[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Murali et al., 2018 - Google Patents

Cassl: Curriculum accelerated self-supervised learning

Murali et al., 2018

View PDF
Document ID
204241051744833560
Author
Murali A
Pinto L
Gandhi D
Gupta A
Publication year
Publication venue
2018 IEEE International Conference on Robotics and Automation (ICRA)

External Links

Snippet

Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces. However, scaling this framework for higher-dimensional control requires either scaling up the data collection …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only

Similar Documents

Publication Publication Date Title
Murali et al. Cassl: Curriculum accelerated self-supervised learning
Ibarz et al. How to train your robot with deep reinforcement learning: lessons we have learned
Pertsch et al. Accelerating reinforcement learning with learned skill priors
Eitel et al. Learning to singulate objects using a push proposal network
Le et al. A deep hierarchical reinforcement learning algorithm in partially observable Markov decision processes
Xie et al. Few-shot goal inference for visuomotor learning and planning
Agrawal et al. Learning to poke by poking: Experiential learning of intuitive physics
Smith et al. Avid: Learning multi-stage tasks via pixel-level translation of human videos
Rahmatizadeh et al. Vision-based multi-task manipulation for inexpensive robots using end-to-end learning from demonstration
Zhu et al. Bottom-up skill discovery from unsegmented demonstrations for long-horizon robot manipulation
Krishnan et al. Ddco: Discovery of deep continuous options for robot learning from demonstrations
Finn et al. Deep visual foresight for planning robot motion
Levine et al. End-to-end training of deep visuomotor policies
Sigaud et al. On-line regression algorithms for learning mechanical models of robots: a survey
Montesano et al. Learning object affordances: from sensory--motor coordination to imitation
Bekiroglu et al. A probabilistic framework for task-oriented grasp stability assessment
Okada et al. Planet of the bayesians: Reconsidering and improving deep planning network by incorporating bayesian inference
Ugur et al. Emergent structuring of interdependent affordance learning tasks using intrinsic motivation and empirical feature selection
Liu et al. Task-constrained motion planning considering uncertainty-informed human motion prediction for human–robot collaborative disassembly
Palleschi et al. Grasp It Like a Pro 2.0: A Data-Driven Approach Exploiting Basic Shape Decomposition and Human Data for Grasping Unknown Objects
Gao et al. Meta-learning regrasping strategies for physical-agnostic objects
Seo et al. Continuous control with coarse-to-fine reinforcement learning
Patel et al. Learning object, grasping and manipulation activities using hierarchical HMMs
Ma et al. A learning from demonstration framework for adaptive task and motion planning in varying package-to-order scenarios
Li et al. Accelerating grasp exploration by leveraging learned priors