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Heterogeneous Teams

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Encyclopedia of Robotics
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Synonyms

Diverse teams; Heterogeneous multi-robot systems

Definitions

A team of robots is heterogeneous when at least one of its members is behaviorally or physically different from any of the other team members.

Overview

Heterogeneity is fundamental to life and commonplace in natural systems. While the scientific field has devoted itself to the analysis of diversity in natural communities for decades, research into the benefits of heterogeneity in engineered teams is relatively nascent. Homogeneous teams are capable of solving complex tasks through coordination and cooperation, yet heterogeneity is even more powerful because it allows for collaboration Prorok et al. (2021); Yang et al. (2018). (Our usage of the terms coordination, cooperation, and collaboration aligns with the definitions in Prorok et al. (2021).)

The aim of this chapter is to provide an overview of heterogeneous teams, from a robotics perspective. We begin by introducing a taxonomy of heterogeneous systems. Then, we...

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References

  • Ayanian N (2019) Dart: Diversity-enhanced autonomy in robot teams. Int J Robotics Res 38(12–13):1329–1337

    Article  Google Scholar 

  • Balch T (1997) Learning roles: behavioral diversity in robot teams. In: AAAI workshop on multiagent learning

    Google Scholar 

  • Balch T (2000) Hierarchic social entropy: An information theoretic measure of robot group diversity. Auton Robot 8(3):209–238

    Article  Google Scholar 

  • Berman S, Halász A, Kumar V, Pratt S (2007) Bio-inspired group behaviors for the deployment of a swarm of robots to multiple destinations. In: Proceedings 2007 IEEE international conference on robotics and automation (ICRA). IEEE, pp 2318–2323

    Google Scholar 

  • Bernstein DS, Givan R, Immerman N, Zilberstein S (2002) The complexity of decentralized control of Markov decision processes. Math Oper Res 27(4):819–840

    Article  MathSciNet  Google Scholar 

  • Bettini M, Shankar A, Prorok A (2023a) Heterogeneous multi-robot reinforcement learning. In: Proceedings of the 22nd international conference on autonomous agents and multiagent systems, international foundation for Autonomous Agents and Multiagent Systems, AAMAS ‘23

    Google Scholar 

  • Bettini M, Shankar A, Prorok A (2023b) System neural diversity: measuring behavioral heterogeneity in multi-agent learning. arXiv preprint arXiv:230502128

    Google Scholar 

  • Blumenkamp J, Prorok A (2021) The emergence of adversarial communication in multi-agent reinforcement learning. In: Conference on robot learning, PMLR, pp. 1394–1414

    Google Scholar 

  • Boroson ER, Ayanian N (2019) 3d keypoint repeatability for heterogeneous multi-robot slam. In: 2019 International Conference on Robotics and Automation (ICRA), IEEE, pp 6337–6343

    Google Scholar 

  • Carlone L, Pinciroli C (2019) Robot co-design: beyond the monotone case. In: 2019 International Conference on Robotics and Automation (ICRA). IEEE, pp 3024–3030

    Google Scholar 

  • Chand P, Carnegie DA (2013) Mapping and exploration in a hierarchical heterogeneous multi-robot system using limited capability robots. Robot Auton Syst 61(6):565–579

    Article  Google Scholar 

  • Chenghao L, Wang T, Wu C, Zhao Q, Yang J, Zhang C (2021) Celebrating diversity in shared multi-agent reinforcement learning. Adv Neural Inf Process Syst 34:3991–4002

    Google Scholar 

  • Christianos F, Papoudakis G, Rahman MA, Albrecht SV (2021) Scaling multi-agent reinforcement learning with selective parameter sharing. In: International conference on machine learning. PMLR, pp 1989–1998

    Google Scholar 

  • Chvatal V (1979) A Greedy heuristic for the set-covering problem. Math Oper Res 4(3):233–235

    Article  MathSciNet  Google Scholar 

  • Debord M, Hönig W, Ayanian N (2018) Trajectory planning for heterogeneous robot teams. In: 2018 IEEE/RSJ international conference on Intelligent Robots and Systems (IROS), IEEE, pp 7924–7931

