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We present a closed-loop multi-arm motion planner that is scalable and flexible with team size.
Multi-agent reinforcement learning: Independent vs. cooperative agents
M. Tan · 1993
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Probabilistic roadmaps for path planning in high-dimensional configuration spaces
L. E. Kavraki, P. Svestka, J. . Latombe, and M. H. Overmars · 1996
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Rapidly-exploring random trees: A new tool for path planning
S. M. LaValle · 1998
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Graspit! a versatile simulator for robotic grasping
A. T. Miller and P. K. Allen · 2004
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Prioritized motion planning for multiple robots
J. P. van den Berg and M. H. Overmars · 2005
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Roadmap-based motion planning in dynamic environments
J. P. van den Berg and M. H. Overmars · 2005
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Planning algorithms
S. M. LaValle · 2006
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Sampling-based algorithms for optimal motion planning
S. Karaman and E. Frazzoli · 2011
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A robot path planning framework that learns from experience
D. Berenson, P. Abbeel, and K. Goldberg · 2012
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Pose error robust grasping from contact wrench space metrics
J. Weisz and P. K. Allen · 2012
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K. Solovey, O. Salzman, and D. Halperin · 2013
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Fast motion planning from experience: trajectory prediction for speeding up movement generation
N. Jetchev and M. Toussaint · 2013
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Optimal reciprocal collision avoidance for multiple non-holonomic robots
J. Alonso-Mora, A. Breitenmoser, M. Rufli, P. Beardsley, and R. Siegwart · 2013
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Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments
A. H. Qureshi and Y. Ayaz · 2015
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Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
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Benchmarking in manipulation research: Using the yale-cmu-berkeley object and model set
B. Calli, A. Walsman, A. Singh, S. Srinivasa, P. Abbeel, and A. M. Dollar · 2015
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Motion planning for multilink robots by implicit configuration-space tiling
O. Salzman, K. Solovey, and D. Halperin · 2016
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Deeply informed neural sampling for robot motion planning
A. H. Qureshi and M. C. Yip · 2018
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Auto-conditioned recurrent mixture density networks for learning generalizable robot skills
H. Zhang, E. Heiden, S. Nikolaidis, J. J. Lim, and G. S. Sukhatme · 2018
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Towards Neural Network Embeddings of Optimal Motion Planners
M. J. Bency · 2018
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Prm-rl: Long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning
A. Faust, K. Oslund, O. Ramirez, A. Francis, L. Tapia, M. Fiser, and J. Davidson · 2018
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Fast, high-quality dual-arm rearrangement in synchronous, monotone tabletop setups
R. Shome, K. Solovey, J. Yu, K. E. Bekris, and D. Halperin · 2018
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Potential functions based sampling heuristic for optimal path planning
A. H. Qureshi and Y. Ayaz · 2016
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Value iteration networks
A. Tamar, Y. Wu, G. Thomas, S. Levine, and P. Abbeel · 2016
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Simultaneous dual-arm motion planning for minimizing operation time
J. Kurosu, A. Yorozu, and M. Takahashi · 2017
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Learning sampling distributions for robot motion planning
B. Ichter, J. Harrison, and M. Pavone · 2017
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From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots
M. Pfeiffer, M. Schaeuble, J. Nieto, R. Siegwart, and C. Cadena · 2017
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Pybullet, a python module for physics simulation in robotics, games and machine learning, 2017
E. Coumans and Y. Bai · 2017
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A unified framework for coordinated multi-arm motion planning
S. S. Mirrazavi Salehian, N. Figueroa, and A. Billard · 2018
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M. Everett, Y. F. Chen, and J. P. How · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine · 2018
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Motion planning networks
A. H. Qureshi, A. Simeonov, M. J. Bency, and M. C. Yip · 2019
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drrt*: Scalable and informed asymptotically-optimal multi-robot motion planning
R. Shome, K. Solovey, A. Dobson, D. Halperin, and K. E. Bekris · 2019
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Harnessing reinforcement learning for neural motion planning
T. Jurgenson and A. Tamar · 2019
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Anytime multi-arm task and motion planning for pick-and-place of individual objects via handoffs
R. Shome and K. E. Bekris · 2019
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Emergent tool use from multi-agent autocurricula
B. Baker, I. Kanitscheider, T. Markov, Y. Wu, G. Powell, B. McGrew, and I. Mordatch · 2019
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Multi-agent motion planning for dense and dynamic environments via deep reinforcement learning
S. H. Semnani, H. Liu, M. Everett, A. de Ruiter, and J. P. How · 2020
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