SSAM_ForcePrediction

This is a Tensorflow implementation for Inferring Force Estimation without Haptic Sensor. This repository includes the implementation of CNN-LSTM Baseline module. (https://arxiv.org/pdf/1811.07190.pdf)

Abstract

Humans can approximately infer the force of interaction between objects using only visual information because we have learned it through experiences. Based on this idea, in this paper, we propose a method based on a recurrent convolutional neural network that uses sequential images to infer the interaction force without using a haptic sensor To train and validate deep learning methods, we collected a large number of images and corresponding data concerning the interaction forces between objects shown therein through an electronic motor-based device. To focus on the changing appearances of a target object owing to external force in the images, we develop a sequential image-based attention module that learns a salient model from temporal dynamics for predicting unknown interaction forces. We propose a sequential image-based spatial attention module and a sequential image-based channel attention module, which are extended to exploit multiple images based on corresponding weighted average pooling layers. Extensive experimental results verified that the proposed method can successfully infer interaction forces in various conditions featuring different target materials, changes in illumination, and directions of external forces.

Prerequisites

Python 3.x
TensorFlow 1.1
tflearn

Prepare Data set

Download DB

To train and evaluate model, We provide a database composed of sequential images and force predictions for interaction force estimation https://github.com/hyeon-jo/Interaction-force-estimation-based-on-deep-learning

TF-Record

In order to optimize the step of loading files in training and evaluation, we should convert the dataset (sequential video images) into TF-Records. https://www.tensorflow.org/tutorials/load_data/tf_records

Train a Model

You can find example of training script in train_queue.py.

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python train_queue.py

Some critical arguments (config.py):

BATCH_SIZE: train batch size.
TRAIN_LEARNING_RATE: train learning rate.
MODEL_OPTIMIZER: train optimizer parameter.
MODEL_NETWORK: The CNN_architecture constucted in nets/cnn.py.

Pretrained-model

https://drive.google.com/file/d/1vqSJz6inKEqx4eOAJNiXBglMTVJZpYo9/view?usp=sharing

Evaluate a Model

Below script gives you an example of evaluating a model with CNN_LSTM after training.

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python eval.py

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