Fetching the paper…
Reading the bibliography…
We study how robots can autonomously learn skills that require a combination of navigation and grasping.
Pyrobot: An open-source robotics framework for research and benchmarking
A. Murali, T. Chen, K. V. Alwala, D. Gandhi, L. Pinto, S. Gupta, and A. Gupta · 1906
Earlier work this paper cites.
Monte carlo localization: Efficient position estimation for mobile robots
D. Fox, W. Burgard, F. Dellaert, and S. Thrun · 1999
Earlier work this paper cites.
Robust monte carlo localization for mobile robots
S. Thrun, D. Fox, W. Burgard, and F. Dellaert · 2001
Earlier work this paper cites.
Towards robotic assistants in nursing homes: Challenges and results
J. Pineau, M. Montemerlo, M. Pollack, N. Roy, and S. Thrun · 2003
Earlier work this paper cites.
Navigation among movable obstacles: Real-time reasoning in complex environments
M. Stilman and J. J. Kuffner · 2005
Earlier work this paper cites.
Development of outdoor service robots
T. Nishida, Y. Takemura, Y. Fuchikawa, S. Kurogi, S. Ito, M. Obata, N. Hiratsuka, H. Miyagawa, Y. Watanabe, F. Koga, et al · 2006
Earlier work this paper cites.
Ecological reinforcement learning
J. D. Co-Reyes, S. Sanjeev, G. Berseth, A. Gupta, and S. Levine · 2006
Earlier work this paper cites.
Structure-based color learning on a mobile robot under changing illumination
M. Sridharan and P. Stone · 2007
Earlier work this paper cites.
Flying fast and low among obstacles: Methodology and experiments
S. A. Scherer, S. Singh, L. Chamberlain, and M. Elgersma · 2008
Earlier work this paper cites.
Learning and performing place-based mobile manipulation
F. Stulp, A. Fedrizzi, and M. Beetz · 2009
Earlier work this paper cites.
Combining analysis, imitation, and experience-based learning to acquire a concept of reachability in robot mobile manipulation
F. Stulp, A. Fedrizzi, F. Zacharias, M. Tenorth, J. Bandouch, and M. Beetz · 2009
Earlier work this paper cites.
Learning accuracy and availability of humans who help mobile robots
S. Rosenthal, M. Veloso, and A. K. Dey · 2011
Earlier work this paper cites.
Unifying perception, estimation and action for mobile manipulation via belief space planning
L. P. Kaelbling and T. Lozano-Pérez · 2012
Earlier work this paper cites.
One-shot visual appearance learning for mobile manipulation
M. R. Walter, Y. Friedman, M. Antone, and S. Teller · 2012
Earlier work this paper cites.
Localization and navigation of the cobots over long-term deployments
J. Biswas and M. M. Veloso · 2013
Earlier work this paper cites.
Deep learning for detecting robotic grasps
I. Lenz, H. Lee, and A. Saxena · 2015
Earlier work this paper cites.
Learning compound multi-step controllers under unknown dynamics
W. Han, S. Levine, and P. Abbeel · 2015
Earlier work this paper cites.
Learning to poke by poking: Experiential learning of intuitive physics
P. Agrawal, A. V. Nair, P. Abbeel, J. Malik, and S. Levine · 2016
Cited alongside, same era.
Persistent localization and life-long mapping in changing environments using the frequency map enhancement
T. Krajník, J. P. Fentanes, M. Hanheide, and T. Duckett · 2016
Cited alongside, same era.
Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
L. Pinto and A. Gupta · 2016
Cited alongside, same era.
Path integral guided policy search
Y. Chebotar, M. Kalakrishnan, A. Yahya, A. Li, S. Schaal, and S. Levine · 2017
Cited alongside, same era.
Combining model-based and model-free updates for trajectory-centric reinforcement learning
Y. Chebotar, K. Hausman, M. Zhang, G. Sukhatme, S. Schaal, and S. Levine · 2017
Cited alongside, same era.
