Policy Compatible Skill Incremental Learning via Lazy Learning Interface
SIL-C framework employs bilateral lazy learning to maintain skill-policy compatibility, improving incremental learning with 42.5% FWT.
Key Findings
Methodology
SIL-C introduces a bilateral lazy learning interface that aligns subtask and skill spaces via trajectory distribution similarity. It employs multimodal Gaussian prototypes for instance matching, supporting non-destructive skill updates and skill validation. The framework dynamically remaps misaligned subtasks to suitable skills without retraining policies. Core components include: • Prototype-based trajectory similarity matching; • Skill validation and hooking mechanisms; • Incremental skill and subtask space updates via unsupervised clustering. This design ensures seamless skill evolution while preserving policy compatibility.
Key Results
- In Franka Kitchen and Meta-World environments, SIL-C improves downstream policy performance with an average FWT of 42.5%, surpassing baselines by over 20%. In few-shot imitation learning, performance nearly doubles, demonstrating high sample efficiency and transferability. The method maintains 85% backward and forward skill compatibility, validating its robustness across evolving skills.
- Compared to BUDS, PTGM, and experience replay, SIL-C achieves superior AUC scores, especially in incremental skill scenarios, significantly reducing skill forgetting and policy degradation.
Significance
This work addresses a fundamental challenge in lifelong robotics—how to evolve skills without breaking existing policies. By enabling dynamic alignment and validation, SIL-C facilitates scalable, efficient skill expansion and reuse, advancing autonomous systems' adaptability. It bridges the gap between static pretraining and flexible lifelong learning, promising broader applications in complex, real-world environments.
Technical Contribution
The key technical innovation is integrating trajectory distribution similarity with instance-based lazy learning to enable dynamic skill-policy alignment. The framework supports incremental skill updates without policy re-training, providing theoretical guarantees of compatibility. It also introduces skill validation and hooking mechanisms, enhancing robustness and scalability over existing static or label-dependent methods.
Novelty
This is the first application of bilateral lazy learning for skill-policy compatibility in lifelong learning. Unlike prior static matching or label-based approaches, SIL-C dynamically aligns evolving skills with policies based on trajectory similarity, allowing seamless skill evolution without re-training. Its non-destructive update mechanism and validation process set it apart from traditional methods.
Limitations
- The reliance on trajectory similarity may struggle in high-dimensional or highly complex tasks, leading to potential mismatches.
- In scenarios with rapid skill evolution or highly diverse skills, computational costs increase, impacting real-time performance.
- The current prototype-based approach may face scalability issues with very large skill libraries, requiring further optimization.
Future Work
Future directions include integrating deep neural representations for more robust trajectory similarity measurement, exploring reinforcement learning-based skill hooking strategies, and extending the framework to multi-agent systems. Additionally, optimizing computational efficiency and scaling to real-world robotics remain key challenges.
AI Executive Summary
Lifelong learning in robotics hinges on the ability to continually expand and refine skills without losing compatibility with existing policies. Traditional approaches often rely on static labels or fixed matching schemes, which falter as skills evolve rapidly. This paper introduces SIL-C, a novel framework that leverages bilateral lazy learning to dynamically align subtask and skill spaces via trajectory distribution similarity. The core idea is to treat skill and subtask matching as an instance-based classification problem, enabling seamless skill updates and validation without retraining policies.
The architecture comprises a hierarchical policy structure with a high-level policy generating subtasks, and a low-level skill decoder executing actions. The interface layer employs Gaussian prototypes to compare trajectory similarities, validating and remapping skills as needed. This approach supports incremental skill learning, allowing the skill library to grow while maintaining policy effectiveness. Experiments in Franka Kitchen and Meta-World environments demonstrate that SIL-C outperforms baseline methods, achieving an average FWT of 42.5%, significantly enhancing transferability and robustness.
The significance of this work lies in its ability to facilitate scalable, flexible lifelong learning for autonomous agents. By ensuring skill-policy compatibility through a non-destructive, instance-based matching mechanism, SIL-C addresses a longstanding bottleneck in continual learning. Its capacity to adapt to evolving skills without costly re-training opens new avenues for deploying intelligent robots in dynamic real-world scenarios. Future work aims to incorporate deep trajectory representations and multi-agent extensions, further broadening its applicability and efficiency.
Deep Analysis
Background
The evolution of skill learning in robotics has transitioned from static pretraining to continual, incremental frameworks. Early methods like BUDS and PTGM focused on unsupervised skill discovery and transfer, but faced challenges in maintaining policy compatibility as skills evolved. Hierarchical reinforcement learning approaches introduced structured control but often relied on fixed skill sets and static matching. Recent advances aim to enable lifelong learning, yet many struggle with skill-policy mismatch during dynamic skill updates, leading to performance degradation and increased retraining costs. Addressing these issues requires mechanisms that support seamless skill evolution while preserving existing policy effectiveness, a gap this work aims to fill.
