[go: up one dir, main page]

Shi et al., 2023 - Google Patents

A constrained framework based on IBLF for robot learning with human supervision

Shi et al., 2023

View PDF
Document ID
14395627008958539660
Author
Shi D
Li Q
Yang C
Lu Z
Publication year
Publication venue
Robotica

External Links

Snippet

Dynamical movement primitives (DMPs) method is a useful tool for efficient robotic skills learning from human demonstrations. However, the DMPs method should know the specified constraints of tasks in advance. One flexible solution is to introduce the human …
Continue reading at uwe-repository.worktribe.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive 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
    • G05B13/027Adaptive 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
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems
    • G05B2219/39Robotics, robotics to robotics hand
    • G05B2219/39376Hierarchical, learning, recognition and skill level and adaptation servo level
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems
    • G05B2219/40Robotics, robotics mapping to robotics vision
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00Programme-control systems
    • G05B19/02Programme-control systems electric
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/004Artificial life, i.e. computers simulating life
    • G06N3/008Artificial life, i.e. computers simulating life based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. robots replicating pets or humans in their appearance or behavior
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00Systems involving the use of models or simulators of said systems
    • G05B17/02Systems involving the use of models or simulators of said systems electric
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B25HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25JMANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00Programme-controlled manipulators
    • B25J9/16Programme controls
    • B25J9/1628Programme controls characterised by the control loop
    • B25J9/1633Programme controls characterised by the control loop compliant, force, torque control, e.g. combined with position control

Similar Documents

Publication Publication Date Title
Osa et al. Online trajectory planning and force control for automation of surgical tasks
Stouraitis et al. Online hybrid motion planning for dyadic collaborative manipulation via bilevel optimization
Cui et al. A task-adaptive deep reinforcement learning framework for dual-arm robot manipulation
Khadivar et al. Adaptive fingers coordination for robust grasp and in-hand manipulation under disturbances and unknown dynamics
Franzese et al. Interactive imitation learning of bimanual movement primitives
Kicki et al. Fast kinodynamic planning on the constraint manifold with deep neural networks
Dimeas et al. Towards progressive automation of repetitive tasks through physical human-robot interaction
Dong et al. A novel human-robot skill transfer method for contact-rich manipulation task
Sun et al. Integrating reinforcement learning and learning from demonstrations to learn nonprehensile manipulation
Stulp et al. Learning motion primitive goals for robust manipulation
Sharma et al. Dmp based trajectory tracking for a nonholonomic mobile robot with automatic goal adaptation and obstacle avoidance
Huang et al. A linearly constrained nonparametric framework for imitation learning
Jankowski et al. From key positions to optimal basis functions for probabilistic adaptive control
Jha et al. Robot programming by demonstration using teleoperation through imitation
Mielke et al. Human-robot co-manipulation of extended objects: Data-driven models and control from analysis of human-human dyads
Davoodi et al. Rule-based safe probabilistic movement primitive control via control barrier functions
Toussaint et al. Dual execution of optimized contact interaction trajectories
JP2024508053A (en) Transfer between tasks in different domains
Nordmann et al. Teaching nullspace constraints in physical human-robot interaction using reservoir computing
Cai et al. Inferring the geometric nullspace of robot skills from human demonstrations
Shi et al. A constrained framework based on IBLF for robot learning with human supervision
Shi et al. A Learning System for Deformable Object Cooperative Manipulation
Oikonomou et al. Reproduction of human demonstrations with a soft-robotic arm based on a library of learned probabilistic movement primitives
Boas et al. A dmps-based approach for human-robot collaboration task quality management
Koropouli et al. Generalization of Force Control Policies from Demonstrations for Constrained Robotic Motion Tasks: A Regression-Based Approach