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Robots must operate safely when deployed in novel and human-centered environments, like homes.
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Z. Qin, K. Zhang, Y. Chen, J. Chen, and C. Fan, “Learning safe multi-agent control with decentralized neural barrier certificates,” International Conference on Learning Representations , 2021
2021
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2021
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2022
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W. Liu, C. Paxton, T. Hermans, and D. Fox, “Structformer: Learning spatial structure for language-guided semantic rearrangement of novel objects,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 6322–6329
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2022
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C. Dawson, Z. Qin, S. Gao, and C. Fan, “Safe nonlinear control using robust neural lyapunov-barrier functions,” in Conference on Robot Learning . PMLR, 2022, pp. 1724–1735
2022
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R. Tian, L. Sun, A. Bajcsy, M. Tomizuka, and A. D. Dragan, “Safety assurances for human-robot interaction via confidence-aware game-theoretic human models,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 11 229–11 235
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L. Brunke, M. Greeff, A. W. Hall, Z. Yuan, S. Zhou, J. Panerati, and A. P. Schoellig, “Safe learning in robotics: From learning-based control to safe reinforcement learning,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 5, no. 1, pp. 411–444, 2022
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J. Li, Q. Liu, W. Jin, J. Qin, and S. Hirche, “Robust safe learning and control in an unknown environment: An uncertainty-separated control barrier function approach,” IEEE Robotics and Automation Letters , 2023
2023
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J. Borquez, K. Nakamura, and S. Bansal, “Parameter-conditioned reachable sets for updating safety assurances online,” in IEEE International Conference on Robotics and Automation (ICRA) , 2023
2023
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K. P. Wabersich, A. J. Taylor, J. J. Choi, K. Sreenath, C. J. Tomlin, A. D. Ames, and M. N. Zeilinger, “Data-driven safety filters: Hamilton-jacobi reachability, control barrier functions, and predictive methods for uncertain systems,” IEEE Control Systems Magazine , vol. 43, no. 5, pp. 137–177, 2023
2023
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K.-C. Hsu, D. P. Nguyen, and J. F. Fisac, “Isaacs: Iterative soft adversarial actor-critic for safety,” in Learning for Dynamics and Control Conference . PMLR, 2023, pp. 90–103
2023
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X. Puig, E. Undersander, A. Szot, M. D. Cote, R. Partsey, J. Yang, R. Desai, A. W. Clegg, M. Hlavac, T. Min, T. Gervet, V. Vondrus, V.-P. Berges, J. Turner, O. Maksymets, Z. Kira, M. Kalakrishnan, J. Malik, D. S. Chaplot, U. Jain, D. Batra, A. Rai, and R. Mottaghi, “Habitat 3.0: A co-habitat for humans, avatars and robots,” 2023
2023
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2024
Closest in time.
K. Kim, G. Swamy, Z. Liu, D. Zhao, S. Choudhury, and S. Z. Wu, “Learning shared safety constraints from multi-task demonstrations,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
D. Lindner, X. Chen, S. Tschiatschek, K. Hofmann, and A. Krause, “Learning safety constraints from demonstrations with unknown rewards,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2024, pp. 2386–2394
2024
Closest in time.
D. P. Nguyen, K.-C. Hsu, J. F. Fisac, J. Tan, and W. Yu, “Gameplay filters: Robust zero-shot safety through adversarial imagination,” in 8th Annual Conference on Robot Learning , 2024
2024
Closest in time.
2024
Closest in time.
E. Trevisan and J. Alonso-Mora, “Biased-mppi: Informing sampling-based model predictive control by fusing ancillary controllers,” IEEE Robotics and Automation Letters , 2024
2024
Closest in time.
A. Dixit, Z. Mei, M. Booker, M. Storey-Matsutani, A. Z. Ren, and A. Majumdar, “Perceive with confidence: Statistical safety assurances for navigation with learning-based perception,” in 8th Annual Conference on Robot Learning , 2024
2024
Closest in time.