GEAR-VLA: Learning Geometry-Aware Action Representations for Generalizable Robotic Manipulation

• URL: http://arxiv.org/abs/2606.08530v1
• Date: Sun, 07 Jun 2026 09:23:16 GMT
• Title: GEAR-VLA: Learning Geometry-Aware Action Representations for Generalizable Robotic Manipulation
• Abstract summary: Vision-Language-Action (VLA) models achieve strong benchmark performance but struggle in real-world deployment with unseen objects.<n>We argue that this stems from the lack of a unified geometry-aware manipulation representation.<n>We propose GEAR-VLA, a VLA framework for learning unified geometry-aware action representations for generalizable robotic manipulation.
• Score: 49.16739604572808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models achieve strong benchmark performance but still struggle in real-world deployment with unseen objects, background shifts, and different robot embodiments. We argue that this stems from the lack of a unified geometry-aware manipulation representation, leaving existing VLAs vulnerable to low-level trajectory supervision, misaligned 3D features, and embodiment differences. To address this, we propose GEAR-VLA, a VLA framework for learning unified geometry-aware action representations for generalizable robotic manipulation. GEAR-VLA adopts coarse-to-fine action learning, where multi-source embodied pretraining equips the VLM with embodied reasoning and discrete action understanding before latent action tokens connect action semantics to a gradient-decoupled DiT continuous action expert. It further performs semantic-aligned 3D integration by aligning a trainable 3D spatial backbone with the VLA representation while freezing the original VLM-aligned visual pathway. To share this representation across robots, GEAR-VLA uses embodiment canonicalization, where embodiment-aware states and embodiment-invariant actions confine robot differences to the low-level interface. Extensive simulation and real-world experiments demonstrate strong generalization: GEAR-VLA achieves state-of-the-art performance on LIBERO, zero-shot LIBERO-Plus, and RoboTwin 2.0, reaches 85.9% success on AgileX and 81.0% on the pretraining-unseen LDT-01 embodiment, and obtains 90.1% success on a 6,360-trial universal grasping benchmark with 212 unseen objects. Code and models will be released at https://github.com/babynabeauty/GEAR-VLA.

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