1. Publications

My CRIS profile:

https://cris.technion.ac.il/en/persons/ariel-barel

Journals and Conferences:
  • “Sensor to Pixels: Decentralized Swarm Gathering via Image-Based Reinforcement Learning”; Yigal Koifman, Eran Iceland, Erez Koifman, Ariel Barel, Alfred M. Bruckstein ; Submitted February 2026 to  AROB – Journal of Artificial Life and Robotics, Springer Nature Japan, eISSN: 1614‑7456.
  • “Virtual Target Based Stochastic Shoot-Shoot-Look Assignment Algorithms”; Gleb Merkulov, Eran Iceland, Shay Michaeli, Oren Gal, Ariel Barel and Tal Shima. Accepted February 2026 JAIS – Journal of Aerospace Information Systems, eISSN: 2327-3097.
  • “Distributed and Decentralized Task Allocation for Heterogeneous Swarms”; Yigal Koifman, Ariel Barel, Alfred M. Bruckstein. January 2026  AROB – Journal of Artificial Life and Robotics, Springer Nature Japan, eISSN: 1614‑7456. https://doi.org/10.1007/s10015-025-01104-3
  • “VariAntNet: Learning Decentralized Control of Multi-Agent Systems”; Yigal Koifman, Erez Koifman, Eran Iceland, Ariel Barel, Alfred M. Bruckstein. The 9th International Symposium on Swarm Behavior and Bio-Inspired Robotics, September 23-25, 2025 in Göttingen, Proc. Swarm-Systems.org  (Best Student Paper Award).
  • “Interpretable Multi-Agent Communication via Information Gating”; Stav Belogolovsky, Eran Iceland, Itay Naeh, Ariel Barel, Shie Manor.  ICML 2025 Workshop on Collaborative and Federated Agentic Workflows.
  • “Cooperative Dynamic Weapon-Target Assignment in a Multiagent Engagement”; Gleb Merkulov, Eran Iceland, Shay Michaeli, Oren Gal, Ariel Barel and Tal Shima. IAAC³ Conference. April 2025. https://arc.aiaa.org/doi/abs/10.2514/6.2025-1546  (Best Paper Award)
  • “Reinforcement-Learning-Based Cooperative Dynamic Weapon-Target Assignment in a Multiagent Engagement”; Gleb Merkulov, Eran Iceland, Shay Michaeli, Oren Gal, Ariel Barel and Tal Shima. AIAA 2025-1546. AIAA SCITECH 2025 Forum. January 2025. https://arc.aiaa.org/doi/10.2514/6.2025-1546
  • “Distributed and Decentralized Control and Task Allocation for Flexible Swarms”; Yigal Koifman, Ariel Barel, Alfred M. Bruckstein; in The 8th International Symposium on Swarm Behavior and Bio-Inspired Robotics, 2024, pp. 137–144, Swarm-Systems.org (download a free copy)
  • “Learning to Explore Indoor Environments using Autonomous Micro Aerial Vehicles”; Yuezhan Tao, Eran Iceland, Beiming Li, Elchanan Zwecher, Uri Heinemann, Avraham Cohen, Amir Avni, Oren Gal, Ariel Barel, Vijay Kumar; In 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan, 2024, pp. 15758-15764, doi: 10.1109/ICRA57147.2024.10610464. https://ieeexplore.ieee.org/document/10610464 (download a free copy)
  • “Reinforcement Learning Based Decentralized Weapon-Target Assignment and Guidance”; Gleb Merkulov, Eran Iceland, Shay Michaeli, Yosef Riechkind, Oren Gal, Ariel Barel, and Tal Shima. In AIAA SCITECH 2024 Forum, p. 0125. 2024; https://arc.aiaa.org/doi/abs/10.2514/6.2024-0125 (download a free copy)
  • “Multi-Agent Distributed and Decentralized Geometric Task Allocation”; Michael Amir, Yigal Koifman, Yakov Bloch, Ariel Barel, Alfred M. Bruckstein, 2023 62nd IEEE Conference on Decision and Control (CDC-2023), Singapore, Singapore, 2023, pp. 8355-8362; doi: 10.1109/CDC49753.2023.10383740 (download a free copy)
  • “Stress-Adaptive Training: An Adaptive Psychomotor Training According to Stress Measured by Grip Force”; Yotam Sahar, Michael Wagner, Ariel Barel, and Shraga Shoval (2023); Paper presented at the 23rd National Industrial Engineering & Management Conference IE&M 2023, Ariel.
  • “Stress-Adaptive Training: An Adaptive Psychomotor Training According to Stress Measured by Grip Force”; Yotam Sahar, Michael Wagner, Ariel Barel, and Shraga Shoval (2022, November). In Sensors – Multidisciplinary Digital Publishing Institute (MDPI); https://www.mdpi.com/1424-8220/22/21/8368
  • “Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping”; Elchanan Zwecher, Eran Iceland, Sean R. Levy, Shmuel Y. Hayoun, Oren Gal, Ariel Barel. In 2022 International Conference on Robotics and Automation (ICRA-2022), Philadelphia, 2022 (pp. 10542-10548). IEEE; https://ieeexplore.ieee.org/abstract/document/9811861 (download a free copy)
