Fetching the paper…
Reading the bibliography…
Despite its importance in both industrial and service robotics, mobile manipulation remains a significant challenge as it requires a seamless integration of end-effector trajectory generation with navigation skills as well as reasoning over long-horizons.
L. P. Kaelbling, “Learning to achieve goals,” in Proc. of the 13th Int. Joint Conference on Artificial Intelligence , 1993, pp. 1094–1098
1993
Earlier work this paper cites.
L. P. Kaelbling, “Hierarchical learning in stochastic domains: Preliminary results,” in Proc. of the Int. Conf. on Machine Learning , vol. 951, 1993, pp. 167–173
1993
Earlier work this paper cites.
R. S. Sutton, D. Precup, and S. Singh, “Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning,” Artificial intelligence , vol. 112, no. 1-2, pp. 181–211, 1999
1999
Earlier work this paper cites.
J. Kuffner and S. LaValle, “Rrt-connect: An efficient approach to single-query path planning,” in Int. Conf. on Robotics & Automation , vol. 2, 2000, pp. 995–1001
2000
Earlier work this paper cites.
R. Diankov, S. S. Srinivasa, D. Ferguson, and J. Kuffner, “Manipulation planning with caging grasps,” in Int. Conf. on Humanoid Robots . IEEE, 2008, pp. 285–292
2008
Earlier work this paper cites.
S. Karaman and E. Frazzoli, “Sampling-based algorithms for optimal motion planning,” Int. Journal of Robotics Research , vol. 30, no. 7, pp. 846–894, 2011
2011
Earlier work this paper cites.
D. Berenson, S. Srinivasa, and J. Kuffner, “Task space regions: A framework for pose-constrained manipulation planning,” Int. Journal of Robotics Research , vol. 30, no. 12, pp. 1435–1460, 2011
2011
Earlier work this paper cites.
N. Vahrenkamp, T. Asfour, and R. Dillmann, “Robot placement based on reachability inversion,” in Int. Conf. on Robotics & Automation , 2013
2013
Earlier work this paper cites.
F. Burget, A. Hornung, and M. Bennewitz, “Whole-body motion planning for manipulation of articulated objects,” in Int. Conf. on Robotics & Automation , 2013, pp. 1656–1662
2013
Earlier work this paper cites.
O. Arslan and P. Tsiotras, “Use of relaxation methods in sampling-based algorithms for optimal motion planning,” in Int. Conf. on Robotics & Automation . IEEE, 2013, pp. 2421–2428
2013
Earlier work this paper cites.
T. El-Gaaly, C. Tomaszewski, A. Valada, P. Velagapudi, B. Kannan, and P. Scerri, “Visual obstacle avoidance for autonomous watercraft using smartphones,” Autonomous Robots and Multirobot Systems Workshop (ARMS) , 2013
2013
Earlier work this paper cites.
D. Leidner, A. Dietrich, F. Schmidt, C. Borst, and A. Albu-Schäffer, “Object-centered hybrid reasoning for whole-body mobile manipulation,” in Int. Conf. on Robotics & Automation , 2014, pp. 1828–1835
2014
Earlier work this paper cites.
D. Coleman, I. Sucan, S. Chitta, and N. Correll, “Reducing the barrier to entry of complex robotic software: a moveit! case study,” Journal of Software Engineering for Robotics , vol. 5, no. 1, pp. 3–16, 2014
2014
Earlier work this paper cites.
M. Otte and E. Frazzoli, “Rrtx: Real-time motion planning/replanning for environments with unpredictable obstacles,” in Algorithmic Foundations of Robotics XI . Springer, 2015, pp. 461–478
2015
Earlier work this paper cites.
T. Schaul, D. Horgan, K. Gregor, and D. Silver, “Universal value function approximators,” in Proc. of the 32nd Int. Conf. on Machine Learning , vol. 37, 2015, pp. 1312–1320
2015
Earlier work this paper cites.
F. Burget, M. Bennewitz, and W. Burgard, “Bi2rrt*: An efficient sampling-based path planning framework for task-constrained mobile manipulation,” in Int. Conf. on Intelligent Robots and Systems , 2016
2016
Earlier work this paper cites.
