robotic python API

rai bindings

class robotic._robotic.ArgWord

[todo: replace by str]

Members:

_left

_right

_sequence

_path

property name

class robotic._robotic.BotOp

Robot Operation interface – see https://marctoussaint.github.io/robotics-course/tutorials/1b-botop.html

getCameraFxycxy(self: robotic._robotic.BotOp, sensorName: str) → arr: returns camera intrinsics

getGripperPos(self: robotic._robotic.BotOp, leftRight: robotic._robotic.ArgWord) → float: returns the gripper pos

getImageAndDepth(self: robotic._robotic.BotOp, sensorName: str) → tuple: returns image and depth from a camera sensor

getImageDepthPcl(self: robotic._robotic.BotOp, sensorName: str, globalCoordinates: bool = False) → tuple: returns image, depth and point cloud (assuming sensor knows intrinsics) from a camera sensor, optionally in global instead of camera-frame-relative coordinates

getKeyPressed(self: robotic._robotic.BotOp) → int: get key pressed in window at last sync

getTimeToEnd(self: robotic._robotic.BotOp) → float: get time-to-go of the current spline reference that is tracked (use getTimeToEnd()<=0. to check if motion execution is done)

get_q(self: robotic._robotic.BotOp) → arr: get the current (real) robot joint vector

get_qDot(self: robotic._robotic.BotOp) → arr: get the current (real) robot joint velocities

get_qHome(self: robotic._robotic.BotOp) → arr: returns home joint vector (defined as the configuration C when you created BotOp)

get_t(self: robotic._robotic.BotOp) → float: returns the control time (absolute time managed by the high freq tracking controller)

get_tauExternal(self: robotic._robotic.BotOp) → arr: get the current (real) robot joint torques (external: gravity & acceleration removed) – each call averages from last call; first call might return nonsense!

gripperClose(self: robotic._robotic.BotOp, leftRight: robotic._robotic.ArgWord, force: float = 10.0, width: float = 0.05, speed: float = 0.1) → None: close gripper

gripperCloseGrasp(self: robotic._robotic.BotOp, leftRight: robotic._robotic.ArgWord, objName: str, force: float = 10.0, width: float = 0.05, speed: float = 0.1) → None: close gripper and indicate what should be grasped – makes no difference in real, but helps simulation to mimic grasping more reliably

gripperDone(self: robotic._robotic.BotOp, leftRight: robotic._robotic.ArgWord) → bool: returns if gripper is done

gripperMove(self: robotic._robotic.BotOp, leftRight: robotic._robotic.ArgWord, width: float = 0.075, speed: float = 0.2) → None: move the gripper to width (default: open)

hold(self: robotic._robotic.BotOp, floating: bool = False, damping: bool = True) → None: hold the robot with a trivial PD controller, floating means reference = real, without damping the robot is free floating

home(self: robotic._robotic.BotOp, C: robotic._robotic.Config) → None: immediately drive the robot home (see get_qHome); keeps argument C synced; same as moveTo(qHome, 1., True); wait(C);

move(self: robotic._robotic.BotOp, path: arr, times: arr, overwrite: bool = False, overwriteCtrlTime: float = -1.0) → None

core motion command: set a spline motion reference; if only a single time [T] is given for multiple waypoints, it assumes equal time spacing with TOTAL time T

By default, the given spline is APPENDED to the current reference spline. The user can also enforce the given spline to overwrite the current reference starting at the given absolute ctrlTime. This allows implementation of reactive (e.g. MPC-style) control. However, the user needs to take care that overwriting is done in a smooth way, i.e., that the given spline starts with a pos/vel that is close to the pos/vel of the current reference at the given ctrlTime.

moveAutoTimed(self: robotic._robotic.BotOp, path: arr, maxVel: float = 1.0, maxAcc: float = 1.0) → None: helper to execute a path (typically fine resolution, from KOMO or RRT) with equal time spacing chosen for given max vel/acc

moveTo(self: robotic._robotic.BotOp, q_target: arr, timeCost: float = 1.0, overwrite: bool = False) → None

helper to move to a single joint vector target, where timing is chosen optimally based on the given timing cost

