{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# An introduction to Jiminy\n", "\n", "\n", "Jiminy is a simulation framework built around the `pinocchio` rigid body dynamics library, targeting the simulation of robotic systems. This document covers the use of jiminy in python for individual robotic simulation: usage in C++ is similar, see C++ API documentation and examples. For information about using jiminy in a learning context with Gym OpenAI, see examples in gym_jiminy. \n", "\n", "Jiminy supports the following features:\n", "\n", " - Simulating a single robot, represented by a URDF file, with possible ground interaction (modeled as a spring-damper mechanism). Note that interaction forces are only applied at user-specified frames, there is no mesh collision handling in jiminy for now.\n", " - Adding joint flexibility, modeled as a Serie-Elastic actuator.\n", " - Applying external forces on the system.\n", " - Applying kinematic constraints, such as fixed-body constraints.\n", " - Simulating multiple robots interacting, including closed kinematic trees.\n", " - Adding basic sensors on the system\n", "\n", "## Basic example\n", "\n", "To illustrate the first part of this tutorial, we will use as example the stabilization of a simple inverted pendulum.\n", "\n", "Jiminy simulation rely on interfacing three fundamental objects : a Robot, a Controller, defining input for this robot, and an Engine, performing the integration. For convenience, connection between these objects is handled in python by the BasicSimulator class (this class does not exist in C++). Of course, it is always possible to instanciate these objects manually for more specific usage - see the unit tests for examples.\n", "\n", "The robot is constructed from a URDF - this builds a jiminy.Model object - but extra information needs to be provided as well for a simulation: which sensors to use and what are their caracteristic ? Which joints have a motor attached and what are its properties ? What are the contact points with the ground (if any) ? All this are informations gathered to build a full robot.\n", "\n", "So let's get our first example running: we set the inverted pendulum away from its upward position and watch it fall." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib widget" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:Boost.Python cannot be shared between modules. Importing bundled jiminy dependencies instead of system-wide install as fallback.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b7b5c3fbfecd42118f11583f883bae70", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0.00/10.0 [00:00]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "import jiminy_py.core as jiminy # The main module of jiminy - this is what gives access to the Robot\n", "from jiminy_py.simulator import Simulator\n", "\n", "try:\n", " from importlib.resources import files\n", "except ImportError:\n", " from importlib_resources import files\n", "\n", "\n", "data_root_path = files(\"gym_jiminy.envs\") / \"data/toys_models/simple_pendulum\"\n", "urdf_path = str(data_root_path / \"simple_pendulum.urdf\")\n", "\n", "# Instantiate and initialize the robot\n", "robot = jiminy.Robot()\n", "robot.initialize(urdf_path, mesh_package_dirs=[str(data_root_path)])\n", "\n", "# Add a single motor\n", "motor = jiminy.SimpleMotor(\"PendulumJoint\")\n", "robot.attach_motor(motor)\n", "motor.initialize(\"PendulumJoint\")\n", "\n", "# Define the command: for now, the motor is off and doesn't modify the output torque.\n", "def compute_command(t, q, v, sensor_measurements, command):\n", " command[:] = 0.0\n", "\n", "# Instantiate and initialize the controller\n", "robot.controller = jiminy.FunctionalController(compute_command)\n", "\n", "# Create a simulator using this robot and controller\n", "simulator = Simulator(robot)\n", "\n", "# Set initial condition and simulation length\n", "q0, v0 = np.array([0.1]), np.array([0.0])\n", "simulation_duration = 10.0\n", "\n", "# Launch the simulation\n", "simulator.simulate(simulation_duration, q0, v0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simulation generates a log of its computation: this log can be retrieved by using ```simulator.log_data``` - and written to a file for latter processing by the engine with ```simulator.engine.write_log```." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "441c30fd6bd245bb960e5a9ac62292a4", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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