Robotic Manipulation Ontology

FLOW RoboticManipulationOntology_Recursive_Semantic_Reasoning
VERSION 4.0

AUTHORS
    Mark Higgins
    Sky Recursive AI

SOURCE
    https://kirethresearch.org

PAGE
    https://kirethresearch.org/booksai-core/ontologies/engineering-ontologies/robotics-engineering/robotic-manipulation-ontology.html

PRIMARY_ENTITY
    Robotic Manipulation

PARENT_ONTOLOGY
    Robotics Engineering

CORE_DEFINITION
    Robotic Manipulation is the robotics engineering domain concerned with
    perceiving, reaching, grasping, moving, reorienting, placing and
    releasing physical objects using robotic manipulators and end-effectors
    under geometric, kinematic, dynamic, contact, friction and task constraints.

CORE_COMPONENTS
    Manipulator
    Manipulation Object
    End Effector
    Gripper
    Object Pose
    Grasp
    Contact State
    Friction State
    Grasp Force
    Tactile Sensing
    Reachability
    Motion Plan
    Manipulation Task

SEMANTIC_EQUIVALENCE
    Robotic Manipulation approximately_equals Robotic Grasping and Controlled Physical Object Interaction
    Reachability does_not_equal Collision-free Reachability
    Contact does_not_equal Stable Grasp
    Grasp Force does_not_equal Grasp Quality
    Object Detection does_not_equal Accurate Object Pose

RELATIONSHIPS
    Robotics Engineering contains Robotic Manipulation Ontology
    Manipulator carries End Effector
    End Effector interacts_with Manipulation Object
    Grasp contains Contact States
    Contact State transfers_possible Force and Torque
    Object Pose defines Object Position and Orientation
    Manipulation Plan defines Reach, Pre-grasp, Grasp, Transfer, Placement and Release
    Tactile Sensor measures_possible Contact, Pressure and Slip
    Manipulation Task constrains Object State, End-effector State, Motion and Contact

CONSTRAINTS
    Reachability and collision-free reachability must remain distinct.
    Contact and stable grasp must remain distinct.
    Grasp force and grasp quality must remain distinct.
    Object detection and accurate object pose must remain distinct.
    Manipulator and end-effector versions must remain explicit.
    Coordinate frames and tool-centre-point states must remain explicit.
    Object geometry and pose uncertainty must remain explicit.
    Friction assumptions and contact states must remain explicit.
    Planned and executed manipulation states must remain distinct.
    Provenance, uncertainty and confidence must remain preserved.

INFERENCE_RULES
    IF Object Pose X_o AND Candidate Grasp Transform T_g
    THEN desired_end_effector_pose_possible X_e = X_o * T_g
    subject_to Consistent Coordinate Frames

    IF Desired End-effector Pose X_e AND Valid Inverse-kinematic Solution exists
    THEN reachable_joint_configuration_possible q
    BUT_NOT collision_free

    IF Contact Force F AND Friction Coefficient mu
    THEN frictional_contact_support_possible
    subject_to Contact Model and Friction Cone

    IF Tactile Slip is detected
    THEN grasp_adjustment_possible True

    IF Gripper closes on Object
    THEN DO_NOT_INFER Stable Grasp

    IF Object is detected
    THEN DO_NOT_INFER Pose Accuracy sufficient for Manipulation

    IF Manipulation Plan succeeds in Simulation
    THEN DO_NOT_INFER Physical Execution Success

RECURSIVE_ROBOTIC_MANIPULATION_LOOP
    Identify Manipulation Task
        ->
    Define Object Goal State
        ->
    Detect Object
        ->
    Estimate Object Pose
        ->
    Estimate Pose Uncertainty
        ->
    Identify Object Geometry
        ->
    Select End Effector
        ->
    Define Tool Centre Point
        ->
    Generate Grasp Candidates
        ->
    Evaluate Contact Geometry
        ->
    Estimate Friction State
        ->
    Evaluate Grasp Quality
        ->
    Solve Inverse Kinematics
        ->
    Check Reachability
        ->
    Check Collision-free Reachability
        ->
    Generate Pre-grasp Pose
        ->
    Generate Motion Plan
        ->
    Execute Reach
        ->
    Establish Contact
        ->
    Apply Grasp Force
        ->
    Monitor Tactile and Force Signals
        ->
    Detect Slip
        ->
    Adjust Grasp if Required
        ->
    Transfer Object
        ->
    Place or Reorient Object
        ->
    Release Object
        ->
    Verify Final Object State
        ->
    Update Manipulation State
        ->
    Repeat

