[go: up one dir, main page]

Mamedov et al., 2024 - Google Patents

Pseudo-rigid body networks: learning interpretable deformable object dynamics from partial observations

Mamedov et al., 2024

View PDF
Document ID
6574352381972501522
Author
Mamedov S
Geist A
Swevers J
Trimpe S
Publication year
Publication venue
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

External Links

Snippet

Accurately predicting deformable linear object (DLO) dynamics is challenging, especially when the task requires a model that is both human-interpretable and computationally efficient. In this work, we draw inspiration from the pseudo-rigid body method (PRB) and …
Continue reading at arxiv.org (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
    • G05B13/04Adaptive 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
    • 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
    • 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
    • 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
    • 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
    • G05B2219/50Machine tool, machine tool null till machine tool work handling
    • G05B2219/50109Soft approach, engage, retract, escape, withdraw path for tool to workpiece
    • 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
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B25HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25JMANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00Programme-controlled manipulators
    • B25J9/16Programme controls
    • B25J9/1602Programme controls characterised by the control system, structure, architecture
    • B25J9/1607Calculation of inertia, jacobian matrixes and inverses
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B25HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25JMANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00Programme-controlled manipulators
    • B25J9/16Programme controls
    • B25J9/1674Programme controls characterised by safety, monitoring, diagnostic

Similar Documents

Publication Publication Date Title
Dasari et al. Robonet: Large-scale multi-robot learning
CN111832702B (en) Deep machine learning method and device for robotic grasping
Giorelli et al. Neural network and jacobian method for solving the inverse statics of a cable-driven soft arm with nonconstant curvature
Ding et al. Sim-to-real transfer for optical tactile sensing
CN113341706A (en) Man-machine cooperation assembly line system based on deep reinforcement learning
Scheiderer et al. Bézier curve based continuous and smooth motion planning for self-learning industrial robots
Omrčen et al. Autonomous acquisition of pushing actions to support object grasping with a humanoid robot
Shaj et al. Action-conditional recurrent kalman networks for forward and inverse dynamics learning
Jia et al. Mail: Improving imitation learning with mamba
Sidiropoulos et al. A human inspired handover policy using gaussian mixture models and haptic cues
Zhou et al. T-td3: A reinforcement learning framework for stable grasping of deformable objects using tactile prior
Čakurda et al. Deep learning methods in soft robotics: Architectures and applications
Jang et al. Bridging the simulation-to-real gap of depth images for deep reinforcement learning
CN117464689A (en) A robot brainwave adaptive grabbing method and system based on autonomous exploration algorithm
Mamedov et al. Pseudo-rigid body networks: learning interpretable deformable object dynamics from partial observations
Xia et al. Deep reinforcement learning based proactive dynamic obstacle avoidance for safe human-robot collaboration
Kratzer et al. Towards combining motion optimization and data driven dynamical models for human motion prediction
Shan et al. Fine robotic manipulation without force/torque sensor
Bonsignorio et al. An imitation learning approach for the control of a low-cost low-accuracy robotic arm for unstructured environments
Cao et al. Shape control of elastic deformable linear objects for robotic cable assembly
Aslan et al. End-to-end learning from demonstation for object manipulation of robotis-Op3 humanoid robot
Tenhumberg et al. Efficient learning of fast inverse kinematics with collision avoidance
Sepahvand et al. Image-to-joint inverse kinematic of a supportive continuum arm using deep learning
US20240412063A1 (en) Demonstration-driven reinforcement learning
Al-Sharif et al. Enhancing Robotic Autonomy: A Review and Case Study of Traditional and Deep Learning Approaches to Inverse Kinematics