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项目作者： yash-goel

项目描述 ：
For Robotics and Robot Learning Resources
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项目地址: git://github.com/yash-goel/reading-list.git
创建时间： 2019-06-02T15:34:45Z
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reading-list

Books/ Extensive Reads

  1. An Algorithmic Perspective on Imitation Learning - https://arxiv.org/pdf/1811.06711.pdf
  2. Factor Graphs for Robot Perception - http://www.cs.cmu.edu/~kaess/pub/Dellaert17fnt.pdf
  3. Introduction to Topology and Modern Analysis
  4. Optimization Algorithms on Matrix Manifolds - http://www.eeci-institute.eu/GSC2011/Photos-EECI/EECI-GSC-2011-M5/book_AMS.pdf
  5. Predictive Uncertainty Quantification with Compound Density Networks - https://wiseodd.github.io/files/cdn_master_thesis.pdf
  6. Belief-space Planning for Active Visual SLAM in Underwater Environments - https://deepblue.lib.umich.edu/handle/2027.42/133303
  7. Adaptive Motion Planning - https://www.ri.cmu.edu/wp-content/uploads/2018/01/Sanjiban-Choudhury-Thesis-2018.pdf
  8. Chance Constrained RRT for Probabilistic Robustness to Environmental Uncertainty - http://acl.mit.edu/papers/Luders10_GNC.pdf
  9. Monte Carlo - https://statweb.stanford.edu/~owen/mc/
  10. Interactive Learning for Sequential Decisions and Predictions - http://www.cs.cmu.edu/~sross1/phd_thesis.pdf
  11. Factor Graphs and GTSAM: A Hands-on Introduction - https://smartech.gatech.edu/bitstream/handle/1853/45226/Factor%20Graphs%20and%20GTSAM%20A%20Hands-on%20Introduction%20GT-RIM-CP%26R-2012-002.pdf?sequence=1&isAllowed=y
  12. Uncertainty in Deep Learning - http://mlg.eng.cam.ac.uk/yarin/thesis/thesis.pdf
  13. Robust Methods for Accurate and Efficient 3D Modeling from Unstructured Imagery
  14. Visual Inertial Odometry and Active Dense Reconstruction for Mobile Robots - https://www.zora.uzh.ch/id/eprint/129478/1/thesis.pdf
  15. Learning Probabilistic Models for Mobile Robot Navigation - http://www2.informatik.uni-freiburg.de/~kretzsch/pdf/kretzschmar14phd.pdf
  16. Efficient Reinforcement Learning with Gaussian Processes

