sensVLA: Spatially-Grounded Vision-Language-Action Model for Autonomous Wheel Loader
Autonomous wheel-loader control requires joint reasoning over task semantics, egocentric vision, proprioception, and 3D scene geometry. We present sensVLA, a Vision-Language-Action (VLA) architecture that combines a Qwen3-2B Vision-Language Model (VLM) with a fully trainable transformer action expert trained by flow-matching velocity regression. sensVLA routes Bird's-Eye-View (BEV) features, extracted from fused front and rear lidar, directly to the action expert through a dedicated cross-attention pathway, while the VLM consumes front and rear RGB views to provide task-conditioned semantic context. This design decouples spatial grounding from linguistic reasoning while preserving interac...
| Authors | Gopi Krishna Erabati, Bjarne Johannsen, Angus Stewart et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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