Deep predictive policy training using reinforcement learning
Leave no trace: Learning to reset for safe and autonomous reinforcement learning
B. Eysenbach, S. Gu, J. Ibarz, and S. Levine · 2018
Later among the works it cites.
Is q-learning provably efficient?
C. Jin, Z. Allen-Zhu, S. Bubeck, and M. I. Jordan · 2018
Later among the works it cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
Later among the works it cites.
The ingredients of real world robotic reinforcement learning
H. Zhu, J. Yu, A. Gupta, D. Shah, K. Hartikainen, A. Singh, V. Kumar, and S. Levine · 2019
Later among the works it cites.
Deep dynamics models for learning dexterous manipulation
A. Nagabandi, K. Konolige, S. Levine, and V. Kumar · 2019
Later among the works it cites.
Learning to walk via deep reinforcement learning
T. Haarnoja, S. Ha, A. Zhou, J. Tan, G. Tucker, and S. Levine · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Ghadirzadeh, A. Maki, D. Kragic, and M. Björkman · 2017
Cited alongside, same era.
Learning mobile manipulation actions from human demonstrations
T. Welschehold, C. Dornhege, and W. Burgard · 2017
Cited alongside, same era.
Grasp pose detection in point clouds
A. ten Pas, M. Gualtieri, K. Saenko, and R. Platt · 2017
Cited alongside, same era.
Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics
J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. Aparicio, and K. Goldberg · 2017
Cited alongside, same era.
Mobile manipulation for planetary exploration
P. Lehner, S. Brunner, A. Domel, H. Gmeiner, S. Riedel, B. Vodermayer, and A. Wedler · 2018
Cited alongside, same era.
Autotrans: an autonomous open world transportation system
B. S. Zapata-Impata, V. Shah, H. Singh, and R. W. Platt · 2018
Cited alongside, same era.
Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation
G. Kahn, A. Villaflor, B. Ding, P. Abbeel, and S. Levine · 2018
Cited alongside, same era.
Later among the works it cites.
Learning mobile manipulation through deep reinforcement learning
C. Wang, Q. Zhang, Q. Tian, S. Li, X. Wang, D. Lane, Y. R. Petillot, and S. Wang · 2020
Later among the works it cites.
Spatial Action Maps for Mobile Manipulation
J. Wu, X. Sun, A. Zeng, S. Song, J. Lee, S. Rusinkiewicz, and T. Funkhouser · 2020
Later among the works it cites.
Whole-body control of a mobile manipulator using end-to-end reinforcement learning
J. Kindle, F. Furrer, T. Novkovic, J. J. Chung, R. Siegwart, and J. Nieto · 2020
Later among the works it cites.
Hrl4in: Hierarchical reinforcement learning for interactive navigation with mobile manipulators
C. Li, F. Xia, R. Martín-Martín, and S. Savarese · 2020
Later among the works it cites.
ReLMoGen: Leveraging motion generation in reinforcement learning for mobile manipulation
F. Xia, C. Li, R. Martín-Martín, O. Litany, A. Toshev, and S. Savarese · 2020
Later among the works it cites.
Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
T. Lesort, V. Lomonaco, A. Stoian, D. Maltoni, D. Filliat, and N. Díaz-Rodríguez · 2020
Later among the works it cites.
Learning to Manipulate Deformable Objects without Demonstrations
Y. Wu, W. Yan, T. Kurutach, L. Pinto, and P. Abbeel · 2020
Later among the works it cites.
Articulated object interaction in unknown scenes with whole-body mobile manipulation
M. Mittal, D. Hoeller, F. Farshidian, M. Hutter, and A. Garg · 2021
Closest in time.
Learning kinematic feasibility for mobile manipulation through deep reinforcement learning
D. Honerkamp, T. Welschehold, and A. Valada · 2021
Closest in time.
Badgr: An autonomous self-supervised learning-based navigation system
G. Kahn, P. Abbeel, and S. Levine · 2021
Closest in time.