  • “Probabilistic Gathering of Agents with Simple Sensors”; Ariel Barel, Thomas Dagès, Rotem Manor, Alfred M. Bruckstein, SIAM Journal on Applied Mathematics, 2021, 81.2: 620-640; https://doi.org/10.1137/20M133333X (download a free copy)
  • “Local Interactions for Cohesive Flexible Swarms”; Rotem Manor, Ariel Barel, Alfred M. Bruckstein (IRC 2020 XIV. Conference, Tokyo 2019); arXiv:1903.09259 [cs.MA]
  • “Probabilistic Gathering of Agents with Simple Sensors”; Ariel Barel, Rotem Manor, Alfred M. Bruckstein (Poster, Conference on Collective Behavior, SMR 3201, Trieste 2018); arXiv:1902.00294 [cs.MA]
  • “On Steering Swarms”; Ariel Barel, Rotem Manor, Alfred M. Bruckstein. Swarm Intelligence ANTS, Rome 2018. Lecture Notes in Computer Science, vol 11172. Springer, Cham; https://doi.org/10.1007/978-3-030-00533-7_35 (download a free copy)
Preprints:
  • “Sensor to Pixels: Decentralized Swarm Gathering via Image-Based Reinforcement Learning”; Yigal Koifman, Eran Iceland, Erez Koifman, Ariel Barel, Alfred M. Bruckstein ; arXiv:2601.03413 [cs.LG]
  • “VariAntNet: Learning Decentralized Control of Multi-Agent Systems”; Yigal Koifman, Erez Koifman, Eran Iceland, Ariel Barel, Alfred M. Bruckstein. The 9th International Symposium on Swarm Behavior and Bio-Inspired Robotics, September 23-25, 2025 in Göttingen, Swarm-Systems.org; arXiv:2509.02271 [cs.LG]
  • “Distributed and Decentralized Control and Task Allocation for Flexible Swarms”; Yigal Koifman, Ariel Barel, Alfred M. Bruckstein; arXiv:2405.13941 [cs.MA]
  • “Learning to Explore Indoor Environments using Autonomous Micro Aerial Vehicles”; Yuezhan Tao, Eran Iceland, Beiming Li, Elchanan Zwecher, Uri Heinemann, Avraham Cohen, Amir Avni, Oren Gal, Ariel Barel, Vijay Kumar; arXiv:2309.06986 [cs.RO]
  • “Multi-Agent Distributed and Decentralized Geometric Task Allocation”; Michael Amir, Yigal Koifman, Yakov Bloch, Ariel Barel, Alfred M. Bruckstein; arXiv:2210.05552 [cs.MA]
  • “Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping”; Elchanan Zwecher, Eran Iceland, Sean R. Levy, Shmuel Y. Hayoun, Oren Gal, Ariel Barel; arXiv:2109.08490 [cs.LG]
  • “Deep-Learning-Aided Path Planning and Map Construction for Expediting Indoor Mapping”; Elchanan Zwecher, Eran Iceland, Shmuel Y. Hayoun, Ahavatya Revivo, Sean R. Levy, Ariel Barel; arXiv:2011.02043 [cs.LG]
  • “Local Interactions for Cohesive Flexible Swarms”; Rotem Manor, Ariel Barel, Alfred M. Bruckstein; arXiv:1903.09259 [cs.MA]
  • “Probabilistic Gathering of Agents with Simple Sensors”; Ariel Barel, Rotem Manor, Alfred M. Bruckstein; arXiv:1902.00294 [cs.MA]
  • “On Steering Swarms”; Ariel Barel, Rotem Manor, Alfred M. Bruckstein; arXiv:1902.00385 [cs.MA]
  • “COME TOGETHER: Multi-Agent Geometric Consensus (Gathering, Rendezvous, Clustering, Aggregation)”; Ariel Barel, Rotem Manor, Alfred M. Bruckstein (Tech report, Technion CIS, 2016); arXiv:1902.01455 [cs.MA]
Thesis
  • “Gathering and Steering Swarms”; Ariel Barel, PhD Dissertation, Technion, I.I.T, Haifa, Israel, 2018; http://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-info.cgi/2018/PHD/PHD-2018-14
  • Abstract:
    This thesis studies the gathering and steering problems for swarms of autonomous agents. The gathering problem is to design local rules that guarantee that a group of agents, starting from arbitrary initial positions, will eventually meet at a single point. The steering problem is to design local rules that enable a group of agents to move together in a desired direction or along a desired trajectory. The thesis presents new algorithms for both problems, analyzes their convergence properties, and demonstrates their effectiveness through simulations and theoretical proofs. The results contribute to the understanding of decentralized control in multi-agent systems and have potential applications in robotics, sensor networks, and distributed computing.