Can I Pour Into It? Robot Imagining Open Containability Affordance of Previously Unseen Objects via Physical Simulations
暂无分享,去创建一个
[1] Nikolaos G. Tsagarakis,et al. Object-based affordances detection with Convolutional Neural Networks and dense Conditional Random Fields , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[2] Sinisa Todorovic,et al. A Multi-scale CNN for Affordance Segmentation in RGB Images , 2016, ECCV.
[3] Dieter Fox,et al. Manipulator and object tracking for in-hand 3D object modeling , 2011, Int. J. Robotics Res..
[4] Eduardo Ruiz,et al. Geometric Affordance Perception: Leveraging Deep 3D Saliency With the Interaction Tensor , 2020, Frontiers in Neurorobotics.
[5] Jessica B. Hamrick,et al. Simulation as an engine of physical scene understanding , 2013, Proceedings of the National Academy of Sciences.
[6] Ernest Davis,et al. Reasoning from Radically Incomplete Information: The Case of Containers , 2013 .
[7] Eric Lengyel. Volumetric Hierarchical Approximate Convex Decomposition , 2016 .
[8] Dinesh Manocha,et al. Motion planning for fluid manipulation using simplified dynamics , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[9] Markus Vincze,et al. AfNet: The Affordance Network , 2012, Asian Conference on Computer Vision.
[10] Yixin Zhu. Visual Commonsense Reasoning: Functionality, Physics, Causality, and Utility , 2018 .
[11] Tamim Asfour,et al. Autonomous acquisition of visual multi-view object representations for object recognition on a humanoid robot , 2010, 2010 IEEE International Conference on Robotics and Automation.
[12] Patricio A. Vela,et al. Learning Affordance Segmentation for Real-World Robotic Manipulation via Synthetic Images , 2019, IEEE Robotics and Automation Letters.
[13] Matthias Nießner,et al. 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Kuan-Ting Yu,et al. Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[15] Marc Levoy,et al. A volumetric method for building complex models from range images , 1996, SIGGRAPH.
[16] Darwin G. Caldwell,et al. AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[17] Christopher G. Atkeson,et al. Differential dynamic programming with temporally decomposed dynamics , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).
[18] Wei Gao,et al. kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation , 2019, ISRR.
[19] L. Uhr,et al. Representing and using functional definitions for visual recognition , 1987 .
[20] Sai Kit Yeung,et al. Fill and Transfer: A Simple Physics-Based Approach for Containability Reasoning , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[21] Juergen Gall,et al. Weakly Supervised Affordance Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Yiannis Aloimonos,et al. Affordance detection of tool parts from geometric features , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[23] Kate Saenko,et al. High precision grasp pose detection in dense clutter , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[24] Edwin Olson,et al. Predicting object functionality using physical simulations , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[25] Wei Liang,et al. Tracking Occluded Objects and Recovering Incomplete Trajectories by Reasoning About Containment Relations and Human Actions , 2018, AAAI.
[26] Li Fei-Fei,et al. Reasoning about Object Affordances in a Knowledge Base Representation , 2014, ECCV.
[27] Shimon Ullman,et al. A model for discovering ‘containment’ relations , 2019, Cognition.
[28] J. Gibson. The Ecological Approach to Visual Perception , 1979 .
[29] Vijay Kumar,et al. Autonomous Precision Pouring From Unknown Containers , 2019, IEEE Robotics and Automation Letters.
[30] Wei Liang,et al. Evaluating Human Cognition of Containing Relations with Physical Simulation , 2015, CogSci.
[31] James J. DiCarlo,et al. How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.
[32] Connor Schenck,et al. Visual closed-loop control for pouring liquids , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[33] John J. Leonard,et al. Toward lifelong object segmentation from change detection in dense RGB-D maps , 2013, 2013 European Conference on Mobile Robots.
[34] Stefan Boschert,et al. Digital Twin—The Simulation Aspect , 2016 .
[35] Michael Beetz,et al. Envisioning the qualitative effects of robot manipulation actions using simulation-based projections , 2017, Artif. Intell..
[36] Hongtao Wu,et al. Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Body , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).
[37] Frank Guerin,et al. Learning how a tool affords by simulating 3D models from the web , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[38] Michael U. Gutmann,et al. Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation , 2017, CoRL.