Robotics codes from scratch (RCFS)
RCFS is a collection of source codes to study robotics through simple 2D examples. Most examples are coded in Python and Matlab/Octave (full compatibility with GNU Octave). Some are also coded in C++ and Julia. The code examples have .m, .py, .cpp and .jl extensions that can be found in their respective folders matlab, python, cpp and julia.
List of examples
| Filename | Description | .m | .py | .cpp | .jl |
|---|---|---|---|---|---|
| MP | Movement primitives with various basis functions | ✅ | ✅ | ||
| IK | Inverse kinematics for a planar manipulator | ✅ | ✅ | ✅ | ✅ |
| IK_nullspace | Inverse kinematics with nullspace projection (position and orientation tracking as primary or secondary tasks) | ✅ | |||
| IK_num | Inverse kinematics with numerical computation for a planar manipulator | ✅ | ✅ | ||
| FD | Forward Dynamics computed in matrix form for a planar manipulator | ✅ | ✅ | ||
| FD_recursive | Forward Dynamics with recursive computation for a planar manipulator | ✅ | ✅ | ||
| LQR_infHor | Infinite Horizon Linear quadratic regulator (LQR) applied to a point mass system | ✅ | ✅ | ||
| LQT | Linear quadratic tracking (LQT) applied to a viapoint task (batch formulation) | ✅ | ✅ | ||
| LQT_tennisServe | LQT in a ballistic task mimicking a bimanual tennis serve problem (batch formulation) | ✅ | |||
| LQT_recursive | LQT applied to a viapoint task with a recursive formulation based on augmented state space to find a controller) | ✅ | ✅ | ||
| LQT_nullspace | Batch LQT with nullspace formulation | ✅ | ✅ | ||
| LQT_recursive_LS | LQT applied to a viapoint task with a recursive formulation based on least squares and an augmented state space to find a controller | ✅ | ✅ | ||
| LQT_recursive_LS_multiAgents | LQT applied to a multi-agent system with recursive formulation based on least squares and augmented state, by using a precision matrix with nonzero offdiagonal elements to find a controller in which the two agents coordinate their movements to find an optimal meeting point | ✅ | ✅ | ||
| LQT_CP | LQT with control primitives applied to a viapoint task (batch formulation) | ✅ | ✅ | ||
| LQT_CP_DMP | LQT with control primitives applied to trajectory tracking with a formulation similar to dynamical movement primitives (DMP), by using the least squares formulation of recursive LQR on an augmented state space | ✅ | ✅ | ||
| iLQR_obstacle | Iterative linear quadratic regulator (iLQR) applied to a viapoint task with obstacles avoidance (batch formulation) | ✅ | ✅ | ||
| iLQR_obstacle_GPIS | iLQR with obstacles represented as Gaussian process implicit surfaces (GPIS) | ✅ | ✅ | ||
| iLQR_manipulator | iLQR applied to a planar manipulator for a viapoints task (batch formulation) | ✅ | ✅ | ✅ | |
| iLQR_manipulator_recursive | iLQR applied to a planar manipulator for a viapoints task (recursive formulation to find a controller) | ✅ | ✅ | ||
| iLQR_manipulator_CoM | iLQR applied to a planar manipulator for a tracking problem involving the center of mass (CoM) and the end-effector (batch formulation) | ✅ | ✅ | ||
| iLQR_manipulator_obstacle | iLQR applied to a planar manipulator for a viapoints task with obstacles avoidance (batch formulation) | ✅ | ✅ | ||
| iLQR_manipulator_CP | iLQR with control primitives applied to a viapoint task with a manipulator (batch formulation) | ✅ | ✅ | ||
| iLQR_manipulator_object_affordance | iLQR applied to an object affordance planning problem with a planar manipulator, by considering object boundaries (batch formulation) | ✅ | ✅ | ||
| iLQR_manipulator_dynamics | iLQR applied to a reaching task by considering the dynamics of the manipulator | ✅ | ✅ | ||
| iLQR_bimanual | iLQR applied to a planar bimanual robot for a tracking problem involving the center of mass (CoM) and the end-effector (batch formulation) | ✅ | ✅ | ||
| iLQR_bimanual_manipulability | iLQR applied to a planar bimanual robot problem with a cost on tracking a desired manipulability ellipsoid at the center of mass (batch formulation) | ✅ | |||
| iLQR_bicopter | iLQR applied to a bicopter problem (batch formulation) | ✅ | ✅ | ✅ | |
| iLQR_car | iLQR applied to a car parking problem (batch formulation) | ✅ | ✅ | ✅ |
Check also the PDF with the corresponding mathematical descriptions.
Additional reading material can be be found as video lectures with corresponding slides in PDF format.
Work in progress
| Filename | Main responsible |
|---|---|
| Example similar to demo_OC_LQT_Lagrangian01.m (inclusion of constraints) | ??? |
TODO:
- fix glut display issues in C++ (ellipsoids, resizing windows)
- add missing equations related to S¹ manifold in RCFS.tex
License
RCFS is maintained by Sylvain Calinon, https://calinon.ch/.
Contributors: Jérémy Maceiras, Julius Jankowski, Teguh Lembono, Tobias Löw, Amirreza Razmjoo, Boyang Ti, Teng Xue
Copyright (c) 2022 Idiap Research Institute, https://idiap.ch/
RCFS is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License version 3 as published by the Free Software Foundation.
You should have received a copy of the GNU General Public License along with RCFS. If not, see https://www.gnu.org/licenses/.