F. Paus, P. Kaiser, N. Vahrenkamp, and T. Asfour, “A combined approach for robot placement and coverage path planning for mobile manipulation,” in Int. Conf. on Intelligent Robots and Systems , 2017
2017
Earlier work this paper cites.
R. Ancona, “Redundancy modelling and resolution for robotic mobile manipulators: a general approach,” Advanced Robotics , vol. 31, no. 13, pp. 706–715, 2017
2017
Earlier work this paper cites.
H. Oleynikova, Z. Taylor, M. Fehr, R. Siegwart, and J. Nieto, “Voxblox: Incremental 3d euclidean signed distance fields for on-board mav planning,” in Int. Conf. on Intelligent Robots and Systems , 2017
2017
Earlier work this paper cites.
P.-L. Bacon, J. Harb, and D. Precup, “The option-critic architecture,” in Proc. of the National Conference on Artificial Intelligence , 2017
2017
Earlier work this paper cites.
T. Welschehold, C. Dornhege, and W. Burgard, “Learning mobile manipulation actions from human demonstrations,” in Int. Conf. on Intelligent Robots and Systems , 2017
2017
Earlier work this paper cites.
T. Welschehold, C. Dornhege, F. Paus, T. Asfour, and W. Burgard, “Coupling mobile base and end-effector motion in task space,” in Int. Conf. on Intelligent Robots and Systems , 2018
2018
Earlier work this paper cites.
M. Toussaint, K. R. Allen, K. A. Smith, and J. B. Tenenbaum, “Differentiable physics and stable modes for tool-use and manipulation planning,” in Robotics: Science and Systems , 2018
2018
Earlier work this paper cites.
C. R. Garrett, T. Lozano-Perez, and L. P. Kaelbling, “Ffrob: Leveraging symbolic planning for efficient task and motion planning,” Int. Journal of Robotics Research , vol. 37, no. 1, pp. 104–136, 2018
2018
Cited alongside, same era.
M. Everett, Y. F. Chen, and J. P. How, “Motion planning among dynamic, decision-making agents with deep reinforcement learning,” in Int. Conf. on Intelligent Robots and Systems , 2018, pp. 3052–3059
2018
Cited alongside, same era.
P. Ruppel, N. Hendrich, S. Starke, and J. Zhang, “Cost functions to specify full-body motion and multi-goal manipulation tasks,” in Int. Conf. on Robotics & Automation . IEEE, 2018, pp. 3152–3159
2018
Cited alongside, same era.
F. Pardo, A. Tavakoli, V. Levdik, and P. Kormushev, “Time limits in reinforcement learning,” in Proc. of the Int. Conf. on Machine Learning , vol. 80, 2018, pp. 4045–4054
2018
Cited alongside, same era.
2021
Later among the works it cites.
M. V. Minniti, R. Grandia, K. Fäh, F. Farshidian, and M. Hutter, “Model predictive robot-environment interaction control for mobile manipulation tasks,” in Int. Conf. on Robotics & Automation , 2021
2021
Later among the works it cites.
F. Xia, C. Li, R. Martín-Martín, O. Litany, A. Toshev, and S. Savarese, “Relmogen: Leveraging motion generation in reinforcement learning for mobile manipulation,” in Int. Conf. on Robotics & Automation , 2021
2021
Later among the works it cites.
D. Honerkamp, T. Welschehold, and A. Valada, “Learning kinematic feasibility for mobile manipulation through deep reinforcement learning,” IEEE Robotics and Automation Letters , 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…
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in Proc. of the Int. Conf. on Machine Learning , vol. 80, 2018, pp. 1861–1870
2018
Cited alongside, same era.
AWS RoboMaker, “aws-robomaker-bookstore-world,” https://github.com/aws-robotics/aws-robomaker-bookstore-world , 2019
2018
Cited alongside, same era.
T. Chen, S. Gupta, and A. Gupta, “Learning exploration policies for navigation,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
L. Han, F. Gao, B. Zhou, and S. Shen, “Fiesta: Fast incremental euclidean distance fields for online motion planning of aerial robots,” in Int. Conf. on Intelligent Robots and Systems . IEEE, 2019, pp. 4423–4430
2019
Cited alongside, same era.
