Lifelong Language-Conditioned Robotic Manipulation Learning

TL;DR

This paper proposes SkillsCrafter, a novel robotic manipulation framework designed to continually learn multiple skills while reducing catastrophic forgetting of old skills, and proposes a Manipulation Skills Adaptation to retain the old skills knowledge while inheriting the shared knowledge between new and old skills to facilitate learning of new skills.

Abstract

Traditional language-conditioned manipulation agent sequential adaptation to new manipulation skills leads to catastrophic forgetting of old skills, limiting dynamic scene practical deployment. In this paper, we propose SkillsCrafter, a novel robotic manipulation framework designed to continually learn multiple skills while reducing catastrophic forgetting of old skills. Specifically, we propose a Manipulation Skills Adaptation to retain the old skills knowledge while inheriting the shared knowledge between new and old skills to facilitate learning of new skills. Meanwhile, we perform the singular value decomposition on the diverse skill instructions to obtain common skill semantic subspace projection matrices, thereby recording the essential semantic space of skills. To achieve forget-less and generalization manipulation, we propose a Skills Specialization Aggregation to compute inter-skills similarity in skill semantic subspaces, achieving aggregation of the previously learned skill knowledge for any new or unknown skill. Extensive experiments demonstrate the effectiveness and superiority of our proposed SkillsCrafter.

Figures (7)

• Figure 1: The proposed Lifelong Robotic Manipulation task. (a) SkillsCrafter is able to evolve and learn new skills based on learned skills. It maintains a skill knowledge base that stores learned knowledge to allow for efficient learning of new skills using shared knowledge of old skills, and for performing any unknown skill in the open world using previously learned knowledge. (b) Skills catastrophic forgetting under lifelong learning settings. (c) Our SkillsCrafter has a better anti-forgetting performance.
• Figure 2: Illustration of the three sets of observation. (a) Subspaces Consistency: The semantic space can be used to associate with the parameter subspace. (b) Different Layers across Various Skills: The importance of different layers is not the same for different skills. (c) Knowledge Decoupled: $\textbf{A}$ tends to learn shared knowledge, while $\textbf{B}$ to learn specific knowledge, naturally.
• Figure 3: Illustration of the proposed SkillsCrafter pipeline. It includes (a) a Manipulation Skills Adaptation to achieve retaining the old skills knowledge while inheriting the shared knowledge between new and old skills to facilitate learning of new skills; (b) a Skills Specialization Aggregation to compute inter-skills similarity in skill-specific subspaces to achieve adaptive aggregation of skills knowledge; (c) a Skills Specified Inference to loads aggregated knowledge to achieve any skill manipulation inference.
• Figure 4: Illustration of the experiment robotic skill tasks setting. We establish a total of 12 robotic simulator skills and 6 real-environment robotic skills for incremental learning.
• Figure 5: Illustration for some visualization examples.