    Google Scholar 

  • Dias MB, Zlot R, Kalra N, Stentz A (2006) Market-based multirobot coordination: a survey and analysis. Proc IEEE 94(7):1257–1270

    Article  Google Scholar 

  • Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Article  Google Scholar 

  • Emam Y, Mayya S, Notomista G, Bohannon A, Egerstedt M (2020) Adaptive task allocation for heterogeneous multi-robot teams with evolving and unknown robot capabilities. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp 7719–7725

    Google Scholar 

  • Emam Y, Notomista G, Glotfelter P, Egerstedt M (2021) Data-driven adaptive task allocation for heterogeneous multi-robot teams using robust control barrier functions. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp 9124–9130

    Google Scholar 

  • Foerster J, Assael IA, De Freitas N, Whiteson S (2016) Learning to communicate with deep multiagent reinforcement learning. Adv Neural Inf Proces Syst (NeurIPS) 29

    Google Scholar 

  • Foerster J, Farquhar G, Afouras T, Nardelli N, Whiteson S (2018) Counterfactual multi-agent policy gradients. In: Proceedings of the AAAI conference on artificial intelligence, vol 32

    Google Scholar 

  • Gerkey BP, Mataric MJ (2004) A formal analysis and taxonomy of task allocation in multi-robot systems. Int J Robotics Res 23(9):939–954

    Article  Google Scholar 

  • Goldberg D, Mataric MJ (1997) Interference as a tool for designing and evaluating multi-robot controllers. In: AAAI/IAAI, pp 637–642

    Google Scholar 

  • Kim S, Santos M, Guerrero-Bonilla L, Yezzi A, Egerstedt M (2022) Coverage control of mobile robots with different maximum speeds for time-sensitive applications. IEEE Robot Autom Lett 7(2):3001–3007

    Article  Google Scholar 

  • Korte B, Vygen J (2000) Combinatorial optimization: theory and algorithms. Springer, Berlin

    Book  Google Scholar 

  • Kortvelesy R, Prorok A (2022) Qgnn: Value function factorisation with graph neural networks. arXiv preprint arXiv:220513005

    Google Scholar 

  • Li L, Martinoli A, Abu-Mostafa YS (2004) Learning and measuring specialization in collaborative swarm systems. Adapt Behav 12(3–4):199–212

    Article  Google Scholar 

  • Li Q, Gama F, Ribeiro A, Prorok A (2020) Graph neural networks for decentralized multi-robot path planning. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp 11785–11792

    Google Scholar 

  • Lowe R, Wu YI, Tamar A, Harb J, Pieter Abbeel O, Mordatch I (2017) Multi-agent actor-critic for mixed cooperative-competitive environments. Adv Neural Inf Process Syst 30

    Google Scholar 

  • Malencia M, Manjanna S, Hsieh MA, Pappas G, Kumar V (2022) Adaptive sampling of latent phenomena using heterogeneous robot teams (aslap-hr). In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp 8762–8769

    Google Scholar 

  • Manjanna S, Li AQ, Smith RN, Rekleitis I, Dudek G (2018) Heterogeneous multi-robot system for exploration and strategic water sampling. In: 2018 IEEE international conference on robotics and automation (ICRA). IEEE, pp 4873–4880

    Google Scholar 

  • Mayya S, D’antonio DS, Saldaña D, Kumar V (2021) Resilient task allocation in heterogeneous multi-robot systems. IEEE Robot Autom Lett 6(2):1327–1334

    Article  Google Scholar 

  • Michael N, Shen S, Mohta K, Kumar V, Nagatani K, Okada Y, Kiribayashi S, Otake K, Yoshida K, Ohno K et al (2014) Collaborative mapping of an earthquake damaged building via ground and aerial robots. In: Field and service robotics: results of the 8th international conference. Springer, Berlin/Heidelberg, pp 33–47

    Chapter  Google Scholar 

  • Nash JF Jr (1950) Equilibrium points in n-person games. Proc Natl Acad Sci 36(1):48–49

    Article  MathSciNet  Google Scholar 

  • Notomista G, Mayya S, Hutchinson S, Egerstedt M (2019) An optimal task allocation strategy for heterogeneous multi-robot systems. In: 2019 18th European Control Conference (ECC). IEEE, pp 2071–2076

