirhosseini, Y., Zion, M. Y. B., Dauchot, O., et al. (2022) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3512290.3528816 · Cited by 6
Abdelli, A., Amamra, A., & Yachir, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-12097-8_14 · Cited by 6
Suzuki, M., Iida, Y., Tsukui, Y., et al. (2024) · Advanced Intelligent Systems · doi:10.1002/aisy.202300517 · Cited by 6
Ceron, S., Xiao, W., & Rus, D. L. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610540 · Cited by 6
“This general form of the swarmalator model is used because it is relevant in macro-scale [20], [21] and micro-scale [22], [4], [23], [24] robot swarms in which the phase-dependent spatial interactions are global and local.”
MethodRobots That Think Fast and Slow: An Example of Throwing the Ball Into the Basket
Li, T. S., Kuo, P., Ho, Y.-F., Liu, C.-Y., Yu, T.-C., Ye, Y.-T., Chang, C.-Y., Chen, G.-Y., Chien, C.-W., Chen, W., et al. (2016) · IEEE Access · doi:10.1109/access.2016.2601167 · Cited by 6
“They are committing themselves to promoting creativity [36], interaction ability [37], natural language speaking ability [38], and collective intelligence [39] in the robot, and so on.”
BackgroundEffects of model parameter interactions on Naïve creatures' success of learning to cross a highway
Ławniczak, A. T., Ly, L., Yu, F., et al. (2016) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2016.7743860 · Cited by 6
“Advances in the areas of autonomous agents, multi-agent systems, and various biomimetic computational tools have allowed us to model, simulate, and demonstrate that a large number of simple, nonexpensive autonomous robots/agents that rely on relatively crude local information about one another and their environment, a…”
BackgroundMulti‐vehicle formation in a controllable force field with non‐identical controller gains
Jain, A., Ghose, D., & Menon, P. P. (2018) · IET Control Theory and Applications · doi:10.1049/iet-cta.2017.0651 · Cited by 6
Choi, T., Pavlic, T. P., & Richa, A. W. (2017) · doi:10.1109/coase.2017.8256320 · Cited by 6
Liu, Z., Wang, H., Xu, L., et al. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460660 · Cited by 6
“In [9], [10], a gradient-based algorithm is established to formulate the global coordinate system and the self-reconfiguration problem of a large-scale swarm has been investigated.”
MethodOptimization of Swarm Behavior Assisted by an Automatic Local Proof for a Pattern Formation Task
Coppola, M., & de Croon, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00533-7_10 · Cited by 5
“This proof, combined with the fact that it can deal with very limited agents (anonymous, homogeneous, memoryless, with limited range sensing, and without needing any communication, global knowledge, or seed agents) moving in space, sets the work in [1] apart from other works such as [13, 19, 15].”
BackgroundState-Dependent Dynamics of the Lohe Matrix Ensemble on the Unitary Group under the Gradient Flow
Kim, D. (2020) · SIAM Journal on Applied Dynamical Systems · doi:10.1137/19m1294605 · Cited by 6
Asymptotic behavior of gradient flows on the unit sphere with various potentials
Huh, H., & Kim, D. (2020) · Journal of Differential Equations · doi:10.1016/j.jde.2020.07.016 · Cited by 6
The near and far of a pair of magnetic capillary disks.
Koens, L., Wang, W., Sitti, M., et al. (2019) · MPG.PuRe (Max Planck Society) · doi:10.17863/cam.36335 · Cited by 6
Lightweight Audio Source Localization for Swarm Robots
Majid, A. Y., van der Horst, C., van Rietbergen, T., et al. (2021) · IEEE Consumer Communications & Networking Conference (CCNC) · doi:10.1109/ccnc49032.2021.9369572 · Cited by 6
“The value of swarm robotic systems lays in utilizing many simple and cheap robots to tackle complex tasks [1, 2, 3, 4].”
