Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias (1807.07049v1)

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.

• The paper’s main contribution is creating a 28,000-instance home grasp dataset that boosts generalization by 43.7% compared to lab data.
• It employs a low-cost mobile manipulator, costing under $3,000, to collect diverse, noisy data from real-world home environments.
• The novel two-network system models noise as a latent variable, establishing a robust framework for real-world robotic learning.

Overview of Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias

The research paper titled "Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias" addresses significant challenges in the domain of data-driven approaches to robotic learning, especially in unstructured environments such as home settings. The paper highlights the limitations of conventional datasets collected in laboratory settings and emphasizes the potential benefits of gathering data from real-world environments to enhance the generalization of robotic grasping tasks.

Core Contributions

The paper’s primary contribution is the creation of a large-scale dataset of robotic grasping instances gathered from home environments. This dataset, comprised of 28,000 grasps, was collected using a custom-built low-cost mobile manipulator designed to operate in variable home settings. Key advancements presented in the paper include:

1. Low-Cost Hardware Implementation: The authors constructed a mobile manipulator for less than 3,000 USD, which facilitated extensive data collection across diverse home environments. The manipulator consists of a Dobot Magician robotic arm with additional customization to enhance functionality for the collection task.
2. Noisy Data Management: Recognizing the inherent inaccuracies in data collected using low-cost robots due to noisy labels resulting from calibration and execution errors, the researchers developed a framework that models these inaccuracies as latent variables. This approach allows the model to factor out noise through a two-network system that differentiates between the grasp prediction and noise modeling.
3. Dataset and Model Evaluation: The dataset was rigorously evaluated against baseline models trained on laboratory data, with the home environment dataset showing a marked improvement in generalization. Grasping models trained on this data exhibited a 43.7% improvement over models trained exclusively on laboratory data. Additionally, the specific model architecture factoring in noise led to further performance gains.

Implications and Speculations

This paper implies significant theoretical and practical advancements for improving robot learning in non-traditional environments. By shifting focus from controlled lab settings to real-world home environments, the research underscores the necessity of diverse datasets to mitigate bias and foster the development of generalizable robotic capabilities.

On a practical level, the paper suggests the feasibility of deploying low-cost robots for effective data collection in household settings, potentially democratizing access to robotic solutions that learn from real-world interactions. The approach of modeling noise as a latent variable also opens avenues for improving training processes across varying fields where data imperfections are prevalent.

From a theoretical perspective, the research advocates for more robust frameworks addressing data distribution mismatches, thus equipping robots with improved adaptability and reliability in unforeseen environments. Future developments may explore enhancements to both hardware and data modeling techniques, leading to broader applicability in home automation and service robotics.

Future Directions

Potential future research directions include refining data collection protocols to better handle the various external factors intrinsic to household environments, advancing noise modeling techniques, and expanding the scope of tasks, beyond grasping, to other manipulative actions. Further exploration into transfer learning and reinforcement learning could augment robotic learning capabilities amidst domain shifts, thereby advancing the trajectory towards more autonomous and versatile home robots.

This research effectively shifts the paradigm in robotic learning by demonstrating both methodology and application that bridge the gap between laboratory precision and real-world complexity, providing a blueprint for subsequent innovations in the field of home robotics.