When using overwrite, this immediately steers to the target – use this as a well-timed reactive q_target controller

setCompliance(self: robotic._robotic.BotOp, J: arr, compliance: float = 0.5) → None: set a task space compliant, where J defines the task space Jacobian, and compliance goes from 0 (no compliance) to 1 (full compliance, but still some damping)

setControllerWriteData(self: robotic._robotic.BotOp, arg0: int) → None: [for internal debugging only] triggers writing control data into a file

stop(self: robotic._robotic.BotOp, C: robotic._robotic.Config) → None: immediately stop the robot; keeps argument C synced; same as moveTo(get_q(), 1., True); wait(C);

sync(self: robotic._robotic.BotOp, C: robotic._robotic.Config, waitTime: float = 0.1, viewMsg: rai::String = '') → int: sync your workspace configuration C with the robot state

wait(self: robotic._robotic.BotOp, C: robotic._robotic.Config, forKeyPressed: bool = True, forTimeToEnd: bool = True, forGripper: bool = False) → int: repeatedly sync your workspace C until a key is pressed or motion ends (optionally)

class robotic._robotic.CameraView

Offscreen rendering

computeImageAndDepth(self: robotic._robotic.CameraView, config: robotic._robotic.Config, visualsOnly: bool = True) → tuple: returns image and depth from a camera sensor; the ‘config’ argument needs to be the same configuration as in the constructor, but in new state

computeSegmentationID(self: robotic._robotic.CameraView) → Array<T>: return a uint16 array with object ID segmentation

computeSegmentationImage(self: robotic._robotic.CameraView) → Array<T>: return an rgb image encoding the object ID segmentation

getFxycxy(self: robotic._robotic.CameraView) → arr: return the camera intrinsics f_x, f_y, c_x, c_y

setCamera(self: robotic._robotic.CameraView, cameraFrameName: str) → rai::CameraView::Sensor: select a camera, typically a frame that has camera info attributes

class robotic._robotic.Config

Core data structure to represent a kinematic configuration (essentially a tree of frames). See https://marctoussaint.github.io/robotics-course/tutorials/1a-configurations.html

addConfigurationCopy(self: robotic._robotic.Config, config: robotic._robotic.Config, prefix: rai::String = '', tau: float = 1.0) → robotic._robotic.Frame

addFile(self: robotic._robotic.Config, filename: str, namePrefix: str = '') → robotic._robotic.Frame: add the contents of the file to C

addFrame(self: robotic._robotic.Config, name: str, parent: str = '', args: str = '') → robotic._robotic.Frame: add a new frame to C; optionally make this a child to the given parent; use the Frame methods to set properties of the new frame

animate(self: robotic._robotic.Config) → None: displays while articulating all dofs in a row

animateSpline(self: robotic._robotic.Config, T: int = 3) → None: animate with random spline in limits bounding box [T=#spline points]

attach(self: robotic._robotic.Config, arg0: str, arg1: str) → None: change the configuration by creating a rigid joint from frame1 to frame2, adopting their current relative pose. This also breaks the first joint that is parental to frame2 and reverses the topological order from frame2 to the broken joint

clear(self: robotic._robotic.Config) → None: clear all frames and additional data; becomes the empty configuration, with no frames

computeCollisions(self: robotic._robotic.Config) → None: [should be obsolete; getCollision* methods auto ensure proxies] call the broadphase collision engine (SWIFT++ or FCL) to generate the list of collisions (or near proximities) between all frame shapes that have the collision tag set non-zero

delFrame(self: robotic._robotic.Config, frameName: str) → None: destroy and remove a frame from C

eval(self: robotic._robotic.Config, featureSymbol: robotic._robotic.FS, frames: StringA = [], scale: arr = array(0.0078125), target: arr = array(0.0078125), order: int = -1) → tuple: evaluate a feature – see https://marctoussaint.github.io/robotics-course/tutorials/features.html

frame(self: robotic._robotic.Config, frameName: str, warnIfNotExist: bool = True) → robotic._robotic.Frame: get access to a frame by name; use the Frame methods to set/get frame properties

frames(self: robotic._robotic.Config) → list[robotic._robotic.Frame]

getCollidablePairs(self: robotic._robotic.Config) → StringA: returns the list of collisable pairs – this should help debugging the ‘contact’ flag settings in a configuration

getCollisionFree(self: robotic._robotic.Config) → bool: returns if the configuration is collision free (binary collision check, using FCL only; collidable objects need to have contact flag)

getCollisions(self: robotic._robotic.Config, belowMargin: float = 1.0) → list: return the results of collision computations: a list of 3 tuples with (frame1, frame2, distance). Optionally report only on distances below a margin To get really precise distances and penetrations use the FS.distance feature with the two frame names

