A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies

TL;DR

This paper presents a data-efficient, simulation-based framework for training robot navigation policies that transfer effectively to real environments using meta-learning and adversarial domain adaptation.

Contribution

It introduces a novel planning framework with a gradient-based planner and a meta-learning training strategy, enhanced by adversarial domain transfer for sim-to-real navigation.

Findings

Successful transfer of navigation policies from simulation to real robots

Effective planning with fewer real-world demonstrations

Robust performance across different navigation tasks

Abstract

Learning effective visuomotor policies for robots purely from data is challenging, but also appealing since a learning-based system should not require manual tuning or calibration. In the case of a robot operating in a real environment the training process can be costly, time-consuming, and even dangerous since failures are common at the start of training. For this reason, it is desirable to be able to leverage \textit{simulation} and \textit{off-policy} data to the extent possible to train the robot. In this work, we introduce a robust framework that plans in simulation and transfers well to the real environment. Our model incorporates a gradient-descent based planning module, which, given the initial image and goal image, encodes the images to a lower dimensional latent state and plans a trajectory to reach the goal. The model, consisting of the encoder and planner modules, is trained through a meta-learning strategy in simulation first. We subsequently perform adversarial domain transfer on the encoder by using a bank of unlabelled but random images from the simulation and real environments to enable the encoder to map images from the real and simulated environments to a similarly distributed latent representation. By fine tuning the entire model (encoder + planner) with far fewer real world expert demonstrations, we show successful planning performances in different navigation tasks.