Courses/ Seminars/ Workshops

  1. Math for Intelligent Systems: Marc Toussaint - https://ipvs.informatik.uni-stuttgart.de/mlr/marc/teaching/16-Maths/paper.pdf
  2. Aritificial Intelligence: University of Queensland - http://robotics.itee.uq.edu.au/~ai/doku.php/wiki/home
  3. Statistical Reinforcement Learning: UIUC - https://nanjiang.cs.illinois.edu/cs598/
  4. Learning from Demonstrations: RSS’15 - https://sites.google.com/site/lfdiocrss15/program
  5. Optimization Perspectives on Learning to Control: ICML’18 - https://people.eecs.berkeley.edu/~brecht/l2c-icml2018/
  6. Deep RL Bootcamp - https://sites.google.com/view/deep-rl-bootcamp/lectures
  7. Deep RL Workshop: NeurIPS’18 - https://sites.google.com/view/deep-rl-workshop-nips-2018/home
  8. Infer2Control: NeurIPS’18 - https://sites.google.com/view/infer2control-nips2018/home?authuser=0
  9. Intro to Neural Networks and Machine Learning: UoT - http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/
  10. Probabilistic Learning and Reasoning: UoT - http://www.cs.toronto.edu/~jessebett/CSC412/
  11. Visual Perception for Autonomous Driving: UoT - http://www.cs.toronto.edu/~urtasun/courses/CSC2541/CSC2541_Winter16.html
  12. Robot Learning: NIPS’17 - https://sites.google.com/view/nips17robotlearning/program?authuser=0
  13. Representation Learning in Computer Vision: Stanford - http://web.stanford.edu/class/cs331b/
  14. Memory of Motion:IDIAP - https://memmows.sciencesconf.org/resource/page/id/7
  15. From Least Squares Regression to High-dimensional Motion Primitives: IROS’18 - https://www.idiap.ch/project/iros2018-tutorial/
  16. Advanced Robotics: UBerkeley - https://people.eecs.berkeley.edu/~pabbeel/cs287-fa15/
  17. Intro to AI: UBerkeley - http://ai.berkeley.edu/lecture_videos.html
  18. Graduate Artificial Intelligence:CMU - http://www.cs.cmu.edu/~15780/
  19. Sensing and Estimation in Robotics:UCSD - https://natanaso.github.io/ece276a/index.html
  20. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning: MIT - http://www-math.mit.edu/~gs/
  21. Intelligent control through learning and optimization: UWash - https://homes.cs.washington.edu/~todorov/courses/amath579/index.html
  22. Deep Reinforcement Learning and Control:CMU - https://katefvision.github.io/
  23. Computer Vision: Gatech - https://www.cc.gatech.edu/~hays/compvision/
  24. Vision Algorithms for Mobile Robotics: UoZurich - http://rpg.ifi.uzh.ch/teaching.html
  25. Probabilistic Graphical Models: Stanford - https://ermongroup.github.io/cs228/
  26. Algorithmic Robotics: UMich - http://web.eecs.umich.edu/~dmitryb/courses/fall2018iar/index.html
  27. Robotics: UWash - https://courses.cs.washington.edu/courses/cse571/16au/
  28. Mobile Robotics: Freiburg - http://ais.informatik.uni-freiburg.de/teaching/ss17/robotics/
  29. Stochastic Control: Stanford - https://stanford.edu/class/ee365/index.html
  30. Probabilistic Learning and Reasoning: UoT - http://www.cs.toronto.edu/~jessebett/CSC412/
  31. Deep Learning: GaTech - https://www.cc.gatech.edu/classes/AY2019/cs7643_spring/
  32. Gaussian Process Summer Schools: Multiple Years - http://gpss.cc/past_meetings.html
  33. Machine Learning in Robot Motion Planning - https://personalrobotics.cs.washington.edu/workshops/mlmp2018/
  34. Statistical Learning Theory and Applications: MIT - http://www.mit.edu/~9.520/fall18/
  35. Learning and Sequential Decision Making: Brown - http://cs.brown.edu/courses/cs2951f/
  36. Reinforcement Learning: Stanford - http://web.stanford.edu/class/cs234/index.html