getCollisionsTotalPenetration(self: robotic._robotic.Config) → float: returns the sum of all penetrations (using FCL for broadphase; and low-level GJK/MRP for fine pair-wise distance/penetration computation)

getFrame(self: robotic._robotic.Config, frameName: str, warnIfNotExist: bool = True) → robotic._robotic.Frame: get access to a frame by name; use the Frame methods to set/get frame properties

getFrameDimension(self: robotic._robotic.Config) → int: get the total number of frames

getFrameNames(self: robotic._robotic.Config) → list[str]: get the list of frame names

getFrameState(self: robotic._robotic.Config) → numpy.ndarray[numpy.float64]: get the frame state as a n-times-7 numpy matrix, with a 7D pose per frame

getJointDimension(self: robotic._robotic.Config) → int: get the total number of degrees of freedom

getJointLimits(self: robotic._robotic.Config) → arr: get the joint limits as a n-by-2 matrix; for dofs that do not have limits defined, the entries are [0,-1] (i.e. upper limit < lower limit)

getJointNames(self: robotic._robotic.Config) → StringA: get the list of joint names

getJointState(self: robotic._robotic.Config) → arr: get the joint state as a numpy vector, optionally only for a subset of joints specified as list of joint names

report(self: robotic._robotic.Config) → str: return a string with basic info (#frames, etc)

selectJoints(self: robotic._robotic.Config, jointNames: list[str], notThose: bool = False) → None: redefine what are considered the DOFs of this configuration: only joints listed in jointNames are considered part of the joint state and define the number of DOFs

setFrameState(*args, **kwargs)

Overloaded function.

setFrameState(self: robotic._robotic.Config, X: list[float], frames: list[str] = []) -> None

set the frame state, optionally only for a subset of frames specified as list of frame names

setFrameState(self: robotic._robotic.Config, X: numpy.ndarray, frames: list[str] = []) -> None

set the frame state, optionally only for a subset of frames specified as list of frame names

setJointState(self: robotic._robotic.Config, q: arr, joints: list = []) → None: set the joint state, optionally only for a subset of joints specified as list of joint names

setJointStateSlice(self: robotic._robotic.Config, arg0: list[float], arg1: int) → None

view(self: robotic._robotic.Config, pause: bool = False, message: str = None) → int: open a view window for the configuration

view_close(self: robotic._robotic.Config) → None: close the view

view_focalLength(self: robotic._robotic.Config) → float: return the focal length of the view camera (only intrinsic parameter)

view_fxycxy(self: robotic._robotic.Config) → arr: return (fx, fy, cx, cy): the focal length and image center in PIXEL UNITS

view_getDepth(self: robotic._robotic.Config) → numpy.ndarray[numpy.float32]

view_getRgb(self: robotic._robotic.Config) → numpy.ndarray[numpy.uint8]

view_pose(self: robotic._robotic.Config) → arr: return the 7D pose of the view camera

view_raise(self: robotic._robotic.Config) → None: raise the view

view_recopyMeshes(self: robotic._robotic.Config) → None

view_savePng(self: robotic._robotic.Config, pathPrefix: str = 'z.vid/') → None: saves a png image of the current view, numbered with a global counter, with the intention to make a video

view_setCamera(self: robotic._robotic.Config, arg0: robotic._robotic.Frame) → None: set the camera pose to a frame, and check frame attributes for intrinsic parameters (focalLength, width height)

watchFile(self: robotic._robotic.Config, arg0: str) → None: launch a viewer that listents (inode) to changes of a file (made by you in an editor), and reloads, displays and animates the configuration whenever the file is changed

write(self: robotic._robotic.Config) → str: write the full configuration in a string (roughly yaml), e.g. for file export

writeCollada(self: robotic._robotic.Config, filename: str, format: str = 'collada') → None: write the full configuration in a collada file for export

writeMesh(self: robotic._robotic.Config, filename: str) → None: write the full configuration in a ply mesh file

writeMeshes(self: robotic._robotic.Config, pathPrefix: str) → None: write all object meshes in a directory

writeURDF(self: robotic._robotic.Config) → str: write the full configuration as URDF in a string, e.g. for file export

class robotic._robotic.ControlMode

Members:

none

position

velocity

acceleration

spline

property name

class robotic._robotic.FS

Members:

