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Collision-free motion generation in unknown environments is a core building block for robot manipulation.
A formal basis for the heuristic determination of minimum cost paths
P. E. Hart, N. J. Nilsson, and B. Raphael · 1968
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Real-time obstacle avoidance for manipulators and mobile robots
O. Khatib · 1986
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Alvinn: An autonomous land vehicle in a neural network
D. A. Pomerleau · 1988
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Formation and control of optimal trajectory in human multijoint arm movement
Y. Uno, M. Kawato, and R. Suzuki · 1989
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A framework for behavioural cloning
M. Bain and C. Sammut · 1995
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Real-time pose estimation of 3d objects from camera images using neural networks
P. Wunsch, S. Winkler, and G. Hirzinger · 1997
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Rapidly-exploring random trees : a new tool for path planning
S. M. LaValle · 1998
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Learning agents for uncertain environments (extended abstract)
S. J. Russell · 1998
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Algorithms for inverse reinforcement learning
A. Y. Ng and S. J. Russell · 2000
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Ara*: Anytime a* with provable bounds on sub-optimality
M. Likhachev, G. J. Gordon, and S. Thrun · 2003
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Anytime dynamic a*: An anytime, replanning algorithm
M. Likhachev, D. Ferguson, G. J. Gordon, A. Stentz, and S. Thrun · 2005
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Planning algorithms
S. M. LaValle · 2006
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Maximum entropy inverse reinforcement learning
B. D. Ziebart, A. L. Maas, J. A. Bagnell, and A. K. Dey · 2008
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Chomp: Gradient optimization techniques for efficient motion planning
N. D. Ratliff, M. Zucker, J. A. Bagnell, and S. S. Srinivasa · 2009
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Efficient reductions for imitation learning
S. Ross and A. Bagnell · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G. Gordon, and A. Bagnell · 2010
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Automated Construction of Robotic Manipulation Programs
R. Diankov · 2010
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Fast smoothing of manipulator trajectories using optimal bounded-acceleration shortcuts
K. K. Hauser and V. Ng-Thow-Hing · 2010
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Sampling-based algorithms for optimal motion planning
S. Karaman and E. Frazzoli · 2011
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Kinectfusion: Real-time dense surface mapping and tracking
R. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohli, J. Shotton, S. Hodges, and A. Fitzgibbon · 2011
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A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G. J. Gordon, and J. A. Bagnell · 2011
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Moveit![ros topics]
S. Chitta, I. Sucan, and S. Cousins · 2012
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The Open Motion Planning Library
I. A. Şucan, M. Moll, and L. E. Kavraki · 2012
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Time-optimal trajectory generation for path following with bounded acceleration and velocity
T. Kunz and M. Stilman · 2012
Cited alongside, same era.
Fast interpolation and time-optimization on implicit contact submanifolds
K. K. Hauser · 2013
Cited alongside, same era.
Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
Cited alongside, same era.
Motion planning with sequential convex optimization and convex collision checking
J. Schulman, Y. Duan, J. Ho, A. X. Lee, I. Awwal, H. Bradlow, J. Pan, S. Patil, K. Goldberg, and P. Abbeel · 2014
Cited alongside, same era.
Batch informed trees (bit*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs
J. D. Gammell, S. S. Srinivasa, and T. D. Barfoot · 2015
Cited alongside, same era.
Neural collision clearance estimator for batched motion planning
J. C. Kew, B. Ichter, M. Bandari, T.-W. E. Lee, and A. Faust · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove · 2019
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Harnessing Reinforcement Learning for Neural Motion Planning
T. Jurgenson and A. Tamar · 2019
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Exploring the limitations of behavior cloning for autonomous driving
F. Codevilla, E. Santana, A. M. López, and A. Gaidon · 2019
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Lego: Leveraging experience in roadmap generation for sampling-based planning
R. Kumar, A. Mandalika, S. Choudhury, and S. S. Srinivasa · 2019
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N. D. Ratliff, M. Toussaint, and S. Schaal · 2015
Cited alongside, same era.