People

  1. Cédric Archambeau - http://www0.cs.ucl.ac.uk/staff/c.archambeau/ - Interesting Probabilistic Material/ Courses
  2. Marc Toussaint - https://ipvs.informatik.uni-stuttgart.de/mlr/marc/teaching/index.html - Probability, Optimization & Learning
  3. Nan Jiang - https://nanjiang.cs.illinois.edu/ - Reinforcement Learning
  4. George Konidaris - https://cs.brown.edu/~gdk/ - Robot Learning
  5. Sylvain Calinon - http://calinon.ch/index.htm - Robot Learning & Human-Robot Interaction
  6. Subramanian Ramamoorthy - http://homepages.inf.ed.ac.uk/sramamoo/
  7. Katharina Muelling - https://sites.google.com/site/katharinamuelling/overview
  8. Katerina Fragkiadaki - https://www.cs.cmu.edu/~katef/
  9. Honglak Lee - http://web.eecs.umich.edu/~honglak/
  10. Natalia Díaz Rodríguez - https://nataliadiaz.github.io/
  11. Roberto Calandra - https://www.robertocalandra.com/about/
  12. Marcello Restelli - http://home.deib.polimi.it/restelli/MyWebSite/index.shtml
  1. STRANDS Project - http://strands.acin.tuwien.ac.at/committee.html
  2. FZI: german institute - https://www.fzi.de/forschung/projekt-details/neuroreact/
  1. Truncated Horizon Policy Search: Combining RL & IL - https://arxiv.org/pdf/1805.11240.pdf
  2. Learning Symbolic Representations for Abstract High-Level Planning - http://irl.cs.brown.edu/pubs/orig_sym_jair.pdf
  3. Towards Learning Abstract Representations for Locomotion Planning in High-dimensional State Space - https://arxiv.org/pdf/1903.02308.pdf
  4. Key Papers in Deep RL - https://spinningup.openai.com/en/latest/spinningup/keypapers.html
  5. Learning Transferable Policies for Monocular Reactive MAV Control - https://arxiv.org/pdf/1608.00627.pdf
  6. Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation - https://arxiv.org/pdf/1709.10489.pdf
  7. An Efficient Reachability-Based Framework for Provably Safe Autonomous Navigation in Unknown Environments - https://arxiv.org/pdf/1905.00532.pdf
  8. Probabilistic Recurrent State-Space Models - https://arxiv.org/pdf/1801.10395.pdf
  9. Learning Physical Collaborative Robot Behaviors from Human Demonstrations - http://www.iri.upc.edu/files/scidoc/1758-Learning-Physical-Collaborative-Robot-Behaviors-from-Human-Demonstrations.pdf
  10. A Generalized Path Integral Control Approach to Reinforcement Learning - http://www.jmlr.org/papers/volume11/theodorou10a/theodorou10a.pdf
  11. Gaussian Processes for Data-Efficient Learning in Robotics and Control - https://www.doc.ic.ac.uk/~mpd37/publications/pami_final_w_appendix.pdf
  12. Bayesian Policy Optimization for Model Uncertainty - https://arxiv.org/pdf/1810.01014.pdf
  13. Learning Attractor Landscapes for Learning Motor Primitives - https://papers.nips.cc/paper/2140-learning-attractor-landscapes-for-learning-motor-primitives.pdf
  14. Safe Reinforcement Learning with Model Uncertainty Estimates - https://arxiv.org/pdf/1810.08700.pdf
  15. Bayesian Optimization with Robust Bayesian Neural Networks - https://papers.nips.cc/paper/6117-bayesian-optimization-with-robust-bayesian-neural-networks.pdf
  16. Uncertainty-Aware Reinforcement Learning for Collision Avoidance - https://arxiv.org/pdf/1702.01182.pdf
  17. Self-supervised Learning of Image Embedding for Continuous Control - https://arxiv.org/pdf/1901.00943.pdf
  18. Socially Compliant Mobile Robot Navigation via Inverse Reinforcement Learning - http://www.cs.cmu.edu/~jeanoh/16-785/papers/kretzschmar-ijrr2016-socially.pdf
  19. Prediction and Control with Temporal Segment Models - https://arxiv.org/pdf/1703.04070.pdf
  20. A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning - https://arxiv.org/pdf/1707.02515.pdf
  21. A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning - https://arxiv.org/pdf/1011.0686.pdf
  22. Online Evolution of Deep Convolutional Network for Vision-Based Reinforcement Learning - http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.636.6995&rep=rep1&type=pdf
  23. End-to-End Deep Reinforcement Learning for Lane Keeping Assist - https://arxiv.org/pdf/1612.04340.pdf
  24. PLATO: Policy Learning using Adaptive Trajectory Optimization - https://arxiv.org/pdf/1603.00622.pdf
  25. Goal-Driven Dynamics Learning via Bayesian Optimization - https://arxiv.org/pdf/1703.09260.pdf
  26. Safely Probabilistically Complete Real-Time Planning and Exploration in Unknown Environments - https://arxiv.org/pdf/1811.07834.pdf
  27. Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning - http://web.eecs.umich.edu/~baveja/Papers/task-generalization.pdf
  28. MPC-Inspired Neural Network Policies for Sequential Decision Making - https://arxiv.org/pdf/1802.05803.pdf
  29. Imitation Learning for Agile Autonomous Driving - https://arxiv.org/pdf/1709.07174.pdf
  30. Safe end-to-end imitation learning for model predictive control - https://arxiv.org/pdf/1803.10231.pdf
  31. Unsupervised Visuomotor Control through Distributional Planning Networks - https://arxiv.org/pdf/1902.05542.pdf
  32. Deep-MPC - https://cs.stanford.edu/people/asaxena/papers/deepmpc_rss2015.pdf
  33. Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search - https://arxiv.org/pdf/1509.06791.pdf
  34. Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning - https://arxiv.org/pdf/1810.08647.pdf

