ABot-Claw: A Foundation for Persistent, Cooperative, and Self-Evolving Robotic Agents
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911600338796544 |
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| author | Huo, Dongjie Liu, Haoyun Liu, Guoqing Qi, Dekang Sun, Zhiming Gao, Maoguo He, Jianxin Yang, Yandan Chang, Xinyuan Xiong, Feng Wei, Xing Ma, Zhiheng Xu, Mu |
| author_facet | Huo, Dongjie Liu, Haoyun Liu, Guoqing Qi, Dekang Sun, Zhiming Gao, Maoguo He, Jianxin Yang, Yandan Chang, Xinyuan Xiong, Feng Wei, Xing Ma, Zhiheng Xu, Mu |
| contents | Current embodied intelligent systems still face a substantial gap between high-level reasoning and low-level physical execution in open-world environments. Although Vision-Language-Action (VLA) models provide strong perception and intuitive responses, their open-loop nature limits long-horizon performance. Agents incorporating System 2 cognitive mechanisms improve planning, but usually operate in closed sandboxes with predefined toolkits and limited real-system control. OpenClaw provides a localized runtime with full system privileges, but lacks the embodied control architecture required for long-duration, multi-robot execution. We therefore propose ABot-Claw, an embodied extension of OpenClaw that integrates: 1) a unified embodiment interface with capability-driven scheduling for heterogeneous robot coordination; 2) a visual-centric cross-embodiment multimodal memory for persistent context retention and grounded retrieval; and 3) a critic-based closed-loop feedback mechanism with a generalist reward model for online progress evaluation, local correction, and replanning. With a decoupled architecture spanning the OpenClaw layer, shared service layer, and robot embodiment layer, ABot-Claw enables real-world interaction, closes the loop from natural language intent to physical action, and supports progressively self-evolving robotic agents in open, dynamic environments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_10096 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ABot-Claw: A Foundation for Persistent, Cooperative, and Self-Evolving Robotic Agents Huo, Dongjie Liu, Haoyun Liu, Guoqing Qi, Dekang Sun, Zhiming Gao, Maoguo He, Jianxin Yang, Yandan Chang, Xinyuan Xiong, Feng Wei, Xing Ma, Zhiheng Xu, Mu Computer Vision and Pattern Recognition 68T45, 68T40, 68T42, 68T50 I.2.10; I.2.9; I.2.11; I.2.7 Current embodied intelligent systems still face a substantial gap between high-level reasoning and low-level physical execution in open-world environments. Although Vision-Language-Action (VLA) models provide strong perception and intuitive responses, their open-loop nature limits long-horizon performance. Agents incorporating System 2 cognitive mechanisms improve planning, but usually operate in closed sandboxes with predefined toolkits and limited real-system control. OpenClaw provides a localized runtime with full system privileges, but lacks the embodied control architecture required for long-duration, multi-robot execution. We therefore propose ABot-Claw, an embodied extension of OpenClaw that integrates: 1) a unified embodiment interface with capability-driven scheduling for heterogeneous robot coordination; 2) a visual-centric cross-embodiment multimodal memory for persistent context retention and grounded retrieval; and 3) a critic-based closed-loop feedback mechanism with a generalist reward model for online progress evaluation, local correction, and replanning. With a decoupled architecture spanning the OpenClaw layer, shared service layer, and robot embodiment layer, ABot-Claw enables real-world interaction, closes the loop from natural language intent to physical action, and supports progressively self-evolving robotic agents in open, dynamic environments. |
| title | ABot-Claw: A Foundation for Persistent, Cooperative, and Self-Evolving Robotic Agents |
| topic | Computer Vision and Pattern Recognition 68T45, 68T40, 68T42, 68T50 I.2.10; I.2.9; I.2.11; I.2.7 |
| url | https://arxiv.org/abs/2604.10096 |