[go: up one dir, main page]

Zhu et al., 2019 - Google Patents

Sim-real joint reinforcement transfer for 3d indoor navigation

Zhu et al., 2019

View PDF
Document ID
11034459492597431455
Author
Zhu F
Zhu L
Yang Y
Publication year
Publication venue
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

External Links

Snippet

There has been an increasing interest in 3D indoor navigation, where a robot in an environment moves to a target according to an instruction. To deploy a robot for navigation in the physical world, lots of training data is required to learn an effective policy. It is quite …
Continue reading at openaccess.thecvf.com (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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements

Similar Documents

Publication Publication Date Title
Zhu et al. Sim-real joint reinforcement transfer for 3d indoor navigation
Ramakrishnan et al. An exploration of embodied visual exploration
Seo et al. Reinforcement learning with action-free pre-training from videos
Morales et al. A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning
Katyal et al. Uncertainty-aware occupancy map prediction using generative networks for robot navigation
Lyu et al. Improving target-driven visual navigation with attention on 3d spatial relationships
Tai et al. A survey of deep network solutions for learning control in robotics: From reinforcement to imitation
Chen et al. Driving maneuvers prediction based autonomous driving control by deep Monte Carlo tree search
Naveed et al. Deep introspective SLAM: Deep reinforcement learning based approach to avoid tracking failure in visual SLAM
Yokoyama et al. Success weighted by completion time: A dynamics-aware evaluation criteria for embodied navigation
Stein et al. Genesis-rt: Generating synthetic images for training secondary real-world tasks
Saroya et al. Online exploration of tunnel networks leveraging topological CNN-based world predictions
Sang et al. A novel neural multi-store memory network for autonomous visual navigation in unknown environment
Wang et al. Towards cooperation in sequential prisoner's dilemmas: a deep multiagent reinforcement learning approach
Wang et al. Achieving cooperation through deep multiagent reinforcement learning in sequential prisoner's dilemmas
Schmid et al. Explore, approach, and terminate: Evaluating subtasks in active visual object search based on deep reinforcement learning
Wu et al. Learning and planning with a semantic model
Chen et al. Think holistically, act down-to-earth: A semantic navigation strategy with continuous environmental representation and multi-step forward planning
Ma et al. Using RGB image as visual input for mapless robot navigation
Ramakrishnan et al. Environment predictive coding for embodied agents
CN118706120A (en) A robot-compatible navigation method and system for social interaction behavior
Choi et al. Efficient policy adaptation with contrastive prompt ensemble for embodied agents
Bougie et al. Towards interpretable reinforcement learning with state abstraction driven by external knowledge
Badawy et al. New approach to enhancing the performance of cloud-based vision system of mobile robots
Ji et al. Communication Emitter Motion Behavior’s Cognition Based on Deep Reinforcement Learning