Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias

TL;DR

This paper introduces a large, real-world dataset for robotic grasping in homes, along with a noise-aware learning framework, significantly improving grasp success rates in unstructured environments compared to lab-trained models.

Contribution

It presents the first large-scale home environment dataset for robotic grasping and a novel noise modeling framework to improve learning from low-cost, noisy data.

Findings

Models trained on the home dataset improved grasp success by 43.7% over lab data.

Factoring out dataset noise increased model performance by 10%.

The approach enables robots to better handle unstructured, real-world home environments.

Abstract

Data-driven approaches to solving robotic tasks have gained a lot of traction in recent years. However, most existing policies are trained on large-scale datasets collected in curated lab settings. If we aim to deploy these models in unstructured visual environments like people's homes, they will be unable to cope with the mismatch in data distribution. In such light, we present the first systematic effort in collecting a large dataset for robotic grasping in homes. First, to scale and parallelize data collection, we built a low cost mobile manipulator assembled for under 3K USD. Second, data collected using low cost robots suffer from noisy labels due to imperfect execution and calibration errors. To handle this, we develop a framework which factors out the noise as a latent variable. Our model is trained on 28K grasps collected in several houses under an array of different environmental conditions. We evaluate our models by physically executing grasps on a collection of novel objects in multiple unseen homes. The models trained with our home dataset showed a marked improvement of 43.7% over a baseline model trained with data collected in lab. Our architecture which explicitly models the latent noise in the dataset also performed 10% better than one that did not factor out the noise. We hope this effort inspires the robotics community to look outside the lab and embrace learning based approaches to handle inaccurate cheap robots.