BackgroundRFID-based tangible and touch tabletop for dual reality in crisis management context
Merrad, W., Héloir, A., Kolski, C., & Krüger, A. (2021) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-021-00370-2 · Cited by 7
Centralised connectivity-preserving transformations for programmable matter: A minimal seed approach
Connor, M., Michail, O., & Potapov, I. (2022) · Theoretical Computer Science · doi:10.1016/j.tcs.2022.09.016 · Cited by 6
A composite micromotor driven by self-thermophoresis and Brownian rectification*
Lou, X., Yu, N., Chen, K., et al. (2021) · Chinese Physics B · doi:10.1088/1674-1056/ac2727 · Cited by 6
Connected coordinated motion planning with bounded stretch
Fekete, S. P., Keldenich, P., Kosfeld, R., et al. (2023) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-023-09626-5 · Cited by 6
Zhang, L., Fu, Z.-H., Liu, H., et al. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560863 · Cited by 6
“The serial docking approaches select a robot as the seed and build the desired structure by connecting other robots to the seed in a serial manner, such as the seed-initiated rule-based methods [8], [10].”
Wang, M., Liu, W., & Wen, C. (2022) · Sensors · doi:10.3390/s22155590 · Cited by 6
Hachmon, G., Mamet, N., Sasson, S., et al. (2016) · Artificial Life · doi:10.1162/artl_a_00194 · Cited by 6
A Soft-Bodied Modular Reconfigurable Robotic System Composed of Interconnected Kilobots
Pratissoli, F., Reina, A., Lopes, Y. K., Sabattini, L., & Groß, R. (2019) · International Symposium on Multi-Robot and Multi-Agent Systems (MRS) · doi:10.1109/mrs.2019.8901061 · Cited by 6
Fusing Swarm Intelligence and Self-Assembly for Optimizing Echo State Networks
Martin, C. E., & Reggia, J. A. (2015) · Computational Intelligence and Neuroscience · doi:10.1155/2015/642429 · Cited by 6
Ishida, T. (2025) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-025-02262-z · Cited by 5
“A notable example is the work conducted by Nagpal’s research group [21–23], which successfully demonstrated self-organized morphogenesis, including star formation, using approximately 1,000 simple robots known as Kilobots.”
InfluentialReliability and security: from swarm robots to AI agents
Yan, Y., Xie, Y., Tang, J., Li, Y., & Jin, Y. (2025) · Journal of Reliability Science and Engineering · doi:10.1088/3050-2454/adea7a · Cited by 6
Engineering morphogenesis of cell clusters with differentiable programming
Deshpande, R., Mottes, F., Vlad, A.-D., Brenner, M. P., & Co, A. D. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00851-4 · Cited by 10
“Unlike systems guided by centralized control signals 1 , most developmental complexity arises from the interplay of local communications, sensing and information processing within individual cells, with minimal external influence 2 .”
Guo, H.-S., Li, K., Yang, J., Li, D., Liu, F., & Zeng, H. (2025) · Nature Communications · doi:10.1038/s41467-025-66395-3 · Cited by 8
Density-Driven Progressive Shape Formation for Swarm Robots in Dynamic Environments
Xiang, Y., Song, J., Zheng, Z., et al. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3640978 · Cited by 5
“This capability has wide-ranging applications in areas such as environmental monitoring [4], search and rescue [5], and infrastructure inspection [6], [7].”
GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models
Ji, W., Chen, H., Chen, M., Zhu, G., Xu, L., Gros, R., Zhou, R., Cao, M., & Zhao, S. (2026) · npj Robotics · doi:10.1038/s44182-025-00065-w · Cited by 12
“This is especially relevant for large-scale swarming systems, where collective behaviors emerge from the interactions among robots with exceedingly limited onboard resources [21–26].”
Synchronization of complex network with delayed nodes via proportional-derivative control
Gu, H., Wang, X., Lü, J., et al. (2017) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon.2017.8216971 · Cited by 5
“Over the previous two decades, complex networks theory has developed rapidly due to its potential applications in many fields such as engineering [1], biology [2] and physics [3], etc.”
