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While current autonomous navigation systems allow robots to successfully drive themselves from one point to another in specific environments, they typically require extensive manual parameter re-tuning by human robotics experts in order to function in new environments.
D. A. Pomerleau, Alvinn: An autonomous land vehicle in a neural network, in: Advances in neural information processing systems, 1989, pp. 305–313
1989
Earlier work this paper cites.
L.-J. Lin, Self-improving reactive agents based on reinforcement learning, planning and teaching, Machine learning 8 (3) (1992) 293–321
1992
Earlier work this paper cites.
S. Quinlan, O. Khatib, Elastic bands: Connecting path planning and control, in: [1993] Proceedings IEEE International Conference on Robotics and Automation, IEEE, 1993, pp. 802–807
1993
Earlier work this paper cites.
S. Thrun, An approach to learning mobile robot navigation, Robotics and Autonomous systems 15 (4) (1995) 301–319
1995
Earlier work this paper cites.
D. Fox, W. Burgard, S. Thrun, The dynamic window approach to collision avoidance, IEEE Robotics & Automation Magazine 4 (1) (1997) 23–33
1997
Earlier work this paper cites.
W. D. Smart, L. P. Kaelbling, Effective reinforcement learning for mobile robots, in: Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), Vol. 4, IEEE, 2002, pp. 3404–3410
2002
Earlier work this paper cites.
N. Hansen, S. D. Müller, P. Koumoutsakos, Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es), Evolutionary computation 11 (1) (2003) 1–18
2003
Earlier work this paper cites.
2004
Earlier work this paper cites.
I. Farkhatdinov, J.-H. Ryu, J. Poduraev, A user study of command strategies for mobile robot teleoperation, Intelligent Service Robotics 2 (2) (2009) 95–104
2009
Earlier work this paper cites.
B. D. Argall, S. Chernova, M. Veloso, B. Browning, A survey of robot learning from demonstration, Robotics and autonomous systems 57 (5) (2009) 469–483
2009
Earlier work this paper cites.
W. B. Knox, P. Stone, C. Breazeal, Training a robot via human feedback: A case study, in: International Conference on Social Robotics, Springer, 2013, pp. 460–470
2013
Earlier work this paper cites.
H.-T. L. Chiang, A. Faust, M. Fiser, A. Francis, Learning navigation behaviors end-to-end with autorl, IEEE Robotics and Automation Letters 4 (2) (2019) 2007–2014
2014
Earlier work this paper cites.
S. Niekum, S. Osentoski, C. G. Atkeson, A. G. Barto, Online bayesian changepoint detection for articulated motion models, in: 2015 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2015, pp. 1468–1475
2015
Cited alongside, same era.
A. Nair, P. Srinivasan, S. Blackwell, C. Alcicek, R. Fearon, A. D. Maria, V. Panneershelvam, M. Suleyman, C. Beattie, S. Petersen, S. Legg, V. Mnih, K. Kavukcuoglu, D. Silver, Massively parallel methods for deep reinforcement learning (2015) · 2015
Cited alongside, same era.
X. Xiao, J. Dufek, T. Woodbury, R. Murphy, Uav assisted usv visual navigation for marine mass casualty incident response, in: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2017, pp. 6105–6110
2017
Cited alongside, same era.
M. Pfeiffer, M. Schaeuble, J. Nieto, R. Siegwart, C. Cadena, From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots, in: 2017 ieee international conference on robotics and automation (icra), IEEE, 2017, pp. 1527–1533
D. Perille, A. Truong, X. Xiao, P. Stone, Benchmarking metric ground navigation, in: 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), IEEE, 2020, pp. 116–121
2020
Later among the works it cites.
V. G. Goecks, G. M. Gremillion, V. J. Lawhern, J. Valasek, N. R. Waytowich, Integrating behavior cloning and reinforcement learning for improved performance in dense and sparse reward environments, in: International Conference on Autonomous Agents and Multi Agent Systems, 2020
2020
Later among the works it cites.
R. Dromnelle, E. Renaudo, G. Pourcel, R. Chatila, B. Girard, M. Khamassi, How to reduce computation time while sparing performance during robot navigation? a neuro-inspired architecture for autonomous shifting between model-based and model-free learning, in: Conference on Biomimetic and Biohybrid Systems, Springer, 2020, pp. 68–79
2020
Later among the works it cites.
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2017
Cited alongside, same era.
S. Aminikhanghahi, D. J. Cook, A survey of methods for time series change point detection, Knowledge and information systems 51 (2) (2017) 339–367
2017
Cited alongside, same era.
N. R. Waytowich, V. G. Goecks, V. J. Lawhern, Cycle-of-learning for autonomous systems from human interaction, in: AI-HRI Symposium, AAAI Fall Symposium Series, 2018
2018
Cited alongside, same era.
R. S. Sutton, A. G. Barto, Reinforcement learning: An introduction, MIT press, 2018
2018
Cited alongside, same era.
M. Sensoy, L. Kaplan, M. Kandemir, Evidential deep learning to quantify classification uncertainty, in: Advances in Neural Information Processing Systems, 2018, pp. 3179–3189
2018
Cited alongside, same era.
S. Fujimoto, H. van Hoof, D. Meger, Addressing function approximation error in actor-critic methods (2018) · 2018
Cited alongside, same era.
S. Siva, M. Wigness, J. Rogers, H. Zhang, Robot adaptation to unstructured terrains by joint representation and apprenticeship learning, in: Robotics: Science and Systems (RSS), 2019
2019
Cited alongside, same era.
V. G. Goecks, G. M. Gremillion, V. J. Lawhern, J. Valasek, N. R. Waytowich, Efficiently combining human demonstrations and interventions for safe training of autonomous systems in real-time, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 2462–2470
2019
Cited alongside, same era.
X. Xiao, B. Liu, G. Warnell, J. Fink, P. Stone, APPLD: Adaptive planner parameter learning from demonstration, IEEE Robotics and Automation Letters 5 (3) (2020) 4541–4547
2020
Cited alongside, same era.
R. Dromnelle, B. Girard, E. Renaudo, R. Chatila, M. Khamassi, Coping with the variability in humans reward during simulated human-robot interactions through the coordination of multiple learning strategies, in: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), IEEE, 2020, pp. 612–617
2020
Later among the works it cites.
Z. Wang, X. Xiao, B. Liu, G. Warnell, P. Stone, APPLI: Adaptive planner parameter learning from interventions, in: 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2021, pp. 6079–6085
2021
Closest in time.
Z. Xu, G. Dhamankar, A. Nair, X. Xiao, G. Warnell, B. Liu, Z. Wang, P. Stone, APPLR: Adaptive planner parameter learning from reinforcement, in: 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2021, pp. 6086–6092
2021
Closest in time.
B. Liu, X. Xiao, P. Stone, A lifelong learning approach to mobile robot navigation, IEEE Robotics and Automation Letters 6 (2) (2021) 1090–1096
2021
Closest in time.
X. Xiao, B. Liu, P. Stone, Agile robot navigation through hallucinated learning and sober deployment, in: 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2021, pp. 7316–7322
2021
Closest in time.
X. Xiao, B. Liu, G. Warnell, P. Stone, Toward agile maneuvers in highly constrained spaces: Learning from hallucination, IEEE Robotics and Automation Letters 6 (2) (2021) 1503–1510
2021
Closest in time.
Z. Wang, X. Xiao, A. J. Nettekoven, K. Umasankar, A. Singh, S. Bommakanti, U. Topcu, P. Stone, From agile ground to aerial navigation: Learning from learned hallucination, in: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2021, pp. 148–153
2021
Closest in time.
Z. Xu, X. Xiao, G. Warnell, A. Nair, P. Stone, Machine learning methods for local motion planning: A study of end-to-end vs. parameter learning, in: 2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), IEEE, 2021, pp. 217–222
2021
Closest in time.
G. Kahn, P. Abbeel, S. Levine, Badgr: An autonomous self-supervised learning-based navigation system, IEEE Robotics and Automation Letters 6 (2) (2021) 1312–1319
2021
Closest in time.