LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planning
暂无分享,去创建一个
Siddhartha S. Srinivasa | Sanjiban Choudhury | Aditya Mandalika | Rahul Kumar | S. Srinivasa | Sanjiban Choudhury | Rahul Kumar | Aditya Mandalika
[1] David Hsu,et al. Workspace-Based Connectivity Oracle: An Adaptive Sampling Strategy for PRM Planning , 2006, WAFR.
[2] B. Faverjon,et al. Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .
[3] Daniel Vallejo,et al. OBPRM: an obstacle-based PRM for 3D workspaces , 1998 .
[4] David Hsu,et al. Workspace importance sampling for probabilistic roadmap planning , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).
[5] Daniel D. Lee,et al. Learning Implicit Sampling Distributions for Motion Planning , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[6] Boris Katz,et al. Deep Sequential Models for Sampling-Based Planning , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[7] James J. Kuffner,et al. Randomized statistical path planning , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[8] Maximilian Karl,et al. Dynamic movement primitives in latent space of time-dependent variational autoencoders , 2016, 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids).
[9] Yisong Yue,et al. Generating Long-term Trajectories Using Deep Hierarchical Networks , 2016, NIPS.
[10] Oliver Brock,et al. Toward Optimal Configuration Space Sampling , 2005, Robotics: Science and Systems.
[11] Oliver Brock,et al. Sampling-Based Motion Planning Using Predictive Models , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.
[12] Nils J. Nilsson,et al. A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..
[13] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[14] Carl Doersch,et al. Tutorial on Variational Autoencoders , 2016, ArXiv.
[15] Jean-Claude Latombe,et al. On the Probabilistic Foundations of Probabilistic Roadmap Planning , 2006, Int. J. Robotics Res..
[16] David Hsu,et al. The bridge test for sampling narrow passages with probabilistic roadmap planners , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).
[17] J. Halton. On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals , 1960 .
[18] Steven M. LaValle,et al. Planning algorithms , 2006 .
[19] Mark H. Overmars,et al. The Gaussian sampling strategy for probabilistic roadmap planners , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).
[20] Gildardo Sánchez-Ante,et al. Hybrid PRM Sampling with a Cost-Sensitive Adaptive Strategy , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.
[21] Lydia Tapia,et al. A Machine Learning Approach for Feature-Sensitive Motion Planning , 2004, WAFR.
[22] Lydia E. Kavraki,et al. A framework for using the workspace medial axis in PRM planners , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).
[23] Siddhartha S. Srinivasa,et al. Informed RRT*: Optimal Incremental Path Planning Focused through an Admissible Ellipsoidal Heuristic , 2014, ArXiv.
[24] Daniel D. Lee,et al. Learning Dimensional Descent for Optimal Motion Planning in High-dimensional Spaces , 2011, AAAI.
[25] Marco Pavone,et al. Robot Motion Planning in Learned Latent Spaces , 2018, IEEE Robotics and Automation Letters.
[26] Jean-Paul Laumond,et al. Linear dimensionality reduction in random motion planning , 2011, Int. J. Robotics Res..
[27] Marco Pavone,et al. Deterministic sampling-based motion planning: Optimality, complexity, and performance , 2015, ISRR.
[28] Han-Lim Choi,et al. Approximate Inference-Based Motion Planning by Learning and Exploiting Low-Dimensional Latent Variable Models , 2018, IEEE Robotics and Automation Letters.
[29] Michael C. Yip,et al. Deeply Informed Neural Sampling for Robot Motion Planning , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[30] Siddhartha S. Srinivasa,et al. Pareto-optimal search over configuration space beliefs for anytime motion planning , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[31] Marco Pavone,et al. Learning Sampling Distributions for Robot Motion Planning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[32] Rajeev Motwani,et al. Path Planning in Expansive Configuration Spaces , 1999, Int. J. Comput. Geom. Appl..
[33] James J. Kuffner,et al. Adaptive workspace biasing for sampling-based planners , 2008, 2008 IEEE International Conference on Robotics and Automation.
[34] Dinesh Manocha,et al. Faster Sample-Based Motion Planning Using Instance-Based Learning , 2012, WAFR.
[35] Honglak Lee,et al. Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.
[36] Nancy M. Amato,et al. An obstacle-based rapidly-exploring random tree , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..
[37] Wojciech Zaremba,et al. Domain randomization for transferring deep neural networks from simulation to the real world , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[38] Ross A. Knepper,et al. Herb 2.0: Lessons Learned From Developing a Mobile Manipulator for the Home , 2012, Proceedings of the IEEE.
[39] Lydia E. Kavraki,et al. Using Local Experiences for Global Motion Planning , 2019, 2019 International Conference on Robotics and Automation (ICRA).
[40] Nancy M. Amato,et al. MAPRM: a probabilistic roadmap planner with sampling on the medial axis of the free space , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).
[41] Mark H. Overmars,et al. Using Workspace Information as a Guide to Non-uniform Sampling in Probabilistic Roadmap Planners , 2005, Int. J. Robotics Res..
[42] Marco Pavone,et al. Deterministic Sampling-Based Motion Planning: Optimality, Complexity, and Performance , 2015, ISRR.
[43] Nancy M. Amato,et al. RESAMPL: A Region-Sensitive Adaptive Motion Planner , 2008, WAFR.