Belangrijkste concepten
FLOWRETRIEVAL improves few-shot imitation learning in robotics by retrieving motion-similar data from prior datasets using optical flow representations, leading to more efficient policy learning compared to methods relying solely on visual or semantic similarity.
Samenvatting
FLOWRETRIEVAL: Flow-Guided Data Retrieval for Few-Shot Imitation Learning (Research Paper Summary)
Bibliographic Information: Lin, L.-H., Cui, Y., Xie, A., Hua, T., & Sadigh, D. (2024). FLOWRETRIEVAL: Flow-Guided Data Retrieval for Few-Shot Imitation Learning. arXiv preprint arXiv:2408.16944v2.
Research Objective: This paper investigates how to leverage motion similarity in prior datasets to improve few-shot imitation learning for robotics, addressing the limitations of existing retrieval methods that rely heavily on visual or semantic similarity.
Methodology: The authors propose FLOWRETRIEVAL, a three-stage approach:
- Motion-Centric Pretraining: A variational autoencoder (VAE) is trained on optical flow data computed from prior datasets to learn a motion-centric latent space.
- Data Retrieval: Similarity scores are calculated by measuring distances between optical flow embeddings of target task data and prior data in the learned latent space. The most similar data points from the prior dataset are retrieved based on these scores.
- Flow-Guided Learning: The policy network is trained using a combination of target task data and retrieved data, incorporating an auxiliary loss for predicting optical flow to encourage motion-centric representation learning.
Key Findings:
- FLOWRETRIEVAL outperforms baseline methods, including those using visual or semantic similarity for retrieval, in simulated and real-world robotic manipulation tasks.
- The method achieves a higher success rate in few-shot learning scenarios compared to baselines, demonstrating the effectiveness of leveraging motion similarity for data retrieval.
- Qualitative analysis shows that FLOWRETRIEVAL successfully retrieves data with similar motion patterns even when visual appearances differ significantly.
Main Conclusions: FLOWRETRIEVAL offers a promising approach for improving data efficiency in imitation learning by effectively identifying and utilizing motion-similar data from prior experiences, even when those experiences are visually dissimilar to the target task.
Significance: This research contributes to the field of robot learning by addressing the challenge of data scarcity in imitation learning. The proposed method enables robots to learn new tasks more efficiently by leveraging previously acquired knowledge in the form of motion patterns.
Limitations and Future Research:
- The computational cost of processing large prior datasets for retrieval can be high. Future work could explore more efficient retrieval strategies.
- The optimal retrieval threshold is task-dependent and currently requires manual tuning. Automated methods for determining this threshold would be beneficial.
Statistieken
FLOWRETRIEVAL achieves an average of 14% higher success rate than the best baseline method across different tasks (+10% in simulation, +19% in real).
FLOWRETRIEVAL achieves on average 27% higher success rate than the best prior retrieval method.
In the Franka-Pen-in-Cup task, FLOWRETRIEVAL achieves 3.7× the performance of the imitation baseline, learning from all prior and target data.
Citaten
"Our key insight is that prior datasets can serve a broader purpose than merely retrieving the same skills of visually similar states."
"Target task data may in fact exhibit similarities to prior data in terms of low-level motion, offering an opportunity for knowledge transfer of motions."
"FLOWRETRIEVAL, instead attempts to use these intermediate representations for retrieval enabling a more policy-agnostic approach when tapping into prior data."