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Robotics codes from scratch (RCFS) is a collection of source codes to study and test learning and optimization problems in 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.
RCFS comes with an accompanying PDF containing the corresponding descriptions of the algorithms.
The RCFS website also includes interactive exercises: https://rcfs.ch

List of examples


Filename
Description
.m
.py
.cpp
.jl


MP
Movement primitives with various basis functions
✅
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spline1D
Concatenated Bernstein basis functions with C0 and C1 constraints to encode a trajectory (1D input, 1D output)
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spline2D
Concatenated Bernstein basis functions with constraints to encode a signed distance function (2D inputs, 1D output)
✅
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spline2D_eikonal
Gauss-Newton optimization of an SDF encoded with concatenated cubic polysplines, by considering unit norm derivatives in the cost function
✅
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IK_manipulator
Inverse kinematics for a planar manipulator
✅
✅
✅
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IK_manipulator3D
Inverse kinematics in a 3D workspace for a manipulator defined with DH Parameters (standard or modified)

✅


IK_bimanual
Inverse kinematics with a planar bimanual robot

✅


IK_nullspace
Inverse kinematics with nullspace projection (position and orientation tracking as primary or secondary tasks)
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IK_num
Inverse kinematics with numerical computation for a planar manipulator
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FD
Forward Dynamics computed for a planar manipulator
✅
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LQR_infHor
Infinite Horizon Linear quadratic regulator (LQR) applied to a point mass system
✅
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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)
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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
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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_distMaintenance
Iterative linear quadratic regulator (iLQR) applied to a 2D point-mass system with the objective of constantly maintaining a desired distance to an object
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iLQR_obstacle
iLQR applied to a viapoint task with obstacles avoidance (batch formulation)
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GPIS
Gaussian process implicit surfaces (GPIS)

✅


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_manipulator3D
iLQR (batch formulation) in a 3D workspace applied to a Franka Emika manipulator for a viapoints task
✅
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iLQR_manipulator_initStateOptim
iLQR applied to a planar manipulator, where both an optimal controller and an optimal robot base location are estimated
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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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ergodic_control_HEDAC_1D
1D ergodic control formulated as Heat Equation Driven Area Coverage (HEDAC)

✅


ergodic_control_HEDAC_2D
2D ergodic control formulated as Heat Equation Driven Area Coverage (HEDAC)

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ergodic_control_SMC_1D
1D ergodic control formulated as Spectral Multiscale Coverage (SMC)
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ergodic_control_SMC_2D
2D ergodic control formulated as Spectral Multiscale Coverage (SMC)
✅
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ergodic_control_SMC_DDP_1D
1D trajectory optimization for ergodic control problem
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ergodic_control_SMC_DDP_2D
2D trajectory optimization for ergodic control problem
✅
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Maintenance, contributors and licensing
RCFS is maintained by Sylvain Calinon, https://calinon.ch
Contributors: Sylvain Calinon, Philip Abbet, Jérémy Maceiras, Hakan Girgin, Julius Jankowski, Teguh Lembono, Tobias Löw, Amirreza Razmjoo, Boyang Ti, Teng Xue, Yifei Dong, Yiming Li, Cem Bilaloglu, Yan Zhang, Guillaume Clivaz, Maximilien Dufau
Copyright (c) 2024 Idiap Research Institute, https://idiap.ch
RCFS is licensed under the GPLv3 License.