Lifelong Language-Conditioned Robotic Manipulation Learning
#robotic manipulation #lifelong learning #language-conditioned #AI #machine learning #task adaptation #skill retention
📌 Key Takeaways
- Researchers propose a lifelong learning framework for robots to continuously acquire new manipulation skills through language instructions.
- The system integrates language understanding with robotic control to enable task execution based on verbal commands.
- It addresses the challenge of adapting to new tasks without forgetting previously learned skills, crucial for long-term deployment.
- Experiments demonstrate improved performance in multi-task environments compared to traditional episodic learning approaches.
📖 Full Retelling
arXiv:2603.05160v1 Announce Type: cross
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
🏷️ Themes
Robotics, AI Learning
📚 Related People & Topics
Artificial intelligence
Intelligence of machines
# Artificial Intelligence (AI) **Artificial Intelligence (AI)** is a specialized field of computer science dedicated to the development and study of computational systems capable of performing tasks typically associated with human intelligence. These tasks include learning, reasoning, problem-solvi...
Entity Intersection Graph
Connections for Artificial intelligence:
🏢
OpenAI
14 shared
🌐
Reinforcement learning
4 shared
🏢
Anthropic
4 shared
🌐
Large language model
3 shared
🏢
Nvidia
3 shared
Mentioned Entities
Original Source
--> Computer Science > Robotics arXiv:2603.05160 [Submitted on 5 Mar 2026] Title: Lifelong Language-Conditioned Robotic Manipulation Learning Authors: Xudong Wang , Zebin Han , Zhiyu Liu , Gan Li , Jiahua Dong , Baichen Liu , Lianqing Liu , Zhi Han View a PDF of the paper titled Lifelong Language-Conditioned Robotic Manipulation Learning, by Xudong Wang and 7 other authors View PDF HTML 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. Comments: 14 pages, 7 figures Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.05160 [cs.RO] (or arXiv:2603.05160v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2603.05160 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Xudong Wang [ view email ] [v1] Thu, 5 Mar 2026 13:30:33 UTC (5,271 KB) Full-text links: Access Paper: View a PDF of the paper titled Lifelong Lang...
Read full article at source