SPREAD: Subspace Representation Distillation for Lifelong Imitation Learning
arXiv:2603.08763v1 Announce Type: new Abstract: A key challenge in lifelong imitation learning (LIL) is enabling agents to acquire new skills from expert demonstrations while retaining prior knowledge. This requires preserving the low-dimensional manifolds and geometric structures that underlie task representations across sequential learning. Existing distillation methods, which rely on L2-norm feature matching in raw feature space, are sensitive to noise and high-dimensional variability, often failing to preserve intrinsic task manifolds. To address this, we introduce SPREAD, a geometry-preserving framework that employs singular value decomposition (SVD) to align policy representations across tasks within low-rank subspaces. This alignment maintains the underlying geometry of multimodal features, facilitating stable transfer, robustness, and generalization. Additionally, we propose a confidence-guided distillation strategy that applies a Kullback-Leibler divergence loss restricted to
arXiv:2603.08763v1 Announce Type: new Abstract: A key challenge in lifelong imitation learning (LIL) is enabling agents to acquire new skills from expert demonstrations while retaining prior knowledge. This requires preserving the low-dimensional manifolds and geometric structures that underlie task representations across sequential learning. Existing distillation methods, which rely on L2-norm feature matching in raw feature space, are sensitive to noise and high-dimensional variability, often failing to preserve intrinsic task manifolds. To address this, we introduce SPREAD, a geometry-preserving framework that employs singular value decomposition (SVD) to align policy representations across tasks within low-rank subspaces. This alignment maintains the underlying geometry of multimodal features, facilitating stable transfer, robustness, and generalization. Additionally, we propose a confidence-guided distillation strategy that applies a Kullback-Leibler divergence loss restricted to the top-M most confident action samples, emphasizing reliable modes and improving optimization stability. Experiments on the LIBERO, lifelong imitation learning benchmark, show that SPREAD substantially improves knowledge transfer, mitigates catastrophic forgetting, and achieves state-of-the-art performance.
Executive Summary
This article introduces SPREAD, a novel framework for lifelong imitation learning (LIL) that addresses the challenge of preserving task representations across sequential learning. By employing singular value decomposition (SVD) to align policy representations within low-rank subspaces, SPREAD maintains the underlying geometry of multimodal features, facilitating stable transfer, robustness, and generalization. The proposed confidence-guided distillation strategy further improves optimization stability by focusing on reliable modes. Experimental results on the LIBERO benchmark demonstrate SPREAD's effectiveness in improving knowledge transfer, mitigating catastrophic forgetting, and achieving state-of-the-art performance. The proposed framework has the potential to advance LIL applications in various fields, including robotics, autonomous vehicles, and healthcare. However, further investigation is needed to explore its scalability and generalizability to more complex tasks and domains.
Key Points
- ▸ Introduces SPREAD, a geometry-preserving framework for LIL
- ▸ Employs SVD to align policy representations within low-rank subspaces
- ▸ Proposes confidence-guided distillation strategy to improve optimization stability
Merits
Strength in Task Representation Preservation
SPREAD effectively preserves the underlying geometry of multimodal features, maintaining the intrinsic task manifolds across sequential learning.
Improved Knowledge Transfer and Stability
The proposed framework facilitates stable transfer, robustness, and generalization, achieving state-of-the-art performance on the LIBERO benchmark.
Demerits
Limited Scalability and Generalizability
Further investigation is needed to explore SPREAD's scalability and generalizability to more complex tasks and domains, particularly in high-dimensional spaces.
Potential Overreliance on SVD
The framework's performance may be sensitive to the choice of SVD parameters, which could impact its robustness and generalizability.
Expert Commentary
The introduction of SPREAD is a significant contribution to the field of lifelong imitation learning. By addressing the challenge of preserving task representations across sequential learning, SPREAD has the potential to advance LIL applications in various fields. However, further investigation is needed to explore its scalability and generalizability to more complex tasks and domains. The framework's geometry-preserving approach has implications for multimodal learning, and its ability to mitigate catastrophic forgetting raises important policy implications for lifelong learning in dynamic environments. Overall, SPREAD is a promising framework that warrants further exploration and development.
Recommendations
- ✓ Further investigation is needed to explore SPREAD's scalability and generalizability to more complex tasks and domains.
- ✓ The use of SVD as a core component of the framework should be carefully evaluated and refined to ensure robustness and generalizability.
