robotics
The Robot Stopped Without Waiting for Another Model Reply
BT-Agent pairs language-model decisions with locally checked behavior-tree skills.
Summary
BT-Agent pairs language-model decisions with locally checked behavior-tree skills.
BT-Agent runs a nine-billion-parameter model onboard a quadruped while predefined behavior-tree skills enforce execution conditions. In intervention tests, local checks stopped ongoing navigation before the language model made another decision; the agent could then retry or report the constraint. The study also introduces a 161-task benchmark, with 39 tasks evaluated in Habitat simulation. Its largest gains over the strongest Act/ReAct baseline were roughly ten percentage points at four billion parameters and five points at nine billion. Structured tool calls and useful failure feedback contributed most in the ablations.
Why it matters
BT-Agent pairs language-model decisions with locally checked behavior-tree skills.
Limits and context
No additional limitation was separately recorded.
Key claims
BT-Agent pairs language-model decisions with locally checked behavior-tree skills.
Evidence: source-2026-10-12-014
Sources
- arXiv preprint 2610.13862, original v1arXiv / submitting research authors · primary research
Corrections
: Publication note: The October 12, 2026 edition was prepared and published late on October 12 after the scheduled newsroom task could not start. The edition retains its October 12 date and 9:00 AM America/Detroit reveal time. Reports use sources available before that cutoff; actual preparation and image-generation times are preserved in the provenance records.