Learning Visual Feedback Control for Dynamic Cloth Folding (IROS 2022)

The above gif has been slowed down to highlight the cloth dynamics, full speed videos can be found at https://sites.google.com/view/dynamic-cloth-folding/home

Introduction

This repository contains code and instructions on how to run the simulation training presented in our paper Learning Visual Feedback Control for Dynamic Cloth Folding published at 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022).

The setup has been tested on Ubuntu 22.04.1 LTS with Asus ROG Strix G513IE_G513IE (GeForce RTX 3050 Ti Mobile, Driver Version: 510.85.02, CUDA Version: 11.6).

Please contact the authors for instructions on how to run trained policies in the real world (our real-world setup included the Franka Emika Panda and Intel Realsense D435, but other robots and sensors can also be used).

Installation

• Run conda create -n dynamic-cloth-folding python=3.8.13 && conda activate dynamic-cloth-folding to create and activate a new python environment (conda is not a strict requirement)
• Run git clone [email protected]:hietalajulius/dynamic-cloth-folding.git --recursive to clone the repository and necessary submodules
• Install MuJoCo using these instructions (i.e. extract the downloaded mujoco210 directory into ~/.mujoco/mujoco210)
• Run cd dynamic-cloth-folding && ./install-dependencies.sh to install all required dependencies (assumes CUDA 11.6 and a compatible Nvidia GPU)

Training a model

The gif shows the RL simulation environment. It is implemented using MuJoCo and OpenAI Gym

• Run source env.sh before each training to set proper environment variables
• Run python train.py <--kwarg value> for a full training

Each training run will create a new directory in the trainings directory that contains

• The current neural network weights
• The settings used for the run
• The git commit hashes of the submodules
• The compiled MuJoCo models used during training
• The evaluation policy and images captured during evaluation for each epoch

Training progress and metrics can be visualized using tensorboard by running tensorboard --logdir tblogs in the root of the repository and opening http://localhost:6006/ in your browser.

Settings

The different settings are passed to the training script as keyword arguments. The available settings are:

General

Kwarg	Type	Default value	Description
--title	String	default	Give a title to the training run, used in filenames when saving progress.
--run	Int	0	Run number, used to generate random seeds. Useful when running multiple experiments with the same setup.
--num-processes	Int	1	How many python multiprocesses/parallel RL environments to use when collecting experience for the training.

Environment

The environment consists of a Franka Emika Panda robot (agent) and a cloth model lying on a flat surface. Visual feedback is captured using a camera whose settings are tuned to approximately match the Realsense D435 camera.

Kwarg	Type	Default value	Description
--cloth-size	Float	0.2	The cloth size in meters
--robot-observation	Choice[ee, ctrl, none]	ctrl	Whether the policy/value functions should observe the true end effector position (ee) or the current desired position of the controller (ctrl) or none
--filter	Float	0.03	The filter value to use in the convex combination interpolation of the controller desired position between time steps
--output-max	Float	0.03	The maximum Cartesian displacement in any direction of the previous controller desired position between time steps i.e. the maximum action from the policy
--damping-ratio	Float	1.0	The damping ratio of the OSC controller
--kp	Float	1000.0	Controller position gain
--success-distance	Float	0.05	The minimum distance within which the considered cloth points should be from their goals for the task to be considered successful
--frame-stack-size	Int	1	How many consecutive frames should be stacked together in the observation
--sparse-dense	Int (1=true, 0=false)	1	Whether the reward should increase linearly with the error if the task is considered successful
--success-reward	Int	0	The reward at a time step when the task is considered successful
--fail-reward	Int	-1	The reward at a time step when the task is considered unsuccessful
--extra-reward	Int	1	When using a sparse dense reward, the maximum extra reward that can be achieved
--timestep	Float	0.01	Simulation timestep length
--control-frequency	Int	10	Control frequency (Hz)
--camera-type	Choice[up, side, front, all]	side	The camera angle to use during training
--camera-config	Choice[small, large]	small	Defines the camera fovy range to use. small corresponds to a 848x100 image on the real camera whereas large corresponds to a 848x480 image on the real camera.

Domain randomization

Kwarg	Type	Default value	Description
--goal-noise	Float	0.03	The range within which the goal positions of the cloth points should be randomized
--image-obs-noise-mean	Float	0.5	The mean of the gaussian defining what the delay is between taking an action and observing an image within a [0,1] timespan between actions
--image-obs-noise-std	Float	0.5	The std of the gaussian defining what the delay is between taking an action and observing an image within a [0,1] timespan between actions
--lights-randomization	Int (1=true, 0=false)	1	Whether lights in the environment should be randomized
--materials-randomization	Int (1=true, 0=false)	1	Whether the visual cloth material properties in the environment should be randomized
--camera-position-randomization	Int (1=true, 0=false)	1	Whether the camera position in the environment should be randomized
--lookat-position-randomization-radius	Float	0.03	Within what radius in meters should the camera lookat position be randomized from the center of the cloth
--lookat-position-randomization	Int (1=true, 0=false)	1	Whether the lookat position should be randomized
--albumentations-randomization	Int (1=true, 0=false)	1	Whether the albumentations library should be used to randomly blur and adjust colors of the image observations
--dynamics-randomization	Int (1=true, 0=false)	1	Whether the cloth dynamics should be randomized
--fovy-perturbation-size	Float	0.05	The max percentage that the camera fovy should be randomized
--rotation-perturbation-size	Float	0.75	The max percentage that the camera rotation should be randomized
--position-perturbation-size	Float	0.2	The max percentage that the camera position should be randomized

Training

The training is structured as follows: one epoch consists of n_cycles cycles. A cycle consists of n_steps. A step means a single step in the RL environment, but also a single gradient update of the model.

Kwarg	Type	Default value	Description
--train-steps	Int	1000	How many steps should be performed per a single cycle
--num-cycles	Int	20	How many cycles should be performed per a single epoch
--num-epochs	Int	100	How many epochs to run for
--save-policy-every-epoch	Int	1	How often should the policy be saved during training
--num-eval-rollouts	Int	20	How many evaluation rollouts (until success or --max-path-length environment steps per rollout) to perform after each epoch
--batch-size	Int	256	The batch size to use during training the policy network
--discount	Float	0.99	The discount factor in the RL problem
--corner-prediction-loss-coef	Float	0.001	The weight of the cloth corner predction loss when updating gradients
--save-images-every-epoch	Int	10	How often should evaluation images from the RL environment be saveed during training
--fc-layer-size	Int	512	The fully-connected layer size to use in the policy and value networks
--fc-layer-depth	Int	5	The fully-connected layer depth to use in the policy and value networks
--her-percent	Float	0.8	Percentage of training samples whose goal should be recalculated using HER
--buffer-size	Int	100000	The maximum number of steps to store in the replay buffer
--num-demoers	Int	0	How many of the parallel RL environments should use an agent only executing a demonstration with added noise
--max-path-length	Int	50	The maximum number of steps the agent can take in the environment before it resets
--max-close-steps	Int	10	The maximum number of steps the task can be considered successful before the environment resets

Misc

• The ./data folder contains a file demos.csv that is the real-world demonstration that is used along with random noise during training
• The ./data folder also contains a file model_params.csv that contains the cloth dynamics parameters that are used during dynamics randomization