RoboHarness: シンプルなハーネスがVLAとワールドアクションモデルを凌駕する
RoboHarness: A Simple Harness Outperforms VLA and World Actions Models
LLMエージェントに視覚幾何学的な操作パネルを与えるロボットハーネスを提案し、VLAモデルを訓練せずにBEHAVIOR Challenge 2025で最先端VLAを上回る性能を達成した。
著者: Bince Qu, Wei Chen, Bo Zhang
分類: cs.RO, cs.AI
原文アブストラクト
The rise of large language models (LLMs) has opened a path toward generalist embodied AI, raising the question of how LLMs can act in the physical world. One straightforward approach is training the model to generate actions: collect robot-action data and video demonstration, refine LLM architecture with an action head, and train a vision-language-action (VLA) model at scale. However, we argue that coding agents have the potential to realize generalist embodied manipulation. All they need is a simple yet effective robot harness. We present RoboHarness: a robotic harness that gives LLM agents a visual-geometric control panel so that they can directly understand and invoke embodied tasks. With RoboHarness, Qwen3.8-Flash-Next outperforms state-of-the-art VLA models on BEHAVIOR Challenge 2025 tasks. The implementation will be released as open source.