Franky is a high-level motion library (both C++ and Python) for the Franka Emika robot. It adds a Python wrapper around libfranka, while replacing necessary real-time programming with higher-level motion commands. As franky focuses on making real-time trajectory generation easy, it allows the robot to react to unforeseen events.
Franky is a fork of frankx, though both codebase and functionality differ substantially from frankx by now. In particular, franky provides the following new features/improvements:
- Motions can be updated asynchronously.
- Reactions allow for the registration of callbacks instead of just printing to stdout when fired.
- The robot state is also available during control.
- A larger part of the libfranka API is exposed to python (e.g.,
setCollisionBehavior
,setJoinImpedance
, andsetCartesianImpedance
). - Cartesian motion generation handles boundaries in Euler angles properly.
- There is a new joint motion type that supports waypoints.
- The signature of
Affine
changed.Affine
does not handle elbow positions anymore. Instead, a new classRobotPose
stores both the end-effector pose and optionally the elbow position. - The
MotionData
class does not exist anymore. Instead, reactions and other settings moved toMotion
. - The
Measure
class allows for arithmetic operations. - Exceptions caused by libfranka are raised properly instead of being printed to stdout.
- We provide wheels for both Franka Research 3 and the older Franka Panda
To start using franky with Python and libfranka 0.12.1, just install it via
pip install franky-panda
We also provide wheels for libfranka versions 0.7.1, 0.8.0, 0.9.2, 0.10.0, 0.11.0, 0.12.1, 0.13.3. They can be installed via
VERSION=0-9-2
wget https://github.com/TimSchneider42/franky/releases/latest/download/libfranka_${VERSION}_wheels.zip
unzip libfranka_${VERSION}_wheels.zip
pip install --no-index --find-links=./dist franky-panda
Franky is based on libfranka, Eigen for transformation calculations and pybind11 for the Python bindings. Franky uses the Ruckig Community Version for Online Trajectory Generation (OTG). As the Franka is quite sensitive to acceleration discontinuities, it requires constrained jerk for all motions. After installing the dependencies (the exact versions can be found below), you can build and install franky via
git clone --recurse-submodules git@github.com:timschneider42/franky.git
cd franky
mkdir -p build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make
make install
To use franky, you can also include it as a subproject in your parent CMake via add_subdirectory(franky)
and then target_link_libraries(<target> libfranky)
. If you need only the Python module, you can install franky via
pip install .
Make sure that the built library can be found from Python by adapting your Python Path.
To use franky within Docker we have supplied a Dockerfile which you currently need to build yourself:
git clone https://github.com/timschneider42/franky.git
cd franky/
docker build -t franky --build-arg libfranka_version=0.7.0 -f docker/Dockerfile .
To use another version of libfranka than the default (v.0.7.0) simply change the build argument. Then, to run the container simply:
docker run -it --rm --network=host --privileged franky
The container requires access to the host machines network and elevated user rights to allow the docker user to set RT capabilities of the processes run from within it.
Franky comes with both a C++ and Python API that differ only regarding real-time capability. We will introduce both languages next to each other. In your C++ project, just include include <franky/franky.hpp>
and link the library. For Python, just import franky
. As a first example, only four lines of code are needed for simple robotic motions.
#include <franky/franky.hpp>
using namespace franky;
// Connect to the robot with the FCI IP address
Robot robot("172.16.0.2");
// Reduce velocity and acceleration of the robot
robot.setDynamicRel(0.05);
// Move the end-effector 20cm in positive x-direction
auto motion = CartesianMotion(RobotPose(Affine({0.2, 0.0, 0.0}), 0.0), ReferenceType::Relative);
// Finally move the robot
robot.move(motion);
The corresponding program in Python is
from franky import Affine, CartesianMotion, Robot, ReferenceType
robot = Robot("172.16.0.2")
robot.relative_dynamics_factor = 0.05
motion = CartesianMotion(Affine([0.2, 0.0, 0.0]), ReferenceType.Relative)
robot.move(motion)
Furthermore, we will introduce methods for geometric calculations, for moving the robot according to different motion types, how to implement real-time reactions and changing waypoints in real time as well as controlling the gripper.
franky.Affine
is a python wrapper for Eigen::Affine3d.
It is used for Cartesian poses, frames and transformation.
franky adds its own constructor, which takes a position and a quaternion as inputs:
import math
from scipy.spatial.transform import Rotation
from franky import Affine
z_translation = Affine([0.0, 0.0, 0.5])
quat = Rotation.from_euler("xyz", [0, 0, math.pi / 2]).as_quat()
z_rotation = Affine([0.0, 0.0, 0.0], quat)
combined_transformation = z_translation * z_rotation
In all cases, distances are in [m] and rotations in [rad].
We wrapped most of the libfanka API (including the RobotState or ErrorMessage) for Python.
Moreover, we added methods to adapt the dynamics of the robot for all motions.
