Efficient Data Collection for Robotic Manipulation via Compositional Generalization
(2403.05110)Abstract
Data collection has become an increasingly important problem in robotic manipulation, yet there still lacks much understanding of how to effectively collect data to facilitate broad generalization. Recent works on large-scale robotic data collection typically vary a wide range of environmental factors during data collection, such as object types and table textures. While these works attempt to cover a diverse variety of scenarios, they do not explicitly account for the possible compositional abilities of policies trained on the data. If robot policies are able to compose different environmental factors of variation (e.g., object types, table heights) from their training data to succeed when encountering unseen factor combinations, then we can exploit this to avoid collecting data for situations that composition would address. To investigate this possibility, we conduct thorough empirical studies both in simulation and on a real robot that compare data collection strategies and assess whether visual imitation learning policies can compose environmental factors. We find that policies do exhibit composition, although leveraging prior robotic datasets is critical for this on a real robot. We use these insights to provide better practices for in-domain data collection by proposing data collection strategies that exploit composition, which can induce better generalization than naive approaches for the same amount of effort during data collection. We further demonstrate that a real robot policy trained on data from such a strategy achieves a success rate of 77.5% when transferred to entirely new environments that encompass unseen combinations of environmental factors, whereas policies trained using data collected without accounting for environmental variation fail to transfer effectively, with a success rate of only 2.5%. We provide videos at http://iliad.stanford.edu/robot-data-comp/.
Overview
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The paper addresses the challenge of collecting diverse and comprehensive datasets for robotic manipulation by exploring compositional generalization.
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It involves an empirical study on how visual imitation learning policies can generalize across different environmental factors.
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Several data collection strategies are proposed, with the Stair strategy being identified as particularly effective in optimizing data collection effort.
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The study underscores the importance of leveraging prior datasets and suggests that strategic data collection can enhance robot training efficiency and robustness.
Efficient Data Collection for Robotic Manipulation via Compositional Generalization
Introduction to the Study
In the domain of robotic manipulation, the collection of diverse and comprehensive datasets is crucial for training models that generalize well to a variety of conditions. However, the task of data collection often faces practical constraints, such as limited resources and the sheer combinatorial explosion of potential environmental conditions robots may encounter. Jensen Gao et al.'s work "Efficient Data Collection for Robotic Manipulation via Compositional Generalization" addresses this challenge by exploring whether robots can compose knowledge about different environmental factors (such as object types and table heights) to deal with previously unseen combinations of these factors, thus potentially reducing the need for exhaustive data collection.
Investigating Compositional Generalization
The authors embark on a comprehensive study to understand the extent to which visual imitation learning policies can generalize compositionally. They differentiate between various data collection strategies, ranging from collecting diverse data regarding all possible combinations of environmental factors to focusing on individual factor values and relying on the model's ability to compose these learned aspects to generalize to new conditions.
A significant contribution of this work is the empirical evaluation of these strategies both in simulation and on a real robot setup. The study notably focuses on a set of environmental factors relevant to robotic manipulation, including object position, type, table texture, height, camera position, and the presence of distractor objects. The findings reveal that policies do exhibit compositional generalization to an extent. Crucially, the ability to leverage prior robotic datasets appears indispensable for achieving this on a real robot, highlighting the importance of incorporating existing knowledge to enhance learning efficiency and effectiveness.
Proposed Data Collection Strategies
Gao et al. propose several data collection strategies designed to take advantage of compositional generalization. These include the Stair, L, and Diagonal strategies, each varying in the way they sample environmental factors during data collection to optimally cover the factor space with minimal effort. The study's analyses suggest that Stair generally outperforms other strategies, offering a more balanced approach to inducing better generalization for the same data collection effort.
Implications and Future Directions
The research posits that understanding and exploiting the compositional abilities of robotic policies can significantly streamline the process of collecting in-domain data, making it more feasible to train robots that are robust across diverse environments. This has profound implications for how we approach the training of robots, emphasizing the strategic collection of data over the sheer volume.
Looking ahead, the work underscores a series of avenues for further exploration. Questions about the scalability of these insights to more complex tasks and robot platforms, the effectiveness of different data collection strategies at a larger scale, and the potential of other policy learning methods for compositional generalization are all deserving of attention.
Moreover, the study elucidates the critical role played by prior robotic datasets in facilitating compositional generalization, hinting at the value of creating and sharing large, diverse datasets within the robotics community. Such collaborative efforts could accelerate progress in the field, moving us towards the goal of creating robots capable of sophisticated adaptation and learning in the face of the myriad scenarios they will inevitably face.
In conclusion, "Efficient Data Collection for Robotic Manipulation via Compositional Generalization" offers valuable insights into a more strategic approach to data collection in robotics. By leveraging the compositional generalization capabilities of imitation learning policies and the collective knowledge encapsulated in prior datasets, the work provides a roadmap for enhancing the generalization of robot behaviors in an efficient and effective manner.
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