Robotic Vision/ SLAM Papers

  1. Visual-Inertial Direct SLAM - http://webdiis.unizar.es/~jcivera/papers/concha_etal_icra16.pdf
  2. Dense 3-D Mapping with Spatial Correlation via Gaussian Filtering - https://arxiv.org/pdf/1801.07380.pdf
  3. Probabilistic Data Association for Semantic SLAM - https://www.cis.upenn.edu/~kostas/mypub.dir/bowman17icra.pdf
  4. Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments - http://www.roboticsproceedings.org/rss14/p16.pdf
  5. UPSNet: A Unified Panoptic Segmentation Network - https://arxiv.org/pdf/1901.03784.pdf
  6. Learning to Localize Using a LiDAR Intensity Map - http://proceedings.mlr.press/v87/barsan18a/barsan18a.pdf
  7. Visual SLAM and Structure from Motion in Dynamic Environments: A Survey - https://www.cs.ox.ac.uk/files/9926/Visual%20Slam.pdf?fbclid=IwAR1OJyQ85sZraMu0FgQauX-cczRasoEaxoKehI7pYHLDI4NcM-oUBfdYyRI
  8. Probabilistic Temporal Inference on Reconstructed 3D Scenes - http://vision.cse.psu.edu/research/3Dreconstruction/relatedWork/papers/schindler2010.pdf
  9. Loosely-Coupled Semi-Direct Monocular SLAM - https://arxiv.org/pdf/1807.10073.pdf
  10. On-Manifold Preintegration for Real-Time Visual-Inertial Odometry - http://rpg.ifi.uzh.ch/docs/TRO16_forster.pdf
  11. Keyframe-Based Visual-Inertial SLAM Using Nonlinear Optimization - http://www.roboticsproceedings.org/rss09/p37.pdf
  12. VINS-Mono - https://arxiv.org/pdf/1708.03852.pdf
  13. Square Root SAM - https://www.cc.gatech.edu/~dellaert/pub/Dellaert06ijrr.pdf
  14. Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization - https://arxiv.org/pdf/1809.01019.pdf
  15. SegMap: 3D Segment Mapping - https://arxiv.org/pdf/1804.09557v1.pdf
  16. Moving Obstacle Detection in Highly Dynamic Scenes - https://europa.informatik.uni-freiburg.de/files/ess-movingobstacledetection-icra09.pdf - https://www.vision.rwth-aachen.de/media/papers/ess08cvpr.pdf
  17. A Probabilistic Framework for Real-time 3D Segmentation using Spatial, Temporal, and Semantic Cues - http://www.roboticsproceedings.org/rss12/p24.pdf
  18. Estimating Metric Poses of Dynamic Objects Using Monocular Visual-Inertial Fusion - https://arxiv.org/pdf/1808.06753.pdf

