Data Efficient Behavior Cloning for Fine Manipulation via Continuity-based Corrective Labels
(2405.19307)Abstract
We consider imitation learning with access only to expert demonstrations, whose real-world application is often limited by covariate shift due to compounding errors during execution. We investigate the effectiveness of the Continuity-based Corrective Labels for Imitation Learning (CCIL) framework in mitigating this issue for real-world fine manipulation tasks. CCIL generates corrective labels by learning a locally continuous dynamics model from demonstrations to guide the agent back toward expert states. Through extensive experiments on peg insertion and fine grasping, we provide the first empirical validation that CCIL can significantly improve imitation learning performance despite discontinuities present in contact-rich manipulation. We find that: (1) real-world manipulation exhibits sufficient local smoothness to apply CCIL, (2) generated corrective labels are most beneficial in low-data regimes, and (3) label filtering based on estimated dynamics model error enables performance gains. To effectively apply CCIL to robotic domains, we offer a practical instantiation of the framework and insights into design choices and hyperparameter selection. Our work demonstrates CCIL's practicality for alleviating compounding errors in imitation learning on physical robots.
Overview
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The paper introduces the Continuity-based Corrective Labels for Imitation Learning (CCIL) framework, which aims to mitigate compounding errors in behavior cloning for fine manipulation tasks in robotics.
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The methodology involves using locally continuous dynamics models to generate corrective labels that guide agents back to expert states, particularly useful in scenarios with limited demonstration data.
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Key findings show that CCIL significantly improves success rates in complex tasks like grasping, gear insertion, and coin manipulation, especially in low-data regimes, and emphasizes the importance of filtering high-error corrective labels for better policy performance.
Data Efficient Behavior Cloning for Fine Manipulation via Continuity-based Corrective Labels
Overview
The paper "Data Efficient Behavior Cloning for Fine Manipulation via Continuity-based Corrective Labels" investigates the use of the Continuity-based Corrective Labels for Imitation Learning (CCIL) framework for improving the performance of behavior cloning (BC) agents in real-world fine manipulation tasks. The primary focus is on addressing the compounding errors due to covariate shift that often limit the effectiveness of imitation learning applications. This work is particularly significant in robotics domains where demonstration data is limited, and system dynamics are complex due to contacts and interactions.
Methodology
The CCIL framework augments the training data of imitation learning agents by generating corrective labels. These labels are derived by learning a locally continuous dynamics model from the expert demonstration data. Through a carefully constructed algorithm, the framework aims to guide the agent back to expert states, thus reducing the compounding errors that occur when executing learned policies. This research offers a detailed methodology on how to practically apply CCIL, including insights into design choices and parameter tuning, which are often necessary for successful real-world deployment.
Key Findings
- Local Lipschitz Continuity: The study provides empirical evidence that real-world fine manipulation tasks, which often involve discontinuities due to physical contacts, exhibit sufficient local Lipschitz continuity. This supports the foundational assumption of CCIL that locally continuous models can be learned effectively from demonstration data.
- Effectiveness in Low-Data Regimes: One of the significant contributions of this paper is the demonstration that CCIL is particularly effective in low-data regimes. Empirical results show that the generated corrective labels significantly improve the success rates of imitation learning agents across various complex tasks, including grasping, gear insertion, and coin manipulation. For example, in the GraspCube task, the success rate increased from 23% to 83% with the incorporation of CCIL-generated labels.
- Label Quality and Filtering: The study highlights the importance of filtering generated labels based on their error bounds to ensure high-quality corrective labels are used in training. Labels generated during states with significant contact interactions exhibited higher errors, and filtering these out led to better overall performance of the policies. The practical instantiation of CCIL includes a method for dynamically computing and applying label rejection thresholds.
Practical Implications
The research has several practical implications for the field of robotics and imitation learning:
- Robust Policy Training: By leveraging locally continuous dynamics models, CCIL provides a way to train more robust policies that can handle real-world complexities, particularly when only a limited amount of demonstration data is available.
- Efficient Data Use: The framework demonstrates how to maximize the utility of available demonstration data through synthetic data augmentation, making it feasible to deploy imitation learning in data-scarce scenarios.
- Guidelines for Deployment: The authors provide detailed guidelines on selecting appropriate hyperparameters and making design choices, which can help practitioners implement CCIL in various robotic applications without extensive trial and error.
Future Developments
The promising results of this study open the door to several future research directions:
- High-Dimensional State Spaces: Extending CCIL to handle high-dimensional state spaces, such as image-based inputs, could significantly broaden its applicability, especially in tasks where visual feedback is critical.
- Integration with Other Augmentation Methods: Exploring how CCIL can be combined with other data augmentation and reinforcement learning methods could lead to even more robust and generalizable policies.
- Variety of Policy Classes: Investigating the impact of CCIL on different classes of policies, including those based on diffusion models, could provide further insights into its versatility and effectiveness.
Conclusion
The paper "Data Efficient Behavior Cloning for Fine Manipulation via Continuity-based Corrective Labels" makes a substantial contribution to the field by demonstrating that CCIL can effectively augment imitation learning with minimal assumptions. The framework's ability to improve policy performance in complex, real-world tasks with limited data availability showcases its potential for practical deployments in diverse robotic applications. The empirical validation and practical guidelines provided form a robust foundation for future research and development in imitation learning and robot autonomy.
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