[go: up one dir, main page]

Sleiman et al., 2024 - Google Patents

Guided reinforcement learning for robust multi-contact loco-manipulation

Sleiman et al., 2024

View PDF
Document ID
1629906603140603384
Author
Sleiman J
Mittal M
Hutter M
Publication year
Publication venue
8th Annual Conference on Robot Learning

External Links

Snippet

Reinforcement learning (RL) has shown remarkable proficiency in developing robust control policies for contact-rich applications. However, it typically requires meticulous Markov Decision Process (MDP) designing tailored to each task and robotic platform. This work …
Continue reading at openreview.net (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
    • 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
    • G05B13/042Adaptive 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 in which a parameter or coefficient is automatically adjusted to optimise the performance
    • 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
    • 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
    • G06N3/08Learning methods
    • 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
    • G06N3/04Architectures, e.g. interconnection topology
    • 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
    • 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
    • G06N5/00Computer systems utilising knowledge based models
    • 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

Similar Documents

Publication Publication Date Title
Ibarz et al. How to train your robot with deep reinforcement learning: lessons we have learned
Muratore et al. Assessing transferability from simulation to reality for reinforcement learning
Dalal et al. Imitating task and motion planning with visuomotor transformers
Whitman et al. Learning modular robot control policies
Fu et al. One-shot learning of manipulation skills with online dynamics adaptation and neural network priors
Franceschetti et al. Robotic arm control and task training through deep reinforcement learning
Fan et al. A learning framework for high precision industrial assembly
Pastor et al. From dynamic movement primitives to associative skill memories
Sleiman et al. Guided reinforcement learning for robust multi-contact loco-manipulation
Shaik et al. Adaptive Control Through Reinforcement Learning: Robotic Systems in Action
Stulp et al. Model-free reinforcement learning of impedance control in stochastic environments
Peters et al. Robot learning
Stan et al. Reinforcement learning for assembly robots: A review
CN117601120A (en) Adaptive variable impedance control method and device, electronic equipment and storage medium
Ma et al. Reinforcement learning with model-based feedforward inputs for robotic table tennis
Toussaint et al. Dual execution of optimized contact interaction trajectories
Zhang et al. Plan-guided reinforcement learning for whole-body manipulation
Eßer et al. Action Space Design in Reinforcement Learning for Robot Motor Skills
Hess et al. Sampling-Based Model Predictive Control for Dexterous Manipulation on a Biomimetic Tendon-Driven Hand
DeWolf A neural model of the motor control system
Emami et al. Survey of Multi-Agent Reinforcement Learning to Solve Inverse Kinematic Problems of Redundant Robotic Manipulators
Lundell Dynamic movement primitives and reinforcement learning for adapting a learned skill
Scherzinger Human-inspired compliant controllers for robotic assembly
Fan Learning industrial assembly by guided-DDPG
Chatzilygeroudis Micro-data reinforcement learning for adaptive robots