The rel
name denotes that this a factor of the maximum constraints of the robot.
from franky import Robot
robot = Robot("172.16.0.2")
# Recover from errors
robot.recover_from_errors()
# Set velocity, acceleration and jerk to 5% of the maximum
robot.relative_dynamics_factor = 0.05
# Alternatively, you can define each constraint individually
robot.velocity_rel = 0.2
robot.acceleration_rel = 0.1
robot.jerk_rel = 0.01
# Get the current pose
current_pose = robot.current_pose
The robot state can be retrieved by calling the following methods:
-
state
: Return an object of thefranky.RobotState
class which contains most of the same attributes, under the same name, as the libfranka franka::RobotState definition. -
current_pose
: Return a 3D Affine transformation object of the measured end effector pose in base frame (alias for franka::RobotState::O_T_EE). -
current_joint_positions
: Return a sequence of the manipulator arm's 7-joint positions (alias for franka::RobotState::q).
robot = Robot("172.16.0.2")
# Get the current state
state = robot.state
pose = robot.current_pose
joint_positions = robot.current_joint_positions
Franky defines a number of different motion types. In python, you can use them as follows:
import math
from scipy.spatial.transform import Rotation
from franky import JointWaypointMotion, JointWaypoint, JointPositionStopMotion, CartesianMotion, CartesianWaypointMotion, CartesianWaypoint, Affine, RobotPose, ReferenceType, CartesianPoseStopMotion
# A point-to-point motion in the joint space
m1 = JointWaypointMotion([JointWaypoint([-1.8, 1.1, 1.7, -2.1, -1.1, 1.6, -0.4])])
# A motion in joint space with multiple waypoints
m2 = JointWaypointMotion([
JointWaypoint([-1.8, 1.1, 1.7, -2.1, -1.1, 1.6, -0.4]),
JointWaypoint([-1.7, 1.2, 1.8, -2.0, -1.0, 1.7, -0.3]),
JointWaypoint([-1.9, 1.0, 1.6, -2.2, -1.2, 1.5, -0.5])
])
# Stop the robot
m3 = JointPositionStopMotion()
# A linear motion in cartesian space
quat = Rotation.from_euler("xyz", [0, 0, math.pi / 2]).as_quat()
m4 = CartesianMotion(Affine([0.2, -0.4, 0.3], quat))
m5 = CartesianMotion(RobotPose(Affine([0.2, -0.4, 0.3], quat), elbow_position=1.7)) # With target elbow angle
# A linear motion in cartesian space relative to the initial position
# (Note that this motion is relative both in position and orientation. Hence, when the robot's end-effector is oriented
# differently, it will move in a different direction)
m6 = CartesianMotion(Affine([0.2, 0.0, 0.0]), ReferenceType.Relative)
# Generalization of CartesianMotion that allows for multiple waypoints
m7 = CartesianWaypointMotion([
CartesianWaypoint(RobotPose(Affine([0.2, -0.4, 0.3], quat), elbow_position=1.7)),
# The following waypoint is relative to the prior one and 50% slower
CartesianWaypoint(Affine([0.2, 0.0, 0.0]), ReferenceType.Relative, velocity_rel=0.5)
])
# Stop the robot. The difference of JointPositionStopMotion to CartesianPoseStopMotion is that JointPositionStopMotion
# stops the robot in joint position control mode while CartesianPoseStopMotion stops it in cartesian pose control mode.
# The difference becomes relevant when asynchronous move commands are being sent (see below).
m8 = CartesianPoseStopMotion()
Every motion and waypoint type allows to adapt the dynamics (velocity, acceleration and jerk) by setting the respective velocity_rel
, acceleration_rel
, and jerk_rel
parameters.
The real robot can be moved by applying a motion to the robot using move
:
robot.move(m1)
robot.move(m2)
By adding reactions to the motion data, the robot can react to unforeseen events. In the Python API, you can define conditions by using a comparison between a robot's value and a given threshold. If the threshold is exceeded, the reaction fires.
from franky import CartesianMotion, Affine, ReferenceType, Measure, Reaction
motion = CartesianMotion(Affine([0.0, 0.0, 0.1]), ReferenceType.Relative) # Move down 10cm
reaction_motion = CartesianMotion(Affine([0.0, 0.0, 0.01]), ReferenceType.Relative) # Move up for 1cm
# Trigger reaction if the Z force is greater than 30N
reaction = Reaction(Measure.FORCE_Z > 30.0, reaction_motion)
motion.add_reaction(reaction)
robot.move(motion)
Possible values to measure are
Measure.FORCE_X,
Measure.FORCE_Y,
Measure.FORCE_Z
: Force in X, Y and Z directionMeasure.REL_TIME
: Time in seconds since the current motion startedMeasure.ABS_TIME
: Time in seconds since the initial motion started
The difference between Measure.REL_TIME
and Measure.ABS_TIME
is that Measure.REL_TIME
is reset to zero whenever a new motion starts (either by calling Robot.move
or as a result of a triggered Reaction
).
Measure.ABS_TIME
, on the other hand, is only reset to zero when a motion terminates regularly without being interrupted and the robot stops moving.
Hence, Measure.ABS_TIME
measures the total time in which the robot has moved without interruption.
Measure
values support all classical arithmetic operations, like addition, subtraction, multiplication, division, and exponentiation (both as base and exponent).
normal_force = (Measure.FORCE_X ** 2 + Measure.FORCE_Y ** 2 + Measure.FORCE_Z ** 2) ** 0.5
With arithmetic comparisons, conditions can be generated.
normal_force_within_bounds = normal_force < 30.0
time_up = Measure.ABS_TIME > 10.0
Conditions support negation, conjunction (and), and disjunction (or):
abort = ~normal_force_within_bounds | time_up
fast_abort = ~normal_force_within_bounds | time_up
To check whether a reaction has fired, a callback can be attached:
from franky import RobotState
def reaction_callback(robot_state: RobotState, rel_time: float, abs_time: float):
print(f"Reaction fired at {abs_time}.")
reaction.register_callback(reaction_callback)
Note that these callbacks are not executed in the control thread since they would otherwise block it. Instead, they are put in a queue and executed by another thread. While this scheme ensures that the control thread can always run, it cannot prevent that the queue grows indefinitely when the callbacks take more time to execute than it takes for new callbacks to be queued. Hence, callbacks might be executed significantly after their respective reaction has fired if they are triggered in rapid succession or take a long time to execute.
In C++ you can additionally use lambdas to define more complex behaviours:
auto motion = CartesianMotion(RobotPose(Affine({0.0, 0.0, 0.2}), 0.0), ReferenceType::Relative);
// Stop motion if force is over 10N
auto stop_motion = StopMotion<franka::CartesianPose>()
motion
.addReaction(
Reaction(
Measure::ForceZ() > 10.0, // [N],
stop_motion))
.addReaction(
Reaction(
Condition(
[](const franka::RobotState& state, double rel_time, double abs_time) {
// Lambda condition
return state.current_errors.self_collision_avoidance_violation;
}),
[](const franka::RobotState& state, double rel_time, double abs_time) {
// Lambda reaction motion generator
// (we are just returning a stop motion, but there could be arbitrary
// logic here for generating reaction motions)
return StopMotion<franka::CartesianPose>();
})
));
robot.move(motion)
By setting the asynchronous
parameter of Robot.move
to True
, the function does not block until the motion finishes.
Instead, it returns immediately and, thus, allows the main thread to set new motions asynchronously.
import time
from franky import Affine, CartesianMotion, Robot, ReferenceType
robot = Robot("172.16.0.2")
robot.relative_dynamics_factor = 0.05
motion1 = CartesianMotion(Affine([0.2, 0.0, 0.0]), ReferenceType.Relative)
robot.move(motion1, asynchronous=True)
time.sleep(0.5)
motion2 = CartesianMotion(Affine([0.2, 0.0, 0.0]), ReferenceType.Relative)
robot.move(motion2, asynchronous=True)
By calling Robot.join_motion
the main thread can be synchronized with the motion thread, as it will block until the robot finishes its motion.
robot.join_motion()
Note that when exceptions occur during the asynchronous execution of a motion, they will not be thrown immediately.
Instead, the control thread stores the exception and terminates.
The next time Robot.join_motion
or Robot.move
are called, they will throw the stored exception in the main thread.
Hence, after an asynchronous motion has finished, make sure to call Robot.join_motion
to ensure being notified of any exceptions that occurred during the motion.
In the franky::Gripper
class, the default gripper force and gripper speed can be set.
Then, additionally to the libfranka commands, the following helper methods can be used:
auto gripper = Gripper("172.16.0.2");
// These are the default values
gripper.gripper_speed = 0.02; // [m/s]
gripper.gripper_force = 20.0; // [N]
// Preshape gripper before grasp, use the given speed
gripper.move(50.0); // [mm]
// Grasp an object of unknown width
is_grasping = gripper.clamp();
// Do something
is_grasping &= gripper.isGrasping();
// Release an object and move to a given distance
if (is_grasping) {
gripper.release(50.0);
}
The Python API is straight-forward for the Gripper class.
An auto-generated documentation can be found at https://timschneider42.github.io/franky/. Moreover, there are multiple examples for both C++ and Python in the examples directory.
Franky is written in C++17 and Python3.7. It is currently tested against following versions
- Eigen v3.4.0
- Libfranka v0.12.1
- Pybind11 v2.9.1
- Catch2 v2.13 (only for testing)
For non-commercial applications, this software is licensed under the LGPL v3.0. If you want to use franky within commercial applications or under a different license, please contact us for individual agreements.