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VLA-OS: Structuring and Dissecting Planning Representations and Paradigms in Vision-Language-Action Models
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Recent studies on Vision-Language-Action (VLA) models have shifted from the end-to-end action-generation paradigm toward a pipeline involving task planning followed by action generation, demonstrating improved performance on various complex, long-horizon manipulation tasks. However, existing approaches vary significantly in terms of network architectures, planning paradigms, representations, and training data sources, making it challenging for researchers to identify the precise sources of performance gains and components to be further improved. To systematically investigate the impacts of different planning paradigms and representations isolating from network architectures and training data, in this paper, we introduce VLA-OS, a unified VLA architecture series capable of various task planning paradigms, and design a comprehensive suite of controlled experiments across diverse object categories (rigid and deformable), visual modalities (2D and 3D), environments (simulation and real-world), and end-effectors (grippers and dexterous hands). Our results demonstrate that: 1) visually grounded planning representations are generally better than language planning representations; 2) the Hierarchical-VLA paradigm generally achieves superior or comparable performance than other paradigms on task performance, pretraining, generalization ability, scalability, and continual learning ability, albeit at the cost of slower training and inference speeds.
Forward citations
Cited by 6 Pith papers
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RoboInter1.5: A Holistic Intermediate Representation Suite for Embodied World Modeling and Robotic Manipulation
Dense per-frame intermediate representations (traces, masks, grasp poses, subtasks) improve embodied VQA, VLA action generation, and world-model video prediction in the new 230k-episode RoboInter-Data suite.
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Mixture of Horizons in Action Chunking
A gated mixture of multiple action-chunk horizons in a shared full-attention transformer improves VLA manipulation success and enables consensus-based early stopping.
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Galaxea Open-World Dataset and G0 Dual-System VLA Model
A new open-world mobile manipulation dataset and a dual-system VLA model show that single-embodiment pre-training, not cross-embodiment pre-training, drives strong downstream task performance.
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Beyond Flat Policies: Hierarchical Post-Training for Embodied Agents in Robotic Manipulation
HiRoC uses a language planner to split manipulation tasks into subgoals and a reinforcement-learned executor to follow them, reporting 93.5% average success on LIBERO.
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Transforming Remanufacturing Automation with Large Language Models: A Forward-Looking Analysis with Case Studies
The authors propose ReManGPT, a conceptual orchestration framework for applying LLMs to remanufacturing, and illustrate it with case studies in disassembly planning, repair guidance, and robotic execution.
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TS-Mask VLA: 2D Temporal-Spatial Masking for Vision-Language-Action Model with Effective Bridging
A 0.5B VLA with bridge-conditioned discrete diffusion and 2D temporal–spatial action masking reaches 95.7% LIBERO success and 4.19 CALVIN average length.
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