Sequential Learning of Movement Prediction in Dynamic Environments using LSTM Autoencoder

TL;DR

This paper introduces an LSTM autoencoder that predicts future frames in dynamic environments, aiding robotic navigation amidst moving obstacles by combining feature extraction and sequence modeling.

Contribution

The paper presents a novel LSTM autoencoder architecture conditioned on actions for predicting future frames in dynamic scenes for robotic navigation.

Findings

Predicted frames are accurate enough for navigation tasks.

The approach effectively models dynamic obstacle movements.

Promising results in a simulated environment for future reinforcement learning integration.

Abstract

Predicting movement of objects while the action of learning agent interacts with the dynamics of the scene still remains a key challenge in robotics. We propose a multi-layer Long Short Term Memory (LSTM) autoendocer network that predicts future frames for a robot navigating in a dynamic environment with moving obstacles. The autoencoder network is composed of a state and action conditioned decoder network that reconstructs the future frames of video, conditioned on the action taken by the agent. The input image frames are first transformed into low dimensional feature vectors with a pre-trained encoder network and then reconstructed with the LSTM autoencoder network to generate the future frames. A virtual environment, based on the OpenAi-Gym framework for robotics, is used to gather training data and test the proposed network. The initial experiments show promising results indicating that these predicted frames can be used by an appropriate reinforcement learning framework in future to navigate around dynamic obstacles.