BackgroundImpact of flexibility on the aggregation of polymeric macromolecules
Emamyari, S., Mirzaei, M., Mohammadinejad, S., et al. (2023) · The European Physical Journal E · doi:10.1140/epje/s10189-023-00324-4 · Cited by 5
Adaptive Shape Formation Against Swarm-Scale Variants in Robot Swarms
Li, X., Zhou, R., & Sun, G. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3414210 · Cited by 5
“Another strategy tries to characterize the desired shape as a continuous region, and then keep robots evenly distributed in this region [21], [22], [23], [24], [25].”
BackgroundTargets capture by distributed active swarms via bio-inspired reinforcement learning
Xu, K., Li, Y., Sun, J., Du, S. X., Di, X., Yang, Y., & Li, B. (2024) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-024-2494-0 · Cited by 6
Szpirer, J., Ramos, D. G., & Birattari, M. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801506 · Cited by 4
“Swarm robotics [1]–[3] has gained notable attention in the past decade [4]–[12].”
Distributed Formation Shape Control of Identity-Less Robot Swarms
Sun, G., Xu, Y., Liu, K., et al. (2025) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2025.3541304 · Cited by 5
“One representative work that can solve this problem is the edge-following strategy [18], whose motion efficiency is however low because it only allows the robots on the edge of a swarm to move while all the others must stay stationary [19].”
Cluster Dynamics in Macroscopic Photoactive Particles
Lévay, S., Katona, A., Löwen, H., et al. (2025) · Physical Review Letters · doi:10.1103/t7st-flj2 · Cited by 5
A Stochastic Approach to Shortcut Bridging in Programmable Matter
Arroyo, M. A., Cannon, S., Daymude, J. J., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66799-7_9 · Cited by 5
Computational Resources of Miniature Robots: Classification and Implications
Trenkwalder, S. M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2917395 · Cited by 5
Roli, A., Ligot, A., & Birattari, M. (2019) · Frontiers in Robotics and AI · doi:10.3389/frobt.2019.00130 · Cited by 6
Random Finite Set Theory and Centralized Control of Large Collaborative Swarms
Doerr, B. G., Linares, R., Zhu, P., & Ferrari, S. (2020) · Journal of Guidance Control and Dynamics · doi:10.2514/1.g004861 · Cited by 6
“Although the topology underlying the work is centralized, the formulation allows for a statistical model of the swarm and provides improvements in computational efficiency and flexibility (similar to the distributed optimal control methods [15–19]) when compared to traditional centralized methods [7].”
Multirobot Formation with Sensor Fusion-Based Localization in Unknown Environment
Le, A. V., Apuroop, K. G. S., Konduri, S., et al. (2021) · Symmetry · doi:10.3390/sym13101788 · Cited by 5
Grouping-Based Optimization Method for Multirobot System Pattern Formation
Wang, T., Zhang, F., Xin, J., et al. (2021) · IEEE Systems Journal · doi:10.1109/jsyst.2021.3122548 · Cited by 5
“Many articles on the pattern formation study how to effectively and stably control multiple robots to form a predefined pattern [9]–[14].”
BackgroundDistributed Self-Assembly of Cantilevers by Force-Aware Robots
Bray, E., & Groß, R. (2021) · International Symposium on Multi-Robot and Multi-Agent Systems (MRS) · doi:10.1109/mrs50823.2021.9620697 · Cited by 5
Physical Neural Cellular Automata for 2D Shape Classification
Walker, K. L., Palm, R. B., Garcia, R. M., Faíña, A., Støy, K., & Risi, S. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981214 · Cited by 8
“Clearly this method relies on the external agent for success.”