position

positionDiff

positionRel

quaternion

quaternionDiff

quaternionRel

pose

poseDiff

poseRel

vectorX

vectorXDiff

vectorXRel

vectorY

vectorYDiff

vectorYRel

vectorZ

vectorZDiff

vectorZRel

scalarProductXX

scalarProductXY

scalarProductXZ

scalarProductYX

scalarProductYY

scalarProductYZ

scalarProductZZ

gazeAt

angularVel

accumulatedCollisions

jointLimits

distance

negDistance

oppose

qItself

jointState

aboveBox

insideBox

pairCollision_negScalar

pairCollision_vector

pairCollision_normal

pairCollision_p1

pairCollision_p2

standingAbove

physics

contactConstraints

energy

transAccelerations

transVelocities

property name

class robotic._robotic.Frame

A (coordinate) frame of a configuration, which can have a parent, and associated shape, joint, and/or inertia

addAttributes(self: robotic._robotic.Frame, arg0: dict) → None: add/set attributes for the frame

getAttributes(self: robotic._robotic.Frame) → dict: get frame attributes

getJointState(self: robotic._robotic.Frame) → arr

getMesh(self: robotic._robotic.Frame) → tuple

getMeshPoints(self: robotic._robotic.Frame) → arr

getMeshTriangles(self: robotic._robotic.Frame) → uintA

getPosition(self: robotic._robotic.Frame) → arr

getQuaternion(self: robotic._robotic.Frame) → arr

getRelativePosition(self: robotic._robotic.Frame) → arr

getRelativeQuaternion(self: robotic._robotic.Frame) → arr

getRelativeTransform(self: robotic._robotic.Frame) → arr

getRotationMatrix(self: robotic._robotic.Frame) → arr

getSize(self: robotic._robotic.Frame) → arr

getTransform(self: robotic._robotic.Frame) → arr

info(self: robotic._robotic.Frame) → dict

setAttribute(self: robotic._robotic.Frame, arg0: str, arg1: float) → robotic._robotic.Frame

setColor(self: robotic._robotic.Frame, arg0: arr) → robotic._robotic.Frame

setContact(self: robotic._robotic.Frame, arg0: int) → robotic._robotic.Frame

setConvexMesh(self: robotic._robotic.Frame, points: arr, colors: Array<T> = array(1, dtype=uint8), radius: float = 0.0) → robotic._robotic.Frame: attach a convex mesh as shape

setDensity(self: robotic._robotic.Frame, data: Array<T>, size: arr) → robotic._robotic.Frame

setImplicitSurface(self: robotic._robotic.Frame, data: Array<T>, size: arr, blur: int, resample: float = -1.0) → robotic._robotic.Frame

setJoint(self: robotic._robotic.Frame, jointType: robotic._robotic.JT, limits: arr = array(0.0078125)) → robotic._robotic.Frame

setJointState(self: robotic._robotic.Frame, arg0: arr) → robotic._robotic.Frame

setMass(self: robotic._robotic.Frame, arg0: float) → robotic._robotic.Frame

setMesh(self: robotic._robotic.Frame, vertices: arr, triangles: uintA, colors: Array<T> = array(1, dtype=uint8), cvxParts: uintA = array(0, dtype=uint32)) → robotic._robotic.Frame: attach a mesh shape

setMeshAsLines(self: robotic._robotic.Frame, arg0: list[float]) → None

setParent(self: robotic._robotic.Frame, parent: robotic._robotic.Frame, keepAbsolutePose_and_adaptRelativePose: bool = False, checkForLoop: bool = False) → robotic._robotic.Frame

setPointCloud(self: robotic._robotic.Frame, points: arr, colors: Array<T> = array(1, dtype=uint8), normals: arr = array(0.0078125)) → robotic._robotic.Frame: attach a point cloud shape

setPose(self: robotic._robotic.Frame, arg0: str) → None

setPosition(self: robotic._robotic.Frame, arg0: arr) → robotic._robotic.Frame

setQuaternion(self: robotic._robotic.Frame, arg0: arr) → robotic._robotic.Frame

setRelativePose(self: robotic._robotic.Frame, arg0: str) → None

setRelativePosition(self: robotic._robotic.Frame, arg0: arr) → robotic._robotic.Frame

setRelativeQuaternion(self: robotic._robotic.Frame, arg0: arr) → robotic._robotic.Frame

setShape(self: robotic._robotic.Frame, type: robotic._robotic.ST, size: arr) → robotic._robotic.Frame

unLink(self: robotic._robotic.Frame) → robotic._robotic.Frame

class robotic._robotic.JT

Members:

hingeX

hingeY

hingeZ

transX

transY

transZ

transXY

trans3

transXYPhi

transYPhi

universal

rigid

quatBall

phiTransXY

XBall

free

generic

tau

property name

class robotic._robotic.KOMO

A framework to define manipulation problems (IK, path optimization, sequential manipulation) as Nonlinear Mathematical Program (NLP). The actual NLP_Solver class is separate. (KOMO = k-order Markov Optimization) – see https://marctoussaint.github.io/robotics-course/tutorials/1c-komo.html

addControlObjective(self: robotic._robotic.KOMO, times: arr, order: int, scale: float = 1.0, target: arr = array(0.0078125), deltaFromSlice: int = 0, deltaToSlice: int = 0) → Objective

times: (as for addObjective) the phase-interval in which this objective holds; [] means all times
order: Do we penalize the jointState directly (order=0: penalizing sqr distance to qHome, order=1: penalizing sqr distances between consecutive configurations (velocities), order=2: penalizing accelerations across 3 configurations)
scale: as usual, but modulated by a factor ‘sqrt(delta t)’ that somehow ensures total control costs in approximately independent of the choice of stepsPerPhase

addModeSwitch(self: robotic._robotic.KOMO, times: arr, newMode: robotic._robotic.SY, frames: StringA, firstSwitch: bool = True) → None

addObjective(self: robotic._robotic.KOMO, times: arr, feature: robotic._robotic.FS, frames: StringA, type: ObjectiveType, scale: arr = array(0.0078125), target: arr = array(0.0078125), order: int = -1) → None: central method to define objectives in the KOMO NLP: * times: the time intervals (subset of configurations in a path) over which this feature is active (irrelevant for IK) * feature: the feature symbol (see advanced Feature tutorial) * frames: the frames for which the feature is computed, given as list of frame names * type: whether this is a sum-of-squares (sos) cost, or eq or ineq constraint * scale: the matrix(!) by which the feature is multiplied * target: the offset which is substracted from the feature (before scaling)

addQuaternionNorms(self: robotic._robotic.KOMO, times: arr = array(0.0078125), scale: float = 3.0, hard: bool = True) → None

addStableFrame(self: robotic._robotic.KOMO, name: str, parent: str, jointType: robotic._robotic.JT, stable: bool, initFrame: str = 0) → robotic._robotic.Frame: complicated…

addTimeOptimization(self: robotic._robotic.KOMO) → None

clearObjectives(self: robotic._robotic.KOMO) → None

getFeatureNames(self: robotic._robotic.KOMO) → StringA: (This is to be passed to the NLP_Solver when needed.) returns a long list of features (per time slice!)

getForceInteractions(self: robotic._robotic.KOMO) → list

getFrameState(self: robotic._robotic.KOMO, arg0: int) → arr

getPath(self: robotic._robotic.KOMO) → arr

getPathFrames(self: robotic._robotic.KOMO) → arr

getPathTau(self: robotic._robotic.KOMO) → arr

getPath_qAll(self: robotic._robotic.KOMO) → arrA

getSubProblem(self: robotic._robotic.KOMO, phase: int) → tuple: return a tuple of (configuration, start q0, end q1) for given phase of this komo problem

getT(self: robotic._robotic.KOMO) → int

info_objectiveErrorTraces(self: robotic._robotic.KOMO) → arr: return a TxO, for O objectives

info_objectiveNames(self: robotic._robotic.KOMO) → StringA: return a array of O strings, for O objectives

info_sliceCollisions(self: robotic._robotic.KOMO, t: int, belowMargin: float) → rai::String: return string info of collosions belowMargin in slice t

info_sliceErrors(self: robotic._robotic.KOMO, t: int, errorTraces: arr) → rai::String: return string info of objectives and errors in slice t – needs errorTraces as input

initOrg(self: robotic._robotic.KOMO) → None

initPhaseWithDofsPath(self: robotic._robotic.KOMO, t_phase: int, dofIDs: uintA, path: arr, autoResamplePath: bool = False) → None

initRandom(self: robotic._robotic.KOMO, verbose: int = 0) → None

initWithConstant(self: robotic._robotic.KOMO, q: arr) → None

initWithPath(self: robotic._robotic.KOMO, q: arr) → None

initWithWaypoints(self: robotic._robotic.KOMO, waypoints: arrA, waypointSlicesPerPhase: int = 1, interpolate: bool = False, qHomeInterpolate: float = 0.0, verbose: int = -1) → uintA

