Learning New Tasks via Reusable Skills: Skill-Compositional Experts for Embodied Continual Learning
Summary
The Skill-Compositional Experts (SCE) framework is proposed to address catastrophic forgetting and feature drift in Embodied Continual Learning (ECL) for robots. ECL aims for robots to continually acquire new manipulation tasks while retaining prior behaviors under closed-loop control, but struggles with structured skill reuse. SCE tackles this by first employing Compositional Skill Grounding (CSG) to decompose task demonstrations into reusable skills, forming a skill base. Subsequently, Dual Execution-and-Transition Experts (DETE) facilitate new task learning through skill composition, with distinct branches managing skill execution and supporting transitions between skills for coherent behavior. Experiments on LIBERO benchmarks and real-world manipulation tasks demonstrate that SCE consistently improves both retention of learned behaviors and overall task performance. The framework was published on 2026-06-14.
Key takeaway
For robotics engineers designing embodied continual learning systems, SCE provides a structured framework to overcome catastrophic forgetting. You should consider implementing skill decomposition via Compositional Skill Grounding to create reusable skill bases. This approach, combined with Dual Execution-and-Transition Experts for managing skill execution and transitions, can significantly improve retention and overall task performance in new manipulation task acquisition. Evaluate its effectiveness on your specific real-world manipulation tasks.
Key insights
SCE enables robots to learn new tasks continually by composing reusable skills, mitigating catastrophic forgetting in embodied learning.
Principles
- Decompose complex tasks into reusable skills.
- Explicitly manage skill execution and transitions.
- Structured skill reuse improves retention.
Method
SCE uses Compositional Skill Grounding (CSG) to build a skill base from demonstrations, then Dual Execution-and-Transition Experts (DETE) for new task learning via skill composition.
In practice
- Apply CSG to break down robot manipulation tasks.
- Implement DETE for robust skill sequencing.
- Test on LIBERO benchmarks for validation.
Topics
- Embodied Continual Learning
- Skill Composition
- Catastrophic Forgetting
- Robot Manipulation
- Dual Execution-and-Transition Experts
- LIBERO Benchmarks
Best for: Research Scientist, AI Scientist, Robotics Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.