Human-Machine Teaming for UAVs: An Experimentation Platform (2312.11718v1)
Abstract: Full automation is often not achievable or desirable in critical systems with high-stakes decisions. Instead, human-AI teams can achieve better results. To research, develop, evaluate, and validate algorithms suited for such teaming, lightweight experimentation platforms that enable interactions between humans and multiple AI agents are necessary. However, there are limited examples of such platforms for defense environments. To address this gap, we present the Cogment human-machine teaming experimentation platform, which implements human-machine teaming (HMT) use cases that features heterogeneous multi-agent systems and can involve learning AI agents, static AI agents, and humans. It is built on the Cogment platform and has been used for academic research, including work presented at the ALA workshop at AAMAS this year [1]. With this platform, we hope to facilitate further research on human-machine teaming in critical systems and defense environments.
- M. S. Islam, S. Das, S. K. Gottipati, W. Duguay, C. Mars, J. Arabneydi, A. Fagette, M. Guzdial, and M. E. Taylor, “WIP: Human-ai interactions in real-world complex environments using a comprehensive reinforcement learning framework,” in Adaptive Learning Agents Workshop, ALA 2023, Held as Part of the AAMAS 2023, 2023.
- L. Cavalcante Siebert, M. L. Lupetti, E. Aizenberg, N. Beckers, A. Zgonnikov, H. Veluwenkamp, D. Abbink, E. Giaccardi, G.-J. Houben, C. M. Jonker, J. van den Hoven, D. Forster, and R. L. Lagendijk, “Meaningful human control: actionable properties for AI system development,” AI and Ethics, May 2022.
- F. Santoni de Sio and J. van den Hoven, “Meaningful human control over autonomous systems: A philosophical account,” Frontiers in Robotics and AI, vol. 5, Feb 2018.
- A. I. Redefined, S. K. Gottipati, S. Kurandwad, C. Mars, G. Szriftgiser, and F. Chabot, “Cogment: Open source framework for distributed multi-actor training, deployment & operations,” CoRR, vol. abs/2106.11345, 2021. [Online]. Available: https://arxiv.org/abs/2106.11345
- G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” 2016.
- E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2012, pp. 5026–5033.
- V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and G. State, “Isaac gym: High performance gpu-based physics simulation for robot learning,” 2021.
- Y. Zhu, J. Wong, A. Mandlekar, R. Martín-Martín, A. Joshi, S. Nasiriany, and Y. Zhu, “robosuite: A modular simulation framework and benchmark for robot learning,” 2022.
- P. Hernandez-Leal, B. Kartal, and M. E. Taylor, “A survey and critique of multiagent deep reinforcement learning,” Autonomous Agents and Multi-Agent Systems, vol. 33, no. 6, p. 750–797, Oct 2019.
- A. Wong, T. Bäck, A. V. Kononova, and A. Plaat, “Deep multiagent reinforcement learning: Challenges and directions,” Artificial Intelligence Review, pp. 1–34, 2022.
- K. Zhang, Z. Yang, and T. Başar, “Multi-agent reinforcement learning: A selective overview of theories and algorithms,” Handbook of reinforcement learning and control, pp. 321–384, 2021.
- J. Terry, B. Black, N. Grammel, M. Jayakumar, A. Hari, R. Sullivan, L. S. Santos, C. Dieffendahl, C. Horsch, R. Perez-Vicente et al., “Pettingzoo: Gym for multi-agent reinforcement learning,” Advances in Neural Information Processing Systems, vol. 34, pp. 15 032–15 043, 2021.
- S. Gronauer and K. Diepold, “Multi-agent deep reinforcement learning: a survey,” Artificial Intelligence Review, Apr 2021.
- M. Bettini, A. Shankar, and A. Prorok, “Heterogeneous multi-robot reinforcement learning,” 2023.
- D. A. Pomerleau, “Alvinn: An autonomous land vehicle in a neural network,” Advances in neural information processing systems, vol. 1, 1988.
- P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei, “Deep reinforcement learning from human preferences,” Advances in neural information processing systems, vol. 30, 2017.
- S. Chernova and M. Veloso, “Interactive policy learning through confidence-based autonomy,” J. Artif. Int. Res., vol. 34, no. 1, p. 1–25, jan 2009.
- A. Bignold, F. Cruz, M. E. Taylor, T. Brys, R. Dazeley, P. Vamplew, and C. Foale, “A conceptual framework for externally-influenced agents: An assisted reinforcement learning review,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 4, p. 3621–3644, Apr 2023. [Online]. Available: https://arxiv.org/abs/2007.01544
- N. Bard, J. N. Foerster, S. Chandar, N. Burch, M. Lanctot, H. F. Song, E. Parisotto, V. Dumoulin, S. Moitra, E. Hughes, I. Dunning, S. Mourad, H. Larochelle, M. G. Bellemare, and M. Bowling, “The hanabi challenge: A new frontier for ai research,” Artificial Intelligence, vol. 280, p. 103216, Mar 2020.
- H. Nekoei, A. Badrinaaraayanan, A. Courville, and S. Chandar, “Continuous coordination as a realistic scenario for lifelong learning,” arXiv:2103.03216 [cs], Jun 2021. [Online]. Available: https://arxiv.org/abs/2103.03216
- S. K. Gottipati, L.-H. Nguyen, C. Mars, and M. E. Taylor, “Hiking up that hill with cogment-verse: Train & operate multi-agent systems learning from humans,” in Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2023.
- V. V. Unhelkar, S. Li, and J. A. Shah, “Decision-making for bidirectional communication in sequential human-robot collaborative tasks,” Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, Mar 2020.
- K. Kassem and F. Michahelles, “Exploring human-robot interaction by simulating robots,” 2022. [Online]. Available: https://dl.gi.de/handle/20.500.12116/39622
- J. L. Sanchez-Lopez, R. A. S. Fernández, H. Bavle, C. Sampedro, M. Molina, J. Pestana, and P. Campoy, “Aerostack: An architecture and open-source software framework for aerial robotics,” in 2016 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2016, pp. 332–341.
- D. Thomas, W. Woodall, and E. Fernandez, “Next-generation ROS: Building on DDS,” in ROSCon Chicago 2014. Mountain View, CA: Open Robotics, sep 2014. [Online]. Available: https://vimeo.com/106992622
- “Design and use paradigms for gazebo, an open-source multi-robot simulator,” in 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH37566), vol. 3. IEEE, pp. 2149–2154.
- T. Hester, M. Vecerik, O. Pietquin, M. Lanctot, T. Schaul, B. Piot, D. Horgan, J. Quan, A. Sendonaris, I. Osband et al., “Deep q-learning from demonstrations,” in AAAI, 2018.
- A. Nair, B. McGrew, M. Andrychowicz, W. Zaremba, and P. Abbeel, “Overcoming exploration in reinforcement learning with demonstrations,” in 2018 IEEE international conference on robotics and automation (ICRA). IEEE, 2018, pp. 6292–6299.
- Laila El Moujtahid (1 paper)
- Sai Krishna Gottipati (8 papers)
- Clodéric Mars (4 papers)
- Matthew E. Taylor (69 papers)