nlp(self: robotic._robotic.KOMO) → NLP: return the problem NLP

report(self: robotic._robotic.KOMO, specs: bool = False, listObjectives: bool = True, plotOverTime: bool = False) → rai::Graph: returns a dict with full list of features, optionally also on problem specs and plotting costs/violations over time

setConfig(self: robotic._robotic.KOMO, config: robotic._robotic.Config, enableCollisions: bool) → None: [deprecated] please set directly in constructor

setTiming(self: robotic._robotic.KOMO, phases: float, slicesPerPhase: int, durationPerPhase: float, kOrder: int) → None: [deprecated] please set directly in constructor

updateRootObjects(self: robotic._robotic.KOMO, config: robotic._robotic.Config) → None: update root frames (without parents) within all KOMO configurations

view(self: robotic._robotic.KOMO, pause: bool = False, txt: str = None) → int

view_close(self: robotic._robotic.KOMO) → None

view_play(self: robotic._robotic.KOMO, pause: bool = False, delay: float = 0.1, saveVideoPath: str = None) → bool

view_slice(self: robotic._robotic.KOMO, t: int, pause: bool = False) → int

class robotic._robotic.NLP

Representation of a Nonlinear Mathematical Program

checkHessian(self: robotic._robotic.NLP, x: arr, tolerance: float) → bool

checkJacobian(self: robotic._robotic.NLP, x: arr, tolerance: float, featureNames: StringA = []) → bool

evaluate(self: robotic._robotic.NLP, arg0: arr) → tuple[arr, arr]: query the NLP at a point $x$; returns the tuple $(phi,J)$, which is the feature vector and its Jacobian; features define cost terms, sum-of-square (sos) terms, inequalities, and equalities depending on ‘getFeatureTypes’

getBounds(self: robotic._robotic.NLP) → arr: returns the tuple $(b_{lo},b_{up})$, where both vectors are of same dimensionality of $x$ (or size zero, if there are no bounds)

getDimension(self: robotic._robotic.NLP) → int: return the dimensionality of $x$

getFHessian(self: robotic._robotic.NLP, arg0: arr) → arr: returns Hessian of the sum of $f$-terms

getFeatureTypes(self: robotic._robotic.NLP) → list[ObjectiveType]: features (entries of $phi$) can be of one of (ry.OT.f, ry.OT.sos, ry.OT.ineq, ry.OT.eq), which means (cost, sum-of-square, inequality, equality). The total cost $f(x)$ is the sum of all f-terms plus sum-of-squares of sos-terms.

getInitializationSample(self: robotic._robotic.NLP, previousOptima: arr = array(0.0078125)) → arr: returns a sample (e.g. uniform within bounds) to initialize an optimization – not necessarily feasible

report(self: robotic._robotic.NLP, arg0: int) → str: displays semantic information on the last query

class robotic._robotic.NLP_Factory

setBounds(self: robotic._robotic.NLP_Factory, arg0: arr, arg1: arr) → None

setDimension(self: robotic._robotic.NLP_Factory, arg0: int) → None

setEvalCallback(self: robotic._robotic.NLP_Factory, arg0: Callable[[arr], tuple[arr, arr]]) → None

setFeatureTypes(self: robotic._robotic.NLP_Factory, arg0: Array<T>) → None

testCallingEvalCallback(self: robotic._robotic.NLP_Factory, arg0: arr) → tuple[arr, arr]

class robotic._robotic.NLP_Solver

An interface to portfolio of solvers

getOptions(self: robotic._robotic.NLP_Solver) → robotic._robotic.NLP_SolverOptions

getProblem(self: robotic._robotic.NLP_Solver) → robotic._robotic.NLP

getTrace_J(self: robotic._robotic.NLP_Solver) → arr

getTrace_costs(self: robotic._robotic.NLP_Solver) → arr: returns steps-times-3 array with rows (f+sos-costs, ineq, eq)

getTrace_phi(self: robotic._robotic.NLP_Solver) → arr

getTrace_x(self: robotic._robotic.NLP_Solver) → arr: returns steps-times-n array with queries points in each row

reportLagrangeGradients(self: robotic._robotic.NLP_Solver, list of names for each objective: StringA = []) → dict: return dictionary of Lagrange gradients per objective

