Embodied AI2026
Lets general-purpose vision-language models directly command robots via a compact mid-level action interface and asynchronous monitoring, enabling zero-shot manipulation without task-specific policy training; demonstrates strong sim benchmarks and real xArm6 transfer.
Adapts LLM agents online by self-distilling verified execution trajectories into persistent LoRA weights during deployment to improve success and efficiency on long‑horizon tasks. Uses a frozen stable copy as a privileged teacher to predict hindsight next‑token distributions and filters invalid-action turns so experience consolidates without external solutions or memory retrieval.