MethodAdaptive particle patterning in the presence of active synthetic nanomotors
Xie, D., Fu, S., Fu, D., et al. (2023) · Nanoscale · doi:10.1039/d3nr00462g · Cited by 5
Centralized Mission Planning for Multiple Robots Minimizing Total Mission Completion Time
Hwang, N. E., Kim, H. J., & Kim, J. G. (2023) · Applied Sciences · doi:10.3390/app13063737 · Cited by 5
Bio-inspired environmental adaptability of swarm active matter
Jin, Y., Wang, G., Yuan, D., Wang, P., Wang, J., Chen, H., Liu, L., & Zan, X. (2023) · Chinese Physics B · doi:10.1088/1674-1056/acd688 · Cited by 6
Self-assembly to synchrony of active gels
Kumar, P., Horváth, D., & Tóth, Á. (2023) · Soft Matter · doi:10.1039/d3sm00461a · Cited by 5
Emotion Components and Understanding in Humans and Machines
Bellon, J. (2023) · Technikzukünfte, Wissenschaft und Gesellschaft · doi:10.1007/978-3-658-37641-3_2 · Cited by 6
Ding, S., Aoyama, H., & Lin, D. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7025-4_40 · Cited by 5
Statistical Robotics: Controlling Multi Robot Systems Using Statistical-Physics
Shvalb, N., Hacohen, S., & Medina, O. (2024) · IEEE Access · doi:10.1109/access.2024.3406599 · Cited by 5
“Such an approach has demonstrated effectiveness, as in accomplishing self-assembly tasks [18].”
BackgroundMethodDistributed Shape Formation of Multirobot Systems via Dynamic Assignment
Li, X., Zhou, R., Zhang, Y., et al. (2024) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2024.3436657 · Cited by 5
“A representative approach proposes an edge-following-based strategy, which only allows robots on the edge to move while the other robots wait [25].”
InfluentialAn Approach for Extended Swarm Formation Flight with Drones: $$\texttt {PROTEASE}^{2.0}$$
Kosak, O., Kastenmüller, P., Wanninger, C., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-75107-3_16 · Cited by 5
“Despite yielding distinct emergent effects, it is apparent that swarm algorithms such as particle swarm optimization [34], the well-known flocking behavior analyzed in [24], shaping and formation algorithms [26], and distribution algorithms [16,18] rely on a common set of local actions.”
The second will be first: competition on directed networks
Cencetti, G., Bagnoli, F., Patti, F. D., et al. (2016) · Scientific Reports · doi:10.1038/srep27116 · Cited by 5
“= ∞ − r q rk q z t e ( , ) 1 2 cos (2 ) ( ) (4) k k Dt L 3 1 2 2”
Using Hardware Specialization and Hierarchy to Simplify Robotic Swarms
Espinosa, G., & Rubenstein, M. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8463206 · Cited by 5
“One difference between this approach for shape formation and many others using homogeneous swarms [11], is that the global location and orientation of the shape in the environment is completely controlled by the algorithm, this means that no manual seed is required and the resulting shape will be at the exact global p…”
BackgroundMethodInfluentialSelf-citationSelf-Assembling Space Architecture: tessellated shell structures for space habitats
Ekblaw, A., Shuter, E., & Paradiso, J. A. (2019) · AIAA SciTech Forum · doi:10.2514/6.2019-0481 · Cited by 5
An output-driven approach to design a swarming model for architectural indoor environments
Mathew, C. D. T., Beneš, B., & Aliaga, D. G. (2020) · Computers & Graphics · doi:10.1016/j.cag.2020.02.003 · Cited by 5
On efficient connectivity-preserving transformations in a grid
Almethen, A., Michail, O., & Potapov, I. (2021) · Theoretical Computer Science · doi:10.1016/j.tcs.2021.11.004 · Cited by 5
Navigation in Large Groups of Robots
Asiain, J., & Godoy, J. (2020) · Current Robotics Reports · doi:10.1007/s43154-020-00017-2 · Cited by 5
“The authors in [75] show how a large-scale multi-robot system, composed of 1024 small robots called “ kilobots, ” is able to perform tasks that involve navigation such as shape assembly (and disassembly in [76]), using only local perception and limited indirect communication.”
BackgroundA Mean Shift-Based Pattern Formation Algorithm for Robot Swarms
Vickery, C., & Salehi, S. A. (2021) · International Conference on Automation, Robotics and Applications · doi:10.1109/icara51699.2021.9376415 · Cited by 5
“Other approaches require more system-wide information 16 2021 International Conference on Automation, Robotics and Applications 978-0-7381-4290-6/21/$31.00 ©2021 IEEE such as the number of robots in the group or take the number of robots into consideration for certain parameters [6, 7, 8, 9].”
MethodInfluentialTranslation based Self Reconfiguration Algorithm for 6-lattice Modular Robots
Buchi, B., Mabed, H., Lassabe, F., et al. (2021) · International Symposium on Parallel and Distributed Computing · doi:10.1109/ispdc52870.2021.9521628 · Cited by 5
Distributed Optimisation and Deconstruction of Bridges by Self-Assembling Robots
Bray, E., & Groß, R. (2022) · doi:10.15607/rss.2022.xviii.030 · Cited by 5
A Formal Modular Synthesis Approach for the Coordination of Multi-Agent Systems
Rosa, M., Cury, J. E. R., & Baldissera, F. L. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.10.337 · Cited by 5
AGENT: an adaptive grouping and entrapping method for flocking systems
Wang, C., Kuang, W., Gu, M., & Fan, Z. (2022) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwac136 · Cited by 5
A unifying method-based classification of robot swarm spatial self-organisation behaviours
Hénard, A., Rivière, J., Peillard, É., Kubicki, S., & Coppin, G. (2023) · Adaptive Behavior · doi:10.1177/10597123231163948 · Cited by 5
“This method has been implemented by Kilobots (Rubenstein et al., 2014), which are robots that only perceive other robots when they are in contact with them.”
BackgroundMethodInfluentialExploration of a Simple Navigation Method for Swarm Robots Pioneered by Heterogeneity
SUEOKA, Y., Okada, M., Tsunoda, Y., et al. (2023) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2023.p0948 · Cited by 5
Optimal distributed interconnectivity of multi-robot systems by spatially-constrained clustering
Macktoobian, M., & Shoorehdeli, M. A. (2017) · Adaptive Behavior · doi:10.1177/1059712317700500 · Cited by 5
“Functional formation of a wheeledmobile-robot swarm has been uniformly achieved by using a mobile-sensor network (Sahin & Zergeroglu, 2008).”
MethodAssembling and Disassembling Planar Structures With Divisible and Atomic Components
Zhang, Y., Whiting, E., & Balkcom, D. (2018) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2018.2809595 · Cited by 5
The Swarm Is More Than the Sum of Its Drones
Hildmann, H., Eledlebi, K., Saffre, F., & Isakovic, A. F. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-63339-4_1 · Cited by 5
PuzzleFlex: kinematic motion of chains with loose joints
Lensgraf, S., Itani, K., Zhang, Y., et al. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196854 · Cited by 5
“, object transport [40], shape generation [41], selfassembly [42], [43], and network connectivity [44]; perhaps the closest work in spirit to the present is [45], which controls swarms of robots by allowing robots to bounce off of frictionless walls.”
BackgroundNon-Markovian dynamics of a torsion pendulum immersed in an active bath
Boudet, J. F., Guérin, T., Tiffon, C., et al. (2025) · Communications Physics · doi:10.1038/s42005-025-02349-x · Cited by 4
3D self-locking granular metamaterials
Liu, T., Sun, T., Cao, N., et al. (2026) · Science Advances · doi:10.1126/sciadv.aec8845 · Cited by 4
“Distinct from two-dimensional (2D) self-locking metamaterials ( 49 – 54 ) or other existing systems ( 55 – 57 ) that rely on boundary pressure, buckling, jamming, or chemical media, our 3D hooked particles achieve stability solely through intrinsic hook engagement without external confinement.”
Self-aligning active agents with inertia and active torque
Fersula, J., Bredèche, N., & Dauchot, O. (2024) · Physical review. E · doi:10.1103/physreve.110.014606 · Cited by 4
Body and Brain Quality-Diversity in Robot Swarms
Mkhatshwa, S., & Nitschke, G. (2024) · ACM Transactions on Evolutionary Learning and Optimization · doi:10.1145/3664656 · Cited by 4
Liu, J., Xu, Q. Y., & Su, M. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3440911 · Cited by 4
“The swarm formulation is a predominant derivation of UAV applications with more application options like surveillance, electronic countermeasure (ECM), suiciding attacks, etc., [5], [6], [7], [8].”
BackgroundStructural transitions of ionic microgel solutions driven by circularly polarized electric fields
Reich, M., Colla, T., & Likos, C. N. (2025) · Soft Matter · doi:10.1039/d4sm01414f · Cited by 4
Externally controlled intermittent randomization enables complex navigation of multiple nanobots
Goyal, R. K., Behera, J., Mandal, P., & Ghosh, A. (2025) · Nature Communications · doi:10.1038/s41467-025-58092-y · Cited by 4
“Controlling a swarm can be achieved through careful engineering of inter-robot communications, which in macroscale robotics can occur through radio frequency signals 42 , while at smaller scales, the robots can interact through hydrodynamic or magnetic forces showing various swarm 43 behaviors.”
Development of a Miniature Underactuated Tripod Robot Inspired by Penguin Waddling Gait
Liu, Y., Zhang, S., Deng, J., et al. (2025) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2025.3559964 · Cited by 4
“The size and weight of the robot proposed in this work are smaller compared to other multimotor-driven robots proposed by Shen et al. [33], Su et al. [34], and Wang et al. [35].”
InfluentialOptimized user-guided motion control of modular robots
Bolotnikova, A., Holdcroft, K., Cerbone, H., Belke, C., Ijspeert, A., & Paik, J. (2025) · Nature Communications · doi:10.1038/s41467-025-63706-6 · Cited by 6
“However, signi fi cantly increasing the number of modules is largely outside the scope of this work, as simply increasing the number of robots is an independent challenge 50 – 52 .”
Aggregation of self-propelled particles with sensitivity to local order
Bhattacharya, K., & Chakraborty, A. (2022) · Physical review. E · doi:10.1103/physreve.105.044124 · Cited by 4
Modular and Scalable Fabrication of Insect‐Scale Aerial Robots toward Demonstrating Swarm Flights
Hsiao, Y., Kim, S., Ceron, S., Ren, Z., & Chen, Y. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202300059 · Cited by 4
A Weighted Mean Field Reinforcement Learning Algorithm for Large-Scale Multi-Agent Collaboration
Yuan, X., Wang, H., & Yu, W. (2023) · Guidance Navigation and Control · doi:10.1142/s2737480723500073 · Cited by 4
The Impact of Morphological Diversity in Robot Swarms
Mkhatshwa, S., & Nitschke, G. (2023) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3583131.3590347 · Cited by 4
“Though, examples include self-assembly swarm robotics systems comprising many individual functionally simple robots that physically attach to each other [6, 34, 42] such as proofof-concept demonstrations using hundreds of Kilobots [11, 56, 59], a mergeable nervous system [35] and a group mind [49], where swarm-robotic…”
Learning Superior Cooperative Policy in Adversarial Multi-Team Reinforcement Learning
Fu, Q., Qiu, T., Pu, Z., Yi, J., Ai, X., & Yuan, W. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191422 · Cited by 4
Cooperative Hunting Method of Unmanned Surface Vehicle based on Attention Mechanism
Sang, G., Zhu, X., Ruan, X., et al. (2023) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc58219.2023.10326816 · Cited by 4
Distributed swarm control for multi-robot systems inspired by shepherding behaviors
Sun, G., Gu, H., & Lü, J. (2024) · Science China Technological Sciences · doi:10.1007/s11431-023-2651-6 · Cited by 4
“Di ff erent from shape formation problems where robots can form user-specified shapes [35–37], we here allow robots to spontaneously form a certain shape configura-tion only relying on local interactions with their neighbors, also known as swarm aggregation [38].”
Omnibot: A Scalable Vision-Based Robot Swarm Platform
Ma, Z., Liang, J., Wang, H., Guo, S., Huo, P., Zhang, Y., & Zhao, S. (2024) · International Conference on Control and Automation · doi:10.1109/icca62789.2024.10591921 · Cited by 4
“For example, platforms including Kilobot [4], Droplet [10], Alice [11], Zooids [12], e-puck [13], and HeRo2.”
BackgroundMethodPercolation transitions in a binary mixture of active Brownian particles with different softness
Sanoria, M., Chelakkot, R., & Nandi, A. (2024) · Soft Matter · doi:10.1039/d4sm00981a · Cited by 4
Mabrouk, M. N., & Floryan, D. (2024) · Bioinspiration & Biomimetics · doi:10.1088/1748-3190/ad936d · Cited by 4
“The benefits of collective motion have even led roboticists to develop robot swarms that aim to attain similar functionality [10, 11], but maintaining a cohesive group is a difficult task that requires unique strategies to coordinate dynamically interacting individuals [12].”
