SkillMemo: Expert-Guided Skill Memory for Robotic Manipulation
A novel framework named Skill-Based Memory (SkillMemo) has been introduced to overcome the challenges faced by embodied visuomotor models in achieving compositional generalization. This framework is elaborated in an arXiv paper (2608.05970) and aims to tackle the limited availability of extensive embodied trajectory datasets that hinder models such as Diffusion Policy (DP) and Vision-Language-Action (VLA). SkillMemo effectively breaks down lengthy demonstrations into fundamental atomic skills and incorporates skill-level attributes into a flexible episodic memory repository. Designed to enhance out-of-distribution (OOD) performance and capture reusable skill structures, the framework features an expert-guided trajectory segmentation module based on a Mixture-of-Experts (MoE) architecture. This module utilizes learned gating mechanisms to divide trajectories into unique skill primitives. The paper's cross-type submission status on arXiv suggests it may have been presented at a conference or journal, with implications for robotics, artificial intelligence, and embodied AI, particularly in autonomous manipulation tasks.
Key facts
- SkillMemo is a framework for compositional embodied manipulation.
- It addresses limitations of Diffusion Policy (DP) and Vision-Language-Action (VLA) models.
- The framework decomposes long-horizon demonstrations into latent atomic skills.
- It uses a dynamic episodic memory bank to store skill-level features.
- An expert-guided trajectory segmentation module is built on Mixture-of-Experts (MoE) architecture.
- The goal is to improve compositional generalization in out-of-distribution scenarios.
- The paper is available on arXiv with ID 2608.05970.
- The announcement type is 'cross', indicating potential conference submission.
Entities
Institutions
- arXiv