Planning Papers

  1. Motion Planning under Uncertainty using Iterative Local Optimization in Belief Space - http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.307.6750&rep=rep1&type=pdf
  2. Efficient Path Planning in Belief Space for Safe Navigation - http://www.ipb.uni-bonn.de/pdfs/schirmer17iros.pdf
  3. Probabilistic navigation in dynamic environment using RRT & GP - https://hal.inria.fr/inria-00332595/document
  4. Probabilistic Feasibility for Nonlinear Systems with Non-Gaussian Uncertainty using RRT - http://acl.mit.edu/papers/Luders11_Infotech.pdf
  5. Fast Robot Motion Planning with Collision Avoidance and Temporal Optimization ~ The Convex Feasible Set Algo
    in Motion Planning - https://arxiv.org/pdf/1709.00627.pdf
  6. Real-Time Planning with Multi-Fidelity Models for Agile Flights in Unknown Environments - https://arxiv.org/pdf/1810.01035.pdf
  7. Monte Carlo Motion Planning for Robot Trajectory Optimization Under Uncertainty - https://arxiv.org/pdf/1504.08053.pdf
  8. Closed-Loop Belief Space Planning for Linear, Gaussian Systems - http://www-personal.acfr.usyd.edu.au/spns/cdm/papers/Belief%20Space.pdf
  9. Belief Space Planning Simplified: Trajectory-Optimized LQG (T-LQG) - https://arxiv.org/pdf/1608.03013.pdf
  10. Rapidly-exploring Random Belief Trees for Motion Planning Under Uncertainty - https://groups.csail.mit.edu/rrg/papers/abry_icra11.pdf
  11. Sampling-Based Methods for Motion Planning with Constraints - http://www.kavrakilab.org/publications/kingston2018sampling-based-methods-for-motion-planning.pdf
  12. Hybrid Parallel Algorithm for Online Planning under Uncertainty - http://www.roboticsproceedings.org/rss14/p04.pdf
  13. Simultaneous Trajectory Estimation and Planning via Probabilistic Inference - http://www.roboticsproceedings.org/rss13/p25.pdf
  14. An Online Learning Approach to Model Predictive Control - https://arxiv.org/pdf/1902.08967.pdf
  15. STEAP: simultaneous trajectory estimation and planning - https://link.springer.com/content/pdf/10.1007%2Fs10514-018-9770-1.pdf
  16. Continuous-time Gaussian process motion planning via probabilistic inference - https://arxiv.org/pdf/1707.07383.pdf
  17. Stochastic Extended LQR for Optimization-based Motion Planning Under Uncertainty - https://robotics.cs.unc.edu/publications/Sun2016_TASE.pdf
  18. Motion Planning under Uncertainty using Iterative Local Optimization in Belief Space - https://robotics.cs.unc.edu/publications/vandenBerg2012_IJRR.pdf
  19. Modular Task and Motion Planning in Belief Space - https://people.eecs.berkeley.edu/~pabbeel/papers/2015-IROS-TMP-belief-space.pdf
  20. Probabilistically Safe Corridors to Guide Sampling-Based Motion Planning - https://arxiv.org/pdf/1901.00101.pdf
  21. Importance sampling-based approximate optimal planning and control - https://arxiv.org/pdf/1612.05594.pdf
  22. Probabilistically Safe Motion Planning to Avoid Dynamic Obstacles with Uncertain Motion Patterns - http://acl.mit.edu/papers/Aoude13_AURO.pdf

Random Read

  1. FASTrack: Real-Time Planning - http://sylviaherbert.com/fastrack
  2. Efficient Multi-Task Deep RL - https://www.youtube.com/watch?v=TfhV51cndPY
  3. Learning for Dynamics and Control - https://l4dc.mit.edu/
  4. https://arxiv.org/pdf/1705.08781.pdf
  5. Safe RL Tutorial - https://las.inf.ethz.ch/files/ewrl18_SafeRL_tutorial.pdf
  6. Manifold Planning and Estimation - https://dinhhuy2109.github.io/files/pdf/QEreport.pdf
  7. https://www.ics.forth.gr/cvrl/publications/journals/fokaIJSR10.pdf

Non - Classified

Robotics, Vision and Control: Seville - https://grvc.us.es/
https://citris-uc.org/initiatives/robotics-2/