On the analysis of movement smoothness
S. Balasubramanian, A. Melendez-Calderon, A. Roby-Brami, and E. Burdet · 2015
Cited alongside, same era.
The ycb object and model set: Towards common benchmarks for manipulation research
B. Çalli, A. Singh, A. Walsman, S. S. Srinivasa, P. Abbeel, and A. M. Dollar · 2015
Cited alongside, same era.
Value iteration networks
A. Tamar, Y. Wu, G. Thomas, S. Levine, and P. Abbeel · 2016
Cited alongside, same era.
Seeing through the human reporting bias: Visual classifiers from noisy human-centric labels
I. Misra, C. L. Zitnick, M. Mitchell, and R. B. Girshick · 2016
Cited alongside, same era.
Learning visual features from large weakly supervised data
A. Joulin, L. van der Maaten, A. Jabri, and N. Vasilache · 2016
Cited alongside, same era.
Dart: Noise injection for robust imitation learning
M. Laskey, J. N. Lee, R. Fox, A. D. Dragan, and K. Goldberg · 2017
Cited alongside, same era.
A. Mousavian, C. Eppner, and D. Fox · 2019
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Advanced bit* (abit*): Sampling-based planning with advanced graph-search techniques
M. P. Strub and J. D. Gammell · 2020
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Adaptively informed trees (ait*): Fast asymptotically optimal path planning through adaptive heuristics
M. P. Strub and J. D. Gammell · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng · 2020
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Learning obstacle representations for neural motion planning
R. A. M. Strudel, R. G. Pinel, J. Carpentier, J.-P. Laumond, I. Laptev, and C. Schmid · 2020
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6-dof grasping for target-driven object manipulation in clutter
A. Murali, A. Mousavian, C. Eppner, C. Paxton, and D. Fox · 2020
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Collaborative interaction models for optimized human-robot teamwork
A. Fishman, C. Paxton, W. Yang, D. Fox, B. Boots, and N. D. Ratliff · 2020
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STORM: An integrated framework for fast joint-space model-predictive control for reactive manipulation
M. Bhardwaj, B. Sundaralingam, A. Mousavian, N. D. Ratliff, D. Fox, F. Ramos, and B. Boots · 2021
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Object rearrangement using learned implicit collision functions
M. Danielczuk, A. Mousavian, C. Eppner, and D. Fox · 2021
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Nerp: Neural rearrangement planning for unknown objects
A. H. Qureshi, A. Mousavian, C. Paxton, M. C. Yip, and D. Fox · 2021
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Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes
M. Sundermeyer, A. Mousavian, R. Triebel, and D. Fox · 2021
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Grasping with chopsticks: Combating covariate shift in model-free imitation learning for fine manipulation
L. Ke, J. Wang, T. Bhattacharjee, B. Boots, and S. S. Srinivasa · 2021
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What matters in learning from offline human demonstrations for robot manipulation
A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y. Zhu, and R. Martín-Martín · 2021
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Learning sampling distributions using local 3d workspace decompositions for motion planning in high dimensions
C. Chamzas, Z. K. Kingston, C. Quintero-Peña, A. Shrivastava, and L. E. Kavraki · 2021
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Differentiable spatial planning using transformers
D. S. Chaplot, D. Pathak, and J. Malik · 2021
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C-learning: Learning to achieve goals via recursive
B. Eysenbach, R. Salakhutdinov, and S. Levine · 2021
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Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever · 2021
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Geometric fabrics: Generalizing classical mechanics to capture the physics of behavior
K. V. Wyk, M. Xie, A. Li, M. A. Rana, B. Babich, B. N. Peele, Q. Wan, I. Akinola, B. Sundaralingam, D. Fox, B. Boots, and N. D. Ratliff · 2022
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