The remarkable progress of Multimodal Large Language Models (MLLMs) has attracted increasing attention to extending them to physical entities like legged robots and robotic arms. This typically requires MLLMs to not only grasp multimodal understanding abilities, but also integrate visual-spatial reasoning and physical interaction capabilities. Nevertheless, existing methods struggle to jointly accommodate these capabilities within a shared multimodal model, due to the substantial differences between vision-language reasoning and low-level physical control. In this paper, we present Visual Embodied Brain-1.5 (VeBrain-1.5), a task-level unified framework that connects multimodal perception and spatial reasoning with robot control through a shared MLLM-compatible decision interface. VeBrain-1.5 reformulates robotic control into common text-based MLLM tasks in the 2D visual space, thereby aligning the supervision format and prediction interface of multimodal understanding, spatial reasoning, and robot control. A modular robotic adapter subsequently translates these high-level textual decisions into embodiment-specific motion commands and executes them in a closed loop. From the data perspective, we further introduce VeBrain-1M, a high-quality instruction dataset encompassing various capabilities of VeBrain-1.5. In VeBrain-1M, we take hundreds of hours to collect, curate and annotate the data, and adopt multimodal chain-of-thought (CoT) to mix the different capabilities into a single conversation. Extensive experiments on 13 multimodal benchmarks, 7 spatial intelligence benchmarks and 4 simulation and real-world robotic benchmarks demonstrate the superior performance of VeBrain-1.5 over existing MLLMs like Qwen2.5-VL. To further eliminate the task ambiguity and enhance the reasoning capability, we adopt an offline RL policy learning with verifiable reward to increase the task success rate. When deployed to two robotic entities including legged robots and robotic arms, VeBrain-1.5 shows strong adaptability, flexibility, and compositional capabilities compared to existing methods.
Ganlin Yang, G. Luo, Ziyang Gong et al.· IEEE Transactions on Pattern...· 0 citations
Robot policy evaluation and deployment remain fragmented by model-specific software dependencies, data representations, and runtime interfaces, so that connecting N policies to M evaluation environments requires O(NM) separate integrations. We present XPolicyLab, a unified standard and open ecosystem that reduces this cost to O(N+M). XPolicyLab specifies common observation, action, and trajectory schemas together with a minimal adapter interface for observation updates, action prediction, batched execution, and episode reset, while a dependency-isolated client/server architecture separates policy inference from environment execution, so that each side retains its native software stack and may run locally or remotely. The ecosystem integrates 42 robot policies and standardizes their installation, debugging, serving, and evaluation workflows. Across these adapters, model-specific code varies by an order of magnitude while the environment-facing loop stays within a few lines of a fixed reference, confirming that the contract confines heterogeneity to the policy side. In a controlled study, conforming to the standard reduces the integration effort of a representative policy from over five hours to two hours, and packaged agent skills reduce it further to thirty minutes. The same adapters serve RoboTwin, RoboDojo simulation, and standardized real-robot evaluation through one interface. XPolicyLab is released as shared infrastructure for reproducible policy comparison and standardized deployment across simulation and physical platforms. Project website: https://xpolicylab.github.io/.
XPolicyLab Community, Tianxing Chen, Yue Chen et al.· 0 citations
Cortex is introduced, a bidirectionally aligned embodied agent framework with a customized planning interface that conveys executable and tractable subtask plans from high-level VLM to low-level VLA and enables zero-shot completion of unseen real-world long-horizon tasks.
Jiaqi Peng, Xiqian Yu, Delin Feng et al.· 1 citation