98年中科大博士段逸凡创立灵犀智涌 以“模型+Harness”架构打破工业具身“唯模型困局”
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Merged summary
TL;DR - Shanghai startup Lingxi Zhiyong introduced ROSS, an industrial embodied-AI harness designed to turn probabilistic robot-model outputs into stable, recoverable production workflows. Its “model + harness” architecture matters because it targets reliability, throughput, and maintainability rather than one-off demonstrations.
- ROSS provides unified interfaces for VLA and world-action models, allowing models to be replaced while preserving task planning, skills, safety policies, and conventional controls.
- Reusable skills encode execution logic, operating conditions, safety constraints, success criteria, and failure handling, converting factory expertise into transferable digital assets.
- Agentic planning handles task decomposition, skill orchestration, and recovery, while layered monitoring assigns real-time safety and higher-level decisions to appropriate control timescales.
- Execution traces—including failures, interventions, and recovery paths—feed improvements to models, skills, scheduling, and memory without requiring every update to await large-scale retraining.
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98年中科大博士段逸凡创立灵犀智涌 以“模型+Harness”架构打破工业具身“唯模型困局”
TL;DR - Shanghai startup Lingxi Zhiyong introduced ROSS, an industrial embodied-AI harness designed to turn probabilistic robot-model outputs into stable, recoverable production workflows. Its “model + harness” architecture matters because it targets reliability, throughput, and maintainability rather than one-off demonstrations.
- ROSS provides unified interfaces for VLA and world-action models, allowing models to be replaced while preserving task planning, skills, safety policies, and conventional controls.
- Reusable skills encode execution logic, operating conditions, safety constraints, success criteria, and failure handling, converting factory expertise into transferable digital assets.
- Agentic planning handles task decomposition, skill orchestration, and recovery, while layered monitoring assigns real-time safety and higher-level decisions to appropriate control timescales.
- Execution traces—including failures, interventions, and recovery paths—feed improvements to models, skills, scheduling, and memory without requiring every update to await large-scale retraining.