C. Li, F. Xia, R. Martin, and S. Savarese, “Hrl4in: Hierarchical reinforcement learning for interactive navigation with mobile manipulators,” in Conference on Robot Learning , 2019
2019
Cited alongside, same era.
2020
Cited alongside, same era.
J. Pankert and M. Hutter, “Perceptive model predictive control for continuous mobile manipulation,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 6177–6184, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
M. Arduengo, C. Torras, and L. Sentis, “Robust and adaptive door operation with a mobile robot,” Intelligent Service Robotics , vol. 14, no. 3, pp. 409–425, 2021
2021
Later among the works it cites.
J. Liu, P. Balatti, K. Ellis, D. Hadjivelichkov, D. Stoyanov, A. Ajoudani, and D. Kanoulas, “Garbage collection and sorting with a mobile manipulator using deep learning and whole-body control,” in Int. Conf. on Humanoid Robots , 2021, pp. 408–414
2021
Later among the works it cites.
J. Sleiman, F. Farshidian, M. V. Minniti, and M. Hutter, “A unified MPC framework for whole-body dynamic locomotion and manipulation,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 4688–4695, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
C. R. Garrett, R. Chitnis, R. Holladay, B. Kim, T. Silver, L. P. Kaelbling, and T. Lozano-Perez, “Integrated task and motion planning,” Annual review of control, robotics, and autonomous systems , vol. 4, 2021
2021
Later among the works it cites.
D. Hoeller, L. Wellhausen, F. Farshidian, and M. Hutter, “Learning a state representation and navigation in cluttered and dynamic environments,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 5081–5088, 2021
2021
Later among the works it cites.
U. Patel, N. Kumar, A. J. Sathyamoorthy, and D. Manocha, “Dwa-rl: Dynamically feasible deep reinforcement learning policy for robot navigation in dense mobile crowds,” in Int. Conf. on Robotics & Automation , 2021
2021
Later among the works it cites.
K. Ehsani, W. Han, A. Herrasti, E. VanderBilt, L. Weihs, E. Kolve, A. Kembhavi, and R. Mottaghi, “Manipulathor: A framework for visual object manipulation,” in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition , 2021, pp. 4497–4506
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Duan, S. Yu, H. L. Tan, H. Zhu, and C. Tan, “A survey of embodied ai: From simulators to research tasks,” IEEE Transactions on Emerging Topics in Computational Intelligence , 2022
2022
Closest in time.
J. Wong, A. Tung, A. Kurenkov, A. Mandlekar, L. Fei-Fei, S. Savarese, and R. Martín-Martín, “Error-aware imitation learning from teleoperation data for mobile manipulation,” in Proc. of the Conference on Robot Learning , A. Faust, D. Hsu, and G. Neumann, Eds., vol. 164, 2022, pp. 1367–1378
2022
Closest in time.
F. Schmalstieg, D. Honerkamp, T. Welschehold, and A. Valada, “Learning long-horizon robot exploration strategies for multi-object search in continuous action spaces,” Proceedings of the International Symposium on Robotics Research (ISRR) , 2022
2022
Closest in time.
2022
Closest in time.
J. Haviland, N. Sunderhauf, and P. Corke, “A holistic approach to reactive mobile manipulation,” IEEE Robotics and Automation Letters , 2022
2022
Closest in time.
2022
Closest in time.
S. Srivastava, C. Li, M. Lingelbach, R. Martín-Martín, F. Xia, K. E. Vainio, Z. Lian, C. Gokmen, S. Buch, K. Liu et al. , “Behavior: Benchmark for everyday household activities in virtual, interactive, and ecological environments,” in Proc. of the Conference on Robot Learning , 2022, pp. 477–490
2022
Closest in time.
A. Röfer, G. Bartels, W. Burgard, A. Valada, and M. Beetz, “Kineverse: A symbolic articulation model framework for model-agnostic mobile manipulation,” IEEE Robotics and Automation Letters , 2022
2022
Closest in time.