对话维他动力秦海龙：具身智能真正难题不是让机器人「学会」，而是跨本体「继承」
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Merged summary
TL;DR - Vbot unveiled its ATOM humanoid robot and “Embodied Genome” architecture for transferring high-level intelligence across robot bodies while adapting low-level control to each morphology. The approach matters because reusable capabilities and real-world feedback loops could reduce the need to retrain every new robot form from scratch.
- Vbot-OmniDuplex aligns streaming multimodal inputs in 240 ms micro-turns and separates low-latency interaction from asynchronous spatial reasoning and task planning.
- Vbot-WorldModel predicts and scores action-conditioned futures, allowing a policy to select actions based on task completion, consistency, geometry, and collision safety.
- Vbot-EvoMorph shares perception, latent state, and abstract action tokens across embodiments, then uses body-specific adapters for quadrupeds, humanoids, and manipulators.
- A Real-Sim-Real loop reconstructs simulations from physical telemetry, trains with large-scale perturbations, and redeploys policies to robots so new failures feed subsequent iterations.
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对话维他动力秦海龙：具身智能真正难题不是让机器人「学会」，而是跨本体「继承」
TL;DR - Vbot unveiled its ATOM humanoid robot and “Embodied Genome” architecture for transferring high-level intelligence across robot bodies while adapting low-level control to each morphology. The approach matters because reusable capabilities and real-world feedback loops could reduce the need to retrain every new robot form from scratch.
- Vbot-OmniDuplex aligns streaming multimodal inputs in 240 ms micro-turns and separates low-latency interaction from asynchronous spatial reasoning and task planning.
- Vbot-WorldModel predicts and scores action-conditioned futures, allowing a policy to select actions based on task completion, consistency, geometry, and collision safety.
- Vbot-EvoMorph shares perception, latent state, and abstract action tokens across embodiments, then uses body-specific adapters for quadrupeds, humanoids, and manipulators.
- A Real-Sim-Real loop reconstructs simulations from physical telemetry, trains with large-scale perturbations, and redeploys policies to robots so new failures feed subsequent iterations.