Robot Swarm Control Based on Smoothed Particle Hydrodynamics for Obstacle-Unaware Navigation
Eguchi, M., Nishimura, M., Yoshida, S., et al. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801800 · Cited by 4
“Real-world applications leveraging small robot swarms’ robustness, flexibility, and scalability have been extensively explored [19], [20].”
BackgroundEmergent functional dynamics of link-bots
Son, K., Bowal, K., Kim, K., Mahadevan, L., & Kim, H. (2025) · Science Advances · doi:10.1126/sciadv.adu8326 · Cited by 4
Cooperative pinning synchronization of a class of undirected complex networks
Zhu, H., Liu, K., & Lü, J. (2015) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2015.7260723 · Cited by 4
“Recently, complex networks theory has developed rapidly for they play an important role in many areas such as biology [1, 2], physics [3] and engineering [4–6].”
BackgroundJing, W., Smith, C. I., Staskevich, G., & Abbe, B. (2015) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2015.7293393 · Cited by 4
“Future work will be focused on experimental validation of the proposed algorithm by using Kilobots [13].”
MethodFrom tadpole to frog: artificial metamorphosis as a method of evolving self-reconfiguring robots
Joachimczak, M., Suzuki, R., & Arita, T. (2015) · doi:10.1162/978-0-262-33027-5-ch012 · Cited by 4
Self-assembly Planning of a Shape by Regular Modular Robots
Li, H., Wang, T., & Chirikjian, G. S. (2015) · Mechanisms and machine science · doi:10.1007/978-3-319-23327-7_74 · Cited by 4
“In addition to the polygon formation, self-assembly of a continuous-space shape was addressed in algorithms and physical system [4].”
Multicriteria adaptation principle on example of groups of mobile robots
Nelyubin, A. P., & Misyurin, S. Y. (2017) · Journal of Physics Conference Series · doi:10.1088/1742-6596/937/1/012034 · Cited by 4
Gaudl, S. E., & Bryson, J. J. (2018) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.02.001 · Cited by 4
“[17] Rubenstein M, Cornejo A, Nagpal R.”
BackgroundSpinBot: An Autonomous, Externally Actuated Robot for Swarm Applications
SaLoutos, A., & Rubenstein, M. (2019) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-05816-6_15 · Cited by 4
Self-citationCollective Formation and Cooperative Function of a Magnetic Microrobotic Swarm
Dong, X., & Sitti, M. (2019) · Robotics: Science and Systems · doi:10.15607/rss.2019.xv.007 · Cited by 4
An artificial moment method for conflict resolutions with robots being close to their targets
Xu, W.-B., Liu, X., Chen, X., et al. (2020) · Information Sciences · doi:10.1016/j.ins.2020.06.040 · Cited by 4
Metadata
- Authors
- Rubenstein, M., Cornejo, A., & Nagpal, R.
- Affiliations
- Harvard University (US)
- Corresponding author
- Rubenstein, Michael
- Venue
- Science
- Published
- 2014-08-15
- Volume
- 345
- Issue
- 6198
- Pages
- 795-799
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2030775133
- Semantic Scholar
- c5ab840850ab57f2f14b67a5485fa3fd727b33b8
- Cited by
- 1,308
- References
- 22
- Citations by year
- 2026: 115 · 2025: 129 · 2024: 110 · 2023: 122 · 2022: 118 · 2021: 112 · 2020: 105 · 2019: 135 · 2018: 117 · 2017: 94 · 2016: 73 · 2015: 66 · 2014: 11
- FWCI
- 98.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
- Physical Sciences › Engineering › Control and Systems Engineering › Distributed Control Multi-Agent Systems
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Micro and Nano Robotics
- Keywords
- programmable self-assembly, swarm robotics, shape formation, decentralized systems, autonomous robots, local interactions, artificial swarms, two-dimensional shapes
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