    Google Scholar 

  • Notomista G, Mayya S, Emam Y, Kroninger C, Bohannon A, Hutchinson S, Egerstedt M (2021) A resilient and energy-aware task allocation framework for heterogeneous multirobot systems. IEEE Trans Robot 38(1):159–179

    Article  Google Scholar 

  • Pimenta LC, Kumar V, Mesquita RC, Pereira GA (2008) Sensing and coverage for a network of heterogeneous robots. In: 2008 47th IEEE conference on decision and control. IEEE, pp 3947–3952

    Google Scholar 

  • Prorok A, Hsieh MA, Kumar V (2017) The impact of diversity on optimal control policies for heterogeneous robot swarms. IEEE Trans Robot 33(2):346–358

    Article  Google Scholar 

  • Prorok A, Malencia M, Carlone L, Sukhatme GS, Sadler BM, Kumar V (2021) Beyond robustness: a taxonomy of approaches towards resilient multi-robot systems. arXiv preprint arXiv:210912343

    Google Scholar 

  • Ravichandar H, Shaw K, Chernova S (2020) Strata: unified framework for task assignments in large teams of heterogeneous agents. Auton Agent Multi-Agent Syst 34:1–25

    Article  Google Scholar 

  • Santos M, Diaz-Mercado Y, Egerstedt M (2018) Coverage control for multirobot teams with heterogeneous sensing capabilities. IEEE Robot Autom Lett 3(2):919–925

    Article  Google Scholar 

  • Schneider-Fontan M, Mataric MJ (1998) Territorial multi-robot task division. IEEE Trans Robot Autom 14(5):815–822

    Article  Google Scholar 

  • Seraj E, Wang Z, Paleja R, Martin D, Sklar M, Patel A, Gombolay M (2022) Learning efficient diverse communication for cooperative heterogeneous teaming. In: Proceedings of the 21st international conference on autonomous agents and multiagent systems, pp 1173–1182

    Google Scholar 

  • Shang B, Crowder R, Zauner KP (2014) Swarm behavioral sorting based on robotic hardware variation. In: 2014 4th international conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH), IEEE, pp 631–636

    Google Scholar 

  • Shehory O, Kraus S (2005) A kernel-oriented model for autonomous-agent coalition-formation in general environments. In: Distributed artificial intelligence architecture and modelling. Springer, Berlin/Heidelberg, pp 31–45

    Google Scholar 

  • Spica R, Cristofalo E, Wang Z, Montijano E, Schwager M (2020) A real-time game theoretic planner for autonomous two-player drone racing. IEEE Trans Robot 36(5):1389–1403

    Article  Google Scholar 

  • Wang T, Dong H, Lesser V, Zhang C (2020) Roma: multi-agent reinforcement learning with emergent roles. In: International conference on machine learning. PMLR, pp 9876–9886

    Google Scholar 

  • Wang M, Wang Z, Talbot J, Gerdes JC, Schwager M (2021a) Game-theoretic planning for selfdriving cars in multivehicle competitive scenarios. IEEE Trans Robot 37(4):1313–1325

    Article  Google Scholar 

  • Wang T, Gupta T, Peng B, Mahajan A, Whiteson S, Zhang C (2021b) Rode: learning roles to decompose multi- agent tasks. In: Proceedings of the international conference on learning representations

    Google Scholar 

  • Yang GZ, Bellingham J, Dupont PE, Fischer P, Floridi L, Full R, Jacobstein N, Kumar V, McNutt M, Merrifield R et al (2018) The grand challenges of science robotics. Sci Robot 3(14):eaar7650

    Article  Google Scholar 

  • Zardini G, Milojevic D, Censi A, Frazzoli E (2020) A formal approach to the co-design of embodied intelligence. arXiv preprint arXiv:201110756

    Google Scholar 

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Correspondence to Amanda Prorok .

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Prorok, A., Bettini, M. (2024). Heterogeneous Teams. In: Ang, M.H., Khatib, O., Siciliano, B. (eds) Encyclopedia of Robotics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41610-1_230-1

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