StreamingClaw Technical Report
Jiawei Chen, Zhe Chen, Chaoqun Du, Maokui He, Wei He, Hengtao Li, Qizhen Li, Zide Liu, Hao Ma, Xuhao Pan, Chang Ren, Xudong Rao, Xintian Shen, Chenfeng Wang, Tao Wei, Chengjun Yu, Pengfei Yu, Shengyu Yao, Chunpeng Zhou, Kun Zhan, Lihao Zheng, Pan Zhou, Xuhan Zhu, Yufei Zheng

TL;DR
StreamingClaw is a comprehensive framework enabling real-time, multimodal streaming video understanding and embodied intelligence, addressing key limitations of existing agents in dynamic real-world environments.
Contribution
It introduces a unified agent framework supporting real-time reasoning, long-term multimodal memory, and closed-loop perception-decision-action, enhancing capabilities for embodied intelligence.
Findings
Supports real-time streaming reasoning and proactive interaction.
Enables multimodal long-term memory storage and sharing.
Compatible with open-source frameworks like OpenClaw.
Abstract
Emerging applications such as embodied intelligence, AI hardware, autonomous driving, and intelligent cockpits rely on a real-time perception-decision-action closed loop, posing stringent challenges for streaming video understanding. However, current agents mostly suffer from fragmented capabilities, such as supporting only offline video understanding, lacking long-term multimodal memory mechanisms, or struggling to achieve real-time reasoning and proactive interaction under streaming input. These shortcomings have become a key bottleneck for preventing agents from sustaining perception, making real-time decisions, and executing closed-loop actions in complex real-world environments, constraining their deployment and potential in dynamic, open physical worlds. To alleviate these issues, we propose StreamingClaw, a unified agent framework for streaming video understanding and embodied…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Social Robot Interaction and HRI · Speech and dialogue systems