setOptions(self: robotic._robotic.NLP_Solver, verbose: int = 1, stopTolerance: float = 0.01, stopFTolerance: float = -1.0, stopGTolerance: float = -1.0, stopEvals: int = 1000, maxStep: float = 0.2, damping: float = 1.0, stepInc: float = 1.5, stepDec: float = 0.5, wolfe: float = 0.01, muInit: float = 1.0, muInc: float = 5.0, muMax: float = 10000.0, muLBInit: float = 0.1, muLBDec: float = 0.2, maxLambda: float = -1.0) → robotic._robotic.NLP_Solver: set solver options

setProblem(self: robotic._robotic.NLP_Solver, arg0: robotic._robotic.NLP) → robotic._robotic.NLP_Solver

setPyProblem(self: robotic._robotic.NLP_Solver, arg0: object) → None

setSolver(self: robotic._robotic.NLP_Solver, arg0: NLP_SolverID) → robotic._robotic.NLP_Solver

setTracing(self: robotic._robotic.NLP_Solver, arg0: bool, arg1: bool, arg2: bool, arg3: bool) → robotic._robotic.NLP_Solver

solve(self: robotic._robotic.NLP_Solver, resampleInitialization: int = -1) → SolverReturn

class robotic._robotic.NLP_SolverID

Members:

gradientDescent

rprop

LBFGS

newton

augmentedLag

squaredPenalty

logBarrier

singleSquaredPenalty

NLopt

Ipopt

Ceres

property name

class robotic._robotic.NLP_SolverOptions

solver options

dict(self: robotic._robotic.NLP_SolverOptions) → dict

set_damping(self: robotic._robotic.NLP_SolverOptions, arg0: float) → robotic._robotic.NLP_SolverOptions

set_maxLambda(self: robotic._robotic.NLP_SolverOptions, arg0: float) → robotic._robotic.NLP_SolverOptions

set_maxStep(self: robotic._robotic.NLP_SolverOptions, arg0: float) → robotic._robotic.NLP_SolverOptions

set_muInc(self: robotic._robotic.NLP_SolverOptions, arg0: float) → robotic._robotic.NLP_SolverOptions

set_muInit(self: robotic._robotic.NLP_SolverOptions, arg0: float) → robotic._robotic.NLP_SolverOptions

set_muLBDec(self: robotic._robotic.NLP_SolverOptions, arg0: float) → robotic._robotic.NLP_SolverOptions

set_muLBInit(self: robotic._robotic.NLP_SolverOptions, arg0: float) → robotic._robotic.NLP_SolverOptions

set_muMax(self: robotic._robotic.NLP_SolverOptions, arg0: float) → robotic._robotic.NLP_SolverOptions

set_stepDec(self: robotic._robotic.NLP_SolverOptions, arg0: float) → robotic._robotic.NLP_SolverOptions

set_stepInc(self: robotic._robotic.NLP_SolverOptions, arg0: float) → robotic._robotic.NLP_SolverOptions

set_stopEvals(self: robotic._robotic.NLP_SolverOptions, arg0: int) → robotic._robotic.NLP_SolverOptions

set_stopFTolerance(self: robotic._robotic.NLP_SolverOptions, arg0: float) → robotic._robotic.NLP_SolverOptions

set_stopGTolerance(self: robotic._robotic.NLP_SolverOptions, arg0: float) → robotic._robotic.NLP_SolverOptions

set_stopTolerance(self: robotic._robotic.NLP_SolverOptions, arg0: float) → robotic._robotic.NLP_SolverOptions

set_verbose(self: robotic._robotic.NLP_SolverOptions, arg0: int) → robotic._robotic.NLP_SolverOptions

set_wolfe(self: robotic._robotic.NLP_SolverOptions, arg0: float) → robotic._robotic.NLP_SolverOptions

class robotic._robotic.OT

Members:

none

f

sos

ineq

eq

ineqB

ineqP

property name

class robotic._robotic.PathFinder

todo doc

setExplicitCollisionPairs(self: robotic._robotic.PathFinder, collisionPairs: StringA) → None: only after setProblem

setProblem(self: robotic._robotic.PathFinder, Configuration: robotic._robotic.Config, starts: arr, goals: arr) → None

solve(self: robotic._robotic.PathFinder) → SolverReturn

class robotic._robotic.ST

Members:

none

box

sphere

capsule

mesh

cylinder

marker

pointCloud

ssCvx

ssBox

ssCylinder

ssBoxElip

quad

camera

sdf

property name

class robotic._robotic.SY

Members:

touch

above

inside

oppose

restingOn

poseEq

positionEq

stableRelPose

stablePose

stable

stableOn

dynamic

dynamicOn

dynamicTrans

quasiStatic

quasiStaticOn

downUp

break

stableZero

contact

contactStick

contactComplementary

bounce

push

magic

magicTrans

pushAndPlace

topBoxGrasp

topBoxPlace

dampMotion

identical

alignByInt

makeFree

forceBalance

relPosY

touchBoxNormalX

touchBoxNormalY

touchBoxNormalZ

boxGraspX

boxGraspY

boxGraspZ

lift

stableYPhi

stableOnX

stableOnY

end

property name

class robotic._robotic.Simulation

A direct simulation interface to physics engines (Nvidia PhysX, Bullet) – see https://marctoussaint.github.io/robotics-course/tutorials/simulation.html

addSensor(self: robotic._robotic.Simulation, sensorName: str, frameAttached: str = '', width: int = 640, height: int = 360, focalLength: float = -1.0, orthoAbsHeight: float = -1.0, zRange: arr = []) → rai::CameraView::Sensor

attach(self: robotic._robotic.Simulation, gripper: robotic._robotic.Frame, obj: robotic._robotic.Frame) → None

closeGripper(self: robotic._robotic.Simulation, gripperFrameName: str, width: float = 0.05, speed: float = 0.3, force: float = 20.0) → None

depthData2pointCloud(self: robotic._robotic.Simulation, arg0: numpy.ndarray[numpy.float32], arg1: list[float]) → numpy.ndarray[numpy.float64]

detach(self: robotic._robotic.Simulation, obj: robotic._robotic.Frame) → None

getGripperIsGrasping(self: robotic._robotic.Simulation, gripperFrameName: str) → bool

getGripperWidth(self: robotic._robotic.Simulation, gripperFrameName: str) → float

getImageAndDepth(self: robotic._robotic.Simulation) → tuple

getScreenshot(self: robotic._robotic.Simulation) → Array<T>

getState(self: robotic._robotic.Simulation) → tuple: returns a 4-tuple or frame state, joint state, frame velocities (linear & angular), joint velocities

getTimeToSplineEnd(self: robotic._robotic.Simulation) → float

get_q(self: robotic._robotic.Simulation) → arr

get_qDot(self: robotic._robotic.Simulation) → arr

openGripper(self: robotic._robotic.Simulation, gripperFrameName: str, width: float = 0.075, speed: float = 0.3) → None

pushConfigurationToSimulator(self: robotic._robotic.Simulation, frameVelocities: arr = array(0.0078125), jointVelocities: arr = array(0.0078125)) → None: set the simulator to the full (frame) state of the configuration

resetSplineRef(self: robotic._robotic.Simulation) → None: reset the spline reference, i.e., clear the current spline buffer and initialize it to constant spline at current position (to which setSplineRef can append)

selectSensor(self: robotic._robotic.Simulation, sensorName: str) → rai::CameraView::Sensor

setSplineRef(self: robotic._robotic.Simulation, path: arr, times: arr, append: bool = True) → None: set the spline reference to generate motion * path: single configuration, or sequence of spline control points * times: array with single total duration, or time for each control point (times.N==path.d0) * append: append (with zero-velocity at append), or smoothly overwrite

setState(self: robotic._robotic.Simulation, frameState: arr, jointState: arr = array(0.0078125), frameVelocities: arr = array(0.0078125), jointVelocities: arr = array(0.0078125)) → None

step(self: robotic._robotic.Simulation, u_control: arr, tau: float = 0.01, u_mode: robotic._robotic.ControlMode = <ControlMode.velocity: 2>) → None

class robotic._robotic.SimulationEngine

Members:

physx

bullet

kinematic

property name

class robotic._robotic.SolverReturn

return of nlp solve call

dict(self: robotic._robotic.SolverReturn) → dict

robotic._robotic.compiled() → str: return a compile date+time version string

robotic._robotic.depthImage2PointCloud(depth: numpy.ndarray[numpy.float32], fxycxy: arr) → arr: return the point cloud from the depth image

robotic._robotic.params_add(python dictionary or params to add: dict) → None: add/set parameters

robotic._robotic.params_clear() → None: clear all parameters

robotic._robotic.params_file(filename: str) → None: add parameters from a file

robotic._robotic.params_print() → None: print the parameters

robotic._robotic.raiPath(arg0: str) → rai::String: get a path relative to rai base path

robotic._robotic.setRaiPath(arg0: str) → None: redefine the rai (or rai-robotModels) path