ACE-Brain-0.5 is presented, a unified embodied foundation model that organizes robot intelligence into five coupled functions: spatial perception, decision making, embodied interaction, self-monitoring, and self-improvement, and SSR+, which extends Scaffold-Specialize-Reconcile with a Reactivate stage after task-vector merging.
Abstract
Embodied AI is moving from isolated perception or action modules toward physical agents that understand, plan under goals, act through robot bodies, monitor progress, and improve from experience. Existing systems address this loop only in parts: end-to-end policies generate actions but often lack spatial reasoning, planning, and execution assessment, while robot-agent systems orchestrate tools or specialists but do not learn a shared representation. This fragmentation limits general Physical Agentic AI. We present ACE-Brain-0.5, a unified embodied foundation model that organizes robot intelligence into five coupled functions: spatial perception, decision making, embodied interaction, self-monitoring, and self-improvement. Built on ACE-Brain-0, which established spatial intelligence as a shared scaffold across robot platforms, ACE-Brain-0.5 extends an understanding-centric model into a closed-loop foundation model. A single 8B backbone instantiates the first four functions: grounding objects and affordances, reasoning over 3D and egocentric spatial relations, decomposing instructions into subgoals, generating navigation and manipulation actions, and estimating progress for verification and recovery. To unify these capabilities without cross-task interference, we introduce SSR+, which extends Scaffold-Specialize-Reconcile with a Reactivate stage after task-vector merging. The fifth function, self-improvement, is realized by a companion framework that updates external execution state, including task schemas, spatial memory, and failure-recovery cases, from rollouts. Across fifteen benchmarks, ACE-Brain-0.5 improves over ACE-Brain-0 on 14 of 18 spatial perception and grounding benchmarks, achieves competitive navigation and manipulation performance, and provides strong progress estimation in ID and OOD settings. Together, these results mark an early step toward general Physical Agentic AI.
A comprehensive review of VLA models for Embodied AI from an action‐generation perspective and proposes an action‐generation‐centered taxonomy that categorizes VLA models into three paradigms: direct policy learning, generative action modeling, and reasoning‐guided modeling.
Ning Xiong, Mingle Xu, Wei Chen et al.· Journal of Field Robotics· 0 citations
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
Embodied Artificial Intelligence (Embodied AI) has emerged as a promising paradigm for developing more general and adaptive intelligent systems, emphasizing that intelligence emerges from continuous interaction among perception, cognition, and action in real-world environments. Recent advances increasingly integrate large language models and multimodal learning into embodied agents; however, most existing approaches remain correlation-driven, relying on implicit objectives, task-specific rewards, or prompt-level instructions. As a consequence, intent is rarely represented explicitly, limiting causal coherence, long-horizon consistency, and robust value alignment in open-world settings. In this Review, we synthesize recent progress in Embodied AI and articulate Intent-Driven Embodied Artificial Intelligence (IDEAI) as a system-level organizing framework in which intent functions as an explicit, revisable, and verifiable mediating construct between human goals, environmental constraints, and agent behavior. Building on this synthesis, we propose a four-layer conceptual organization-semantic grounding, concept generation and learning, intent modeling, and value alignment-that clarifies how explicit intent mediates perception, cognition, and action in embodied systems. We analyze how existing techniques address recurring failure modes along the intent-to-execution pipeline and highlight the limitations that arise when intent remains implicit. By making intent explicit, revisable, and value-constrained where such structure is needed, IDEAI supports interpretable decision-making, adaptive task decomposition, and value-consistent behavior in open-ended, human-interactive, and safety-critical embodied domains, providing a unifying perspective for advancing Embodied AI toward robust, socially deployable intelligent systems.
Nanning Zheng· National Science Review· 0 citations
Capek 0.5 is presented, an embodied vision-language model built around an execution-centric capability taxonomy that improves the large majority of matched benchmark rows over its initialization, retains all four specialized capabilities in one checkpoint with quantified losses, and transfers to closed-loop embodied task execution.
Results demonstrate that unified World Action Modeling together with progressive embodied pretraining effectively transforms large-scale embodied experience into generalizable robot manipulation capabilities.
Hao Sun, Jiangbo Pei, Fei Kang et al.· 0 citations
Large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning, and world knowledge. As embodied agents become increasingly capable, there is a growing demand for compact models that can serve as an on-device brain, preserving the broad general intelligence of LLMs while enabling effective high-level interaction with embodied environments. Existing approaches, however, often prioritize either general-purpose intelligence or specialized embodied capabilities, making it challenging to satisfy both requirements within a single model. We present \textbf{Athena-Brain-8B}, an 8B LLM designed to serve as an on-device brain for embodied intelligence for embodied intelligence. Through a multi-stage post-training pipeline consisting of General Supervised Fine-Tuning, General Reinforcement Learning, Embodied Expert training, and Model Merge, Athena-Brain-8B maintains strong general capabilities while acquiring strong high-level embodied interaction capabilities and generating concise responses for efficient embodied interaction. Experimental results demonstrate the effectiveness of Athena across both general and embodied