  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.
