SOURCE-LINKED INTELLIGENCE
FRAMES: Failure Recovery And Monitoring of Embodied Skills for Humanoid Loco-Manipulation
Large language model (LLM) planners can decompose natural-language instructions and select reusable robot skills, but choosing the correct skill does not guarantee successful physical execution. This gap is especially important in humanoid loco-manipulation, where errors during approach, grasping, transport, or placement can invalidate the remainder of a long-horizon plan. We present FRAMES, a failure-aware supervisory framework for the Unitree G1 humanoid that operates above the CEER whole-body controller. A Planner Agent selects subtasks through parameterized mid-level skills, while a vision
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-18T19:53:21.000Z
First collected: 2026-09-23T12:01:45.602Z. This is not the publication date.