FRAMES: ヒューマノイド移動操作のための失敗回復とモニタリング
FRAMES: Failure Recovery And Monitoring of Embodied Skills for Humanoid Loco-Manipulation
ヒューマノイドの移動操作において、LLMプランナーとVLMモニターを組み合わせ、失敗を検知して回復する監視フレームワークを提案。MuJoCoで監視モジュールの精度94%を達成。
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著者: Ajay Vikram Periasami, Xinyuan Luo, Haoyu Li, Xianyi Cheng
分類: cs.RO, cs.AI
原文アブストラクト
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-language-model-based Monitor Agent evaluates each skill using temporal multi-view observations and structured robot and contact evidence. Detected failures stop the active skill and provide grounded feedback to a Recovery Agent. The framework further includes a Memory Module for reusing prior skill experience, and geometric grounding via depth and segmentation. We independently evaluate the monitoring module of the framework in MuJoCo using 100 trials comprising 50 failed and 50 successful executions across five tasks. The monitor detects 48 of 50 failures, correctly accepts 46 of 50 successful executions, and achieves 94.0% overall accuracy. These results provide initial evidence for the monitoring component, while end-to-end evaluation of the complete recovery loop remains ongoing.
関連論文
- Opt2Skill: 動的に実行可能な全身軌道を模倣する多様なヒューマノイド移動操作ヒューマノイド移動操作