Core Problem
The core challenge is maintaining backward and forward compatibility between evolving skills and downstream policies in lifelong learning settings. As skills are incrementally added or refined, existing policies may become invalid or less effective, necessitating costly re-training. Static matching schemes or predefined labels lack flexibility, especially in complex, high-dimensional tasks. The problem becomes more acute in real-world applications where continuous skill adaptation is essential. Therefore, developing a dynamic, scalable, and non-intrusive alignment mechanism is critical to advance autonomous lifelong learning systems.
Innovation
This work introduces several key innovations: 1) a bilateral lazy learning interface that models subtask and skill spaces via Gaussian prototypes, enabling trajectory-based similarity matching; 2) a non-destructive skill update mechanism that incrementally expands the skill library without retraining policies; 3) a skill validation and hooking system that ensures semantic consistency and remaps misaligned subtasks; 4) leveraging instance-based classification to support scalable, flexible skill-policy alignment. These innovations collectively address the limitations of static matching and static labels, enabling continuous skill evolution with minimal policy disruption.
Methodology
- �� Construct multimodal Gaussian prototypes for subtask and skill spaces based on initial states and subgoals. • Use trajectory distribution similarity to perform instance-based classification, matching subtasks to skills. • Implement a bilateral interface with task-side and skill-side modules that predict subgoals and assess skill executability. • During skill updates, cluster streamed datasets with unsupervised algorithms (e.g., K-means), generate prototypes, and store in skill memory. • For subtask space, generate prototypes from expert demonstrations, cluster subgoals, and update accordingly. • During policy inference, predict subgoals, validate skills via Mahalanobis distance, and remap mismatched subtasks to suitable skills using skill hooking. • Support incremental updates through append-only prototype memories, avoiding policy re-training.
Experiments
采用Frank Kitchen和Meta-World两个模拟环境，设计四个SIL阶段，逐步引入新技能，训练24个下游策略。对比基线包括BUDS、PTGM、经验回放等，评估指标涵盖FWT、BWT和AUC。实验中调节技能更新频率、匹配阈值和技能数目，验证方法的鲁棒性和扩展性。通过不同场景（Emergent和Explicit SIL）测试策略兼容性和迁移能力，确保结果的全面性和可信度。
Results
SIL-C在所有场景中均优于基线，平均FWT达42.5%，比传统方法提升20%以上。在少样本模仿学习中，性能几乎翻倍，显示出极强的样本效率。技能演化过程中，保持85%的Backward和Forward Skill Compatibility指标，验证了其在技能迁移和策略重用方面的优越性。实验还表明，SIL-C能有效减少技能遗忘，提升策略稳定性，特别是在技能快速演变的复杂环境中表现出色。
Applications
该方法适用于自主机器人、智能制造和服务机器人等场景，支持持续学习和技能扩展。无需频繁重训练策略，便于在动态环境中快速适应新任务。未来可结合深度学习实现多模态轨迹表示，推动机器人自主学习体系的规模化和多样化发展，满足工业和服务行业的智能化需求。
Limitations & Outlook
当前方法依赖轨迹分布相似性度量，可能在高维复杂任务中表现不佳，存在匹配误差。技能快速演变或多模态技能场景下，接口的准确性和效率可能受到影响。此外，算法在大规模技能库中计算成本较高，需优化匹配机制以适应复杂应用。未来需解决高维轨迹表示的鲁棒性和匹配效率问题，拓展到多智能体系统和真实环境中。
Plain Language Accessible to non-experts
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Abstract
Skill Incremental Learning (SIL) is the process by which an embodied agent expands and refines its skill set over time by leveraging experience gained through interaction with its environment or by the integration of additional data. SIL facilitates efficient acquisition of hierarchical policies grounded in reusable skills for downstream tasks. However, as the skill repertoire evolves, it can disrupt compatibility with existing skill-based policies, limiting their reusability and generalization. In this work, we propose SIL-C, a novel framework that ensures skill-policy compatibility, allowing improvements in incrementally learned skills to enhance the performance of downstream policies without requiring policy re-training or structural adaptation. SIL-C employs a bilateral lazy learning-based mapping technique to dynamically align the subtask space referenced by policies with the skill space decoded into agent behaviors. This enables each subtask, derived from the policy's decomposition of a complex task, to be executed by selecting an appropriate skill based on trajectory distribution similarity. We evaluate SIL-C across diverse SIL scenarios and demonstrate that it maintains compatibility between evolving skills and downstream policies while ensuring efficiency throughout the learning process.