Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
Embodied AI models now span vision-language-action (VLA) models and world-action models (WAMs), but practical deployment remains fragmented across model-specific Python stacks, backend assumptions, and robot-side glue code, especially on heterogeneous edge devices. Existing inference runtimes are designed mainly for request-response serving and therefore do not satisfy the runtime contract of embodied deployment: multi-rate execution inside closed-loop control, latency-first batch-1 inference on heterogeneous hardware, and extensible embodied interfaces beyond fixed token I/O. We present Embod
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- PossiblePossibly related (embedding) · 52%OpenMind/OM1 →
- PossiblePossibly related (embedding) · 50%DashAISoftware/dashAI →
- PossiblePossibly related (embedding) · 50%OpenHands/OpenHands →
- PossiblePossibly related (embedding) · 49%pytorch/executorch →
- PossiblePossibly related (embedding) · 48%Build generative UI for AI agents on Amazon Bedrock AgentCore with the AG-UI protocol →
- LinkedLinked via arxiv author · 85%Dingling Xu →
“Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots”
- LinkedLinked via arxiv author · 85%Chuyu Han →
“Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots”
- LinkedLinked via arxiv author · 85%Borui Li →
“Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots”