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robotics-codes-from-scratch

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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
✅
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IK
Inverse kinematics for a planar manipulator
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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
✅
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FD
Forward Dynamics computed in matrix form for a planar manipulator
✅
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FD_recursive
Forward Dynamics with recursive computation for a planar manipulator
✅
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LQT
Linear quadratic tracking (LQT) applied to a viapoint task (batch formulation)
✅
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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)
✅
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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
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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
✅
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LQT_CP
LQT with control primitives applied to a viapoint task (batch formulation)
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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
✅
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iLQR_obstacle
Iterative linear quadratic regulator (iLQR) applied to a viapoint task with obstacles avoidance (batch formulation)
✅
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iLQR_obstacle_GPIS
iLQR with obstacles represented as Gaussian process implicit surfaces (GPIS)
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iLQR_manipulator
iLQR applied to a planar manipulator for a viapoints task (batch formulation)
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iLQR_manipulator_recursive
iLQR applied to a planar manipulator for a viapoints task (recursive formulation to find a controller)
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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)
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iLQR_manipulator_obstacle
iLQR applied to a planar manipulator for a viapoints task with obstacles avoidance (batch formulation)
✅
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iLQR_manipulator_CP
iLQR with control primitives applied to a viapoint task with a manipulator (batch formulation)
✅
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iLQR_manipulator_object_affordance
iLQR applied to an object affordance planning problem with a planar manipulator, by considering object boundaries (batch formulation)
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iLQR_manipulator_dynamics
iLQR applied to a reaching task by considering the dynamics of the manipulator
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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)
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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)
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iLQR_bicopter
iLQR applied to a bicopter problem (batch formulation)
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iLQR_car
iLQR applied to a car parking problem (batch formulation)
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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/.