Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias
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.
In this paper a new dataset for robot grasping task is proposed. Compared to grasping data collected in a lab environment, the authors propose to collect the data from real world environments (homes). To collect data in the wild, the authors propose to use cheap robots (measured by the $ cost) with low DoF. In order to compensate the noisy behavior of the less calibrated robots, the authors model the noise as a latent variable and jointly learn it with the grasping task. Results show that the combination of the aforementioned ideas result in a robot grasping model that can work well on both lab environments, and new real world environment.  Pros: 1. I really like the motivation of the paper as it is, to the best of my knowledge, the first one to emphasize the following two very important perspectives in robot learning: 1. how do we develop and use cheap robots with less calibrated mechanical details (sensors, actuators, hardware wear-out etc.) 2. how we can extend robots to real world, so we can learn much richer representations. The paper does a good job from this perspective and opens up potentially a new research direction. 2. Results are competitive and clearly suggest the efficacy and advantage in generalization when learning in the wild. 3. As the major technical contribution (though the specific tech has been developed and used in [18]), the noise modeling network shows promising results and worth further discussions.  Cons: As probably the first attempt, the paper is still very preliminary. For cheap robots, it is currently measured by the cost. It is okay but it might be even more helpful to set up a standard of system identification for cheap robots, i.e. what are the possible factors that make a robot cheap? It is okay to just model the noise as a simple latent variable in this vision based grasping task, however, considering harder control tasks, or with a RL approach, we might need to have a better idea on what could possibly go wrong. One interesting thing to try is to see how cheap robots perform after a long period of working time without human recalibration, and see if the robust learning algorithm can handle that to some extent. Of course this is beyond the scope of this paper.  For real world environments, what are the major difference between different homes? Is it just different background image for grasping (floor/carpet?). Do you deploy the robots on places of different physical properties as well, e.g. on a table, on the bed, or in a bathtub?  The thing really worries me is the experimental evaluations. I have the feeling that the gap between Lab vs Home can be easily reduced by simple data augmentation. It is mentioned in the paper that in the lab environments, people usually only care about variations of objects for grasping. And I think the major difference of doing this at real homes (despite the issue of cheap robots etc), is adding a new data augmentation dimension on the grasping background. One experiment I could think of is, under lab environment, using different carpet on the workstation as the "background" of the grasping task. I would imagine this will completely match the performance of home collected data.  From the algorithm perspective, since it is basically a vision task (no need to worry about, say rl policy adaptation) simple domain adaptation could help reduce the gap too. I might be careless but I'm wondering what's the justification of no finetuning experiment for the experimental evaluation (learning on lab data, and finetune on the real world data for a small number of epochs).  Overall, this is an interesting paper. We are far from solving the sim2real transfer problem, but it is a good to think about "lab2real" transfer problem and this paper is, though not perfect, a good initial attempt.