SpeedFolding

Learning Efficient Bimanual Folding of Garments

This repository contains the code for SpeedFolding: Learning Efficient Bimanual Folding of Garments, with the corresponding paper accepted at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) in Kyoto. The code is not meant to be easily runnable, as it depends tightly on the hardware setup including the ABB Yumi robot and a Photoneo PhoXi camera. We recommend to see it as a reference implementation, in particular for our BiMama-Net architecture.

Installation

The SpeedFolding code depends on abb_librws, the SDK for Photoneo PhoXi camera, as well as on the relevant NVIDIA driver and CUDA installation for PyTorch. Further Python (3.6+) dependencies can then be installed via

pip install -r requirements.txt
pip install -e third_party/*

The third_party components, namely an OMPL planner and a PhoXi camera wrapper, are written in C++ and use pybind11 to expose Python funcionality. Before running the scripts, make sure to have both the learning and manipulation directory in your PYTHONPATH.

Structure

The overall structure is as follows:

• /data includes the user-specified instructions for folding garments and calibration data.
• /database contains the backend-server for saving data to our database. Have a look at learning/database.py for information about reading the database.
• /learning is about our introduced BiMama-Net architecture (and everything related to training and running inference with it). It includes scripts for drawing, augmentation, as well as model and reward definitions. inference.py is the main class for calculating predictions and the next action.
• /manipulation includes everything related to the robot and running the overall high-level pipeline. While yumi.py controls the robot itself, experiment.py defines the motions for the manipulation primitives. The heuristics directory includes all calculations of primitives that are not learned, in particular for instruction matching or folding primitives.

Entry Points

1. First, start the database by running uvicorn main:app --app-dir database from the project root directory. When running the Speedfolding setup, it will upload the most recent images to the database as the current image for debugging purposes.
2. To check robot motions or run single manipulation primitives, use manipulation/experiment.py. For example, python manipulation/experiment.py --do-the-fling will fling the garment from pre-defined pick poses. python manipulation/experiment.py --instruction shirt will fold an already smooth shirt according to the defined folding-lines instruction. python manipulation/experiment.py --do-2s-fold will apply the 2 seconds folding heuristic.
3. To run the complete pipeline, use mainpulation/run.py. Most of our experiments were run using the python manipulation/run.py --horizon 10 --demo --fold arguments. To repeat the experiment even after a robot error, swap run.py with run_forever.py keeping the same arguments. The overall end-to-end pipeline depends on a number of hyperparameters that are explained furthermore.

Hyperparameters

Group	Parameter	Commonly used value
Database	URL	http://127.0.0.1:8000
Camera	PhoXi Serial number	1703005
	Extrinsic calibration	data/calibrations/phoxi_to_world_bww.tf
Motion	Speed (Full)	0.6 m/s
	Speed (Half)	0.12 m/s
	Speed (Fling)	1.0 m/s
	Speed (Stretch)	0.06 m/s
	Force Threshold (Stretch)	0.025 N
	Timeout	7 s
Grasping	Inwards distance	0.018 m
	Inwards angle	0.25 rad
	Gripper force	20 N
	Approach distance	0.04 m
Learning	Inference image size	256 x 192 px
	Depth image distances	0.85 - 1.25 m
	Reachability masks	data/masks/{left, right}.npy
	Bimama-Net rotations	20
	Bimama-Net Embedding size	8
	Training epochs	100
	Learning rate	4e-4
	Learning rate exponential decay	0.97

These are some of the parameters. In particular, the parametrization of the mainipulation primitives are too complex to state here. We refer to the manipulation/experiment.py directly.