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RoboMatrix: A Skill-centric Hierarchical Framework for Scalable Robot Task Planning and Execution in Open-World
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Existing robot policies predominantly adopt the task-centric approach, requiring end-to-end task data collection. This results in limited generalization to new tasks and difficulties in pinpointing errors within long-horizon, multi-stage tasks. To address this, we propose RoboMatrix, a skill-centric hierarchical framework designed for scalable robot task planning and execution in open-world environments. RoboMatrix extracts general meta-skills from diverse complex tasks, enabling the completion of unseen tasks through skill composition. Its architecture consists of a high-level scheduling layer that utilizes large language models (LLMs) for task decomposition, an intermediate skill layer housing meta-skill models, and a low-level hardware layer for robot control. A key innovation of our work is the introduction of the first unified vision-language-action (VLA) model capable of seamlessly integrating both movement and manipulation within one model. This is achieved by combining vision and language prompts to generate discrete actions. Experimental results demonstrate that RoboMatrix achieves a 50% higher success rate than task-centric baselines when applied to unseen objects, scenes, and tasks. To advance open-world robotics research, we will open-source code, hardware designs, model weights, and datasets at https://github.com/WayneMao/RoboMatrix.
Forward citations
Cited by 6 Pith papers
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Training-free STF-Tokens plus a Causal Spatio-Temporal Graph let VLMs keep object permanence and action history, raising long-horizon robotic manipulation success far above reactive baselines.
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InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation
InCoM reports 23–28 percentage-point success-rate gains in mobile manipulation benchmarks by dynamically reweighting multi-scale perception via inferred motion intent and decoupling base-arm action generation with flo...
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SkillMemo: Expert-guided Skill Memory Framework for Compositional Embodied Manipulation
SkillMemo couples MoE-based skill discovery with episodic memory retrieval and reports consistent success-rate gains on diffusion and VLA policies for simulated and real manipulation tasks.
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AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation
AC-DiT adds mobility-to-body conditioning and perception-aware 2D/3D weighting to a diffusion transformer, improving success rates on simulated and real-world mobile manipulation tasks.
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