Fetching the paper…
Reading the bibliography…
Accurate control of robots at high speeds requires a control system that can take into account the kinodynamic interactions of the robot with the environment.
S. Piche, B. Sayyar-Rodsari, D. Johnson, and M. Gerules, “Nonlinear model predictive control using neural networks,” IEEE Control Systems Magazine , vol. 20, no. 3, pp. 53–62, 2000
2000
Earlier work this paper cites.
A. Tahirovic, G. Magnani, and P. Rocco, “Mobile robot navigation using passivity-based mpc,” in 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics . IEEE, 2010, pp. 483–488
2010
Earlier work this paper cites.
S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,” The international journal of robotics research , vol. 30, no. 7, pp. 846–894, 2011
2011
Earlier work this paper cites.
J. J. Park, C. Johnson, and B. Kuipers, “Robot navigation with model predictive equilibrium point control,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2012, pp. 4945–4952
2012
Earlier work this paper cites.
J. D. Gammell, S. S. Srinivasa, and T. D. Barfoot, “Batch informed trees (bit*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs,” in 2015 IEEE international conference on robotics and automation (ICRA) . IEEE, 2015, pp. 3067–3074
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
L. Tai, S. Li, and M. Liu, “A deep-network solution towards model-less obstacle avoidance,” in 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2016, pp. 2759–2764
2016
Earlier work this paper cites.
B. Kim and J. Pineau, “Socially adaptive path planning in human environments using inverse reinforcement learning,” International Journal of Social Robotics , vol. 8, no. 1, pp. 51–66, 2016
2016
Earlier work this paper cites.
H. Kretzschmar, M. Spies, C. Sprunk, and W. Burgard, “Socially compliant mobile robot navigation via inverse reinforcement learning,” The International Journal of Robotics Research , vol. 35, no. 11, pp. 1289–1307, 2016
2016
Earlier work this paper cites.
G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou, “Aggressive driving with model predictive path integral control,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2016, pp. 1433–1440
2016
Earlier work this paper cites.
M. Pfeiffer, M. Schaeuble, J. Nieto, R. Siegwart, and 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
2017
Earlier work this paper cites.
L. Tai, G. Paolo, and M. Liu, “Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 31–36
2017
Earlier work this paper cites.
Y. F. Chen, M. Liu, M. Everett, and J. P. How, “Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning,” in 2017 IEEE international conference on robotics and automation (ICRA) . IEEE, 2017, pp. 285–292
2017
Earlier work this paper cites.
G. Williams, A. Aldrich, and E. A. Theodorou, “Model predictive path integral control: From theory to parallel computation,” Journal of Guidance, Control, and Dynamics , vol. 40, no. 2, pp. 344–357, 2017
2017
Cited alongside, same era.
G. Williams, N. Wagener, B. Goldfain, P. Drews, J. M. Rehg, B. Boots, and E. A. Theodorou, “Information theoretic mpc for model-based reinforcement learning,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2017, pp. 1714–1721
2017
Cited alongside, same era.
M. Pfeiffer, S. Shukla, M. Turchetta, C. Cadena, A. Krause, R. Siegwart, and J. Nieto, “Reinforced imitation: Sample efficient deep reinforcement learning for mapless navigation by leveraging prior demonstrations,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 4423–4430, 2018
2018
Cited alongside, same era.
M. Wigness, J. G. Rogers, and L. E. Navarro-Serment, “Robot navigation from human demonstration: Learning control behaviors,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 1150–1157
2021
Later among the works it cites.
2021
Later among the works it cites.
Z. Wang, X. Xiao, B. Liu, G. Warnell, and 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
Later among the works it cites.
Z. Wang, X. Xiao, G. Warnell, and P. Stone, “Apple: Adaptive planner parameter learning from evaluative feedback,” IEEE Robotics and Automation Letters , vol. 6, no. 4, pp. 7744–7749, 2021
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
J. Lorenzetti, B. Landry, S. Singh, and M. Pavone, “Reduced order model predictive control for setpoint tracking,” in 2019 18th European Control Conference (ECC) . IEEE, 2019, pp. 299–306
2019
Cited alongside, same era.
H.-T. L. Chiang, A. Faust, M. Fiser, and A. Francis, “Learning navigation behaviors end-to-end with autorl,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 2007–2014, 2019
2019
Cited alongside, same era.
J. Kabzan, L. Hewing, A. Liniger, and M. N. Zeilinger, “Learning-based model predictive control for autonomous racing,” IEEE Robotics and Automation Letters , vol. 4, no. 4, pp. 3363–3370, 2019
2019
Cited alongside, same era.
Y. Pan, C.-A. Cheng, K. Saigol, K. Lee, X. Yan, E. A. Theodorou, and B. Boots, “Imitation learning for agile autonomous driving,” The International Journal of Robotics Research , vol. 39, no. 2-3, pp. 286–302, 2020
2020
Cited alongside, same era.
X. Xiao, B. Liu, G. Warnell, J. Fink, and P. Stone, “Appld: Adaptive planner parameter learning from demonstration,” IEEE Robotics and Automation Letters , vol. 5, no. 3, pp. 4541–4547, 2020
2020
Cited alongside, same era.
L. Hewing, K. P. Wabersich, M. Menner, and M. N. Zeilinger, “Learning-based model predictive control: Toward safe learning in control,” Annual Review of Control, Robotics, and Autonomous Systems , vol. 3, pp. 269–296, 2020
2020
Cited alongside, same era.
X. Xiao, B. Liu, G. Warnell, and P. Stone, “Toward agile maneuvers in highly constrained spaces: Learning from hallucination,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 1503–1510, 2021
2021
Cited alongside, same era.
X. Xiao, B. Liu, and 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
Cited alongside, same era.
Z. Xu, G. Dhamankar, A. Nair, X. Xiao, G. Warnell, B. Liu, Z. Wang, and 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
Later among the works it cites.
2021
Later among the works it cites.
X. Xiao, J. Biswas, and P. Stone, “Learning inverse kinodynamics for accurate high-speed off-road navigation on unstructured terrain,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 6054–6060, 2021
2021
Later among the works it cites.
X. Xiao, B. Liu, G. Warnell, and P. Stone, “Motion control for mobile robot navigation using machine learning: a survey,” Autonomous Robots , 2022
2022
Closest in time.
K. S. Sikand, S. Rabiee, A. Uccello, X. Xiao, G. Warnell, and J. Biswas, “Visual representation learning for preference-aware path planning,” in 2022 ieee international conference on robotics and automation (icra) . IEEE, 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
P. Atreya and J. Biswas, “State supervised steering function for sampling-based kinodynamic planning.” in AAMAS , 2022, pp. 35–43
2022
Closest in time.