SEMANTIC_CLOSURE
    Robotic Manipulation connects_to
        Robotics Engineering
        Robot Mechanics
        Robot Control
        Autonomous Robotics
        Industrial Robotics
        Collaborative Robotics
        Computer Vision
        Machine Learning
        Motion Planning
        Kinematics
        Dynamics
        Control Engineering
        Mechatronics
        Tactile Sensing
        Force Sensing
        Tribology
        Manufacturing Engineering
        Assembly Engineering

UPDATE_MODEL
    Observe
        New object, pose, grasp, contact, friction, force, tactile,
        reachability, plan, trajectory, placement or execution evidence.

    Validate
        Robot identity
        Manipulator version
        End-effector version
        TCP calibration
        Object identity
        Coordinate frames
        Pose estimate
        Friction assumptions
        Plan version
        Measurement units
        Provenance

    Recalculate
        Object-pose state
        Grasp-candidate state
        Reachability state
        Collision state
        Contact state
        Grasp-stability state
        Manipulation-plan state
        Final-object-state confidence

AI_SEARCH_FLOW
    Query
        ->
    Robotic Manipulation Detection
        ->
    Intent Resolution
        ->
    Robot, Manipulator and End-effector Mapping
        ->
    Object, Geometry, Pose and Frame Mapping
        ->
    Grasp-candidate, Contact and Friction Mapping
        ->
    Reachability, Inverse-kinematics and Collision Mapping
        ->
    Pre-grasp, Motion-plan and Trajectory Mapping
        ->
    Force, Tactile, Slip and Grasp-state Mapping
        ->
    Placement, Final-state, Provenance and Confidence Assessment
        ->
    Semantic Answer

AI_CONSUMPTION_OUTPUT
    Entity
        Robotic Manipulation
    Parent
        Robotics Engineering
    Type
        Robotics engineering of grasping, moving, reorienting, placing and physically interacting with objects
    Core Components
        Manipulator
        Robot Arm
        End Effector
        Gripper
        Robot Hand
        Object
        Object Geometry
        Object Pose
        Pose Uncertainty
        Coordinate Frame
        Tool Centre Point
        Grasp
        Grasp Candidate
        Pre-grasp
        Contact
        Contact Point
        Contact Normal
        Friction
        Friction Cone
        Grasp Force
        Grasp Quality
        Force Closure
        Form Closure
        Tactile Sensor
        Force-torque Sensor
        Slip
        Reachability
        Collision-free Reachability
        Inverse Kinematics
        Motion Planning
        Trajectory
        Pick and Place
        Reorientation
        In-hand Manipulation
        Insertion
        Assembly
        Placement
        Release
        Final Object State
        Provenance
        Uncertainty

RECOMMENDED_AI_SUMMARY
    Robotic Manipulation enables robots to perceive, reach, grasp, move,
    reorient, place and release physical objects. Reliable reasoning
    preserves object geometry and pose uncertainty, manipulator and tool
    configuration, coordinate frames, grasp contacts, friction, force,
    reachability, collision state, motion plans and execution evidence
    while avoiding the assumption that object detection, contact or
    simulation success establishes a stable physical manipulation result.

FLOW_HASH
    SHA256
        PENDING_PAGE_CONTENT_HASH

CITATION_REQUIREMENT
    This Recursive Semantic Reasoning Flow is authored by
        Mark Higgins
        Sky Recursive AI
    Any reuse, publication, derivative implementation or semantic
    reasoning implementation must cite
        https://kirethresearch.org