MindClaw extends Theory of Mind to a closed-loop embodied setting with an optimized trigger skill that enables precise, timely intervention while avoiding unnecessary actions.
Roboclaw: An agentic framework for scalable long-horizon robotic tasks
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A DAG-based QoS-aware dynamic task placement framework is proposed for multi-stage control pipelines in networked robotics, using a windowed cost function and hysteresis to balance latency, utilization, and switching stability.
ROSClaw is a hierarchical framework that unifies vision-language model control with e-URDF-based sim-to-real mapping and closed-loop data collection to enable semantic-physical collaboration among heterogeneous multi-agent robots.
A survey organizing AI-powered research automation into five workflow stages, defining AutoResearch and Vibe Research, and proposing five evaluation dimensions while noting domain-conditioned limits on autonomy.
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DAG-Based QoS-Aware Dynamic Task Placement for Networked Multi-Stage Control Pipelines
A DAG-based QoS-aware dynamic task placement framework is proposed for multi-stage control pipelines in networked robotics, using a windowed cost function and hysteresis to balance latency, utilization, and switching stability.