FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning (2408.16944v2)

Abstract: Few-shot imitation learning relies on only a small amount of task-specific demonstrations to efficiently adapt a policy for a given downstream tasks. Retrieval-based methods come with a promise of retrieving relevant past experiences to augment this target data when learning policies. However, existing data retrieval methods fall under two extremes: they either rely on the existence of exact behaviors with visually similar scenes in the prior data, which is impractical to assume; or they retrieve based on semantic similarity of high-level language descriptions of the task, which might not be that informative about the shared low-level behaviors or motions across tasks that is often a more important factor for retrieving relevant data for policy learning. In this work, we investigate how we can leverage motion similarity in the vast amount of cross-task data to improve few-shot imitation learning of the target task. Our key insight is that motion-similar data carries rich information about the effects of actions and object interactions that can be leveraged during few-shot adaptation. We propose FlowRetrieval, an approach that leverages optical flow representations for both extracting similar motions to target tasks from prior data, and for guiding learning of a policy that can maximally benefit from such data. Our results show FlowRetrieval significantly outperforms prior methods across simulated and real-world domains, achieving on average 27% higher success rate than the best retrieval-based prior method. In the Pen-in-Cup task with a real Franka Emika robot, FlowRetrieval achieves 3.7x the performance of the baseline imitation learning technique that learns from all prior and target data. Website: https://flow-retrieval.github.io

• The paper introduces a novel optical flow-based data retrieval method that enhances few-shot imitation learning by focusing on motion similarity.
• It decouples data retrieval from policy learning, achieving a 27% average success rate gain in both simulated and real-world robotic experiments.
• The approach enables robots to efficiently adapt to new tasks with minimal demonstrations by retrieving relevant past experiences based on motion features.

Overview of FlowRetrieval for Few-Shot Imitation Learning

The paper "FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation Learning" by Lin et al. presents an approach to optimize few-shot imitation learning by leveraging optical flow for data retrieval. The challenge addressed is the dependency of existing data retrieval methods on either high-level visual or semantic similarities, limiting their effectiveness across varied tasks. The proposed solution, FlowRetrieval, introduces a method to extract motion-similar data from a vast corpus of prior experiences, thereby enhancing the learning efficiency in few-shot scenarios.

Core Contributions

1. Motion-Centric Retrieval Mechanism:
   • FlowRetrieval utilizes optical flow, representing pixel-level movement between frames, to create a motion-centric latent space. This space is generated using a Variational Autoencoder (VAE), which helps in identifying data from prior tasks that exhibit similar motion patterns to the target task.
2. Robust Performance Metrics:
   • The method shows significant improvements over previous retrieval-based methods, with an average success rate gain of 27%. In real-world experiments involving a Franka Emika robot, FlowRetrieval achieved 3.7 times better performance compared to baseline imitation learning techniques.
3. Decoupled Problem Formulation:
   • The research splits the problem into two distinct components: data retrieval based on motion similarity and policy learning using the retrieved data. This decoupling allows for independent optimization and evaluation of the retrieval process, ensuring that the policy learning benefits maximally from the most relevant prior data.

Experimental Validation

The paper validates FlowRetrieval through comprehensive experiments:

1. Simulated Tasks:
   • Square Assembly: The task involves a robot assembling a square peg, with the primary challenge being the action selection due to varying backgrounds and visually different scenes. FlowRetrieval successfully distinguished between useful and adversarial demonstrations, retrieving relevant motion data despite different visual contexts.
   • LIBERO-Can: This pick-and-place task tests the model with a diverse dataset, again demonstrating FlowRetrieval's ability to focus on motion similarities rather than visual features alone.
2. Real-World Tasks:
   • Bridge-Pot and Bridge-Microwave: Using a ViperX robotic arm, the experiments on transferring a pot to a sink and opening a microwave door highlight the practical applicability of the method in operational settings.
   • Franka-Pen-in-Cup: Evaluated with a Franka Emika robot, this experiment underscores the efficacy of FlowRetrieval in filtering useful data even from vastly heterogeneous prior datasets like DROID.

Key Insights and Implications

1. Optical Flow as a Robust Representation:
   • Optical flow effectively encapsulates low-level motion details, providing a more granular measure of similarity which is crucial for tasks requiring precise manipulations. By using optical flow for creating a motion-centric latent space, FlowRetrieval ensures the retrieval of action sequences that are inherently similar in their mechanical execution.
2. Auxiliary Loss in Policy Learning:
   • Integrating an auxiliary loss for optical flow prediction during policy learning ensures that the policy network remains focused on motion-relevant features, further enhancing the learning process.
3. Practical Implications:
   • The ability to retrieve data based on motion rather than semantic or visual similarity has profound implications for robotic learning. Real-world deployment of robots in varied environments can significantly benefit from this approach, allowing robots to adapt to new tasks quickly with minimal demonstrations.
4. Theoretical Contributions:
   • The decoupled problem formulation not only streamlines the data retrieval process but also allows future research to focus independently on improving either retrieval algorithms or policy learning mechanisms. This modular approach can foster advancements in both retrieval methodologies and imitation learning frameworks.

Future Directions

While FlowRetrieval shows considerable promise, the paper also identifies potential areas for further research:

• Scalability Enhancements: Addressing the computational overheads associated with the pairwise distance computations required during retrieval.
• Automating Threshold Selection: Developing adaptive methods to automatically determine the optimal retrieval threshold for various tasks, potentially through active learning strategies.
• Extended Experiments: Conducting more extensive validations across a broader range of tasks and robotic systems to further establish the robustness and generalizability of FlowRetrieval.

In conclusion, FlowRetrieval represents a significant advance in the field of few-shot imitation learning by introducing a novel mechanism to leverage motion similarity for data retrieval. Its strong empirical performance across a variety of tasks underscores its potential for real-world robotic applications, setting a foundation for further research and development in efficient robotic learning paradigms.