ChunkVLA-AM: 積層造形における視覚-言語-行動ロボット制御のための並列アクションチャンキング
ChunkVLA-AM: Parallel Action Chunking for Vision-Language-Action Robot Control in Additive Manufacturing
積層造形向けにOpenVLA-OFTをFAIRINO FR3ロボットへ適応させ、8ステップのアクションチャンクを並列予測してクラウドエッジ構成で物体搬送を実行、42回中39回成功した。
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著者: Zhugang Liu, Kaichuang Zhang, Jinman Zhang, Pu Sun, Martha Asare, Jose Hernandez, Maxim Ermolinsky, Efren Saenz, Qi Lu, Jinghao Yang
分類: cs.RO
原文アブストラクト
Vision-language-action (VLA) models unify visual perception, language understanding, and action generation, offering new opportunities for automation in additive manufacturing (AM). However, deployment in AM remains challenging because adapting these models to unseen robot embodiments is costly, and performance can degrade under environment changes. In this work, we present a framework for deploying OpenVLA-OFT on a FAIRINO FR3 robot in a fixed AM workcell. A data pipeline converts monocular real-world demonstrations into OpenVLA-compatible TFDS/RLDS datasets to support adaptation to the FR3 embodiment. At runtime, each inference request predicts an eight-step chunk of 7-D actions. The FR3 executes each chunk open loop before capturing a new observation, providing closed-loop feedback between chunks. The system uses a cloud-edge architecture in which the FR3 client streams observations to a remote inference server through a FastAPI interface. In 42 physical A-to-B object-transfer trials, evenly split between red and blue targets, the system succeeded in 39 (92.9%). All three failures occurred during final placement, when insufficient release-height control caused the object to topple. An illumination sweep identified a low-error luminance range of 85-125 on a 0-255 scale, with the lowest mean spatial error at 95.