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Tag: #robotics1483 results

LAP Achieves Zero-Shot Robot Embodiment Transfer

Language-Action Pre-training (LAP) represents robot actions in natural language for zero-shot transfer across embodiments without fine-tuning. LAP-3B, a 3B VLA, delivers over 50% success on novel robots and tasks. Enables efficient adaptation and unifies action prediction with VQA.

ArXiv AIResearch•Feb 12#research#lap-3b#v1

AugVLA-3D Boosts VLA with Depth Augmentation

AugVLA-3D integrates depth estimation from RGB inputs via VGGT to enrich 3D features in vision-language-action models. An action assistant module ensures consistency with control tasks. It enhances generalization and robustness in complex 3D robotic environments.

ArXiv AIResearch•Feb 12#research#augvla-3d#v1

Affordances Build Partial LLM World Models

Proves LLMs possess predictive partial-world models via task-agnostic affordances for intents. Introduces distribution-robust affordances for multi-task efficiency. Reduces search branching in robotics, outperforming full world models.

ArXiv AIResearch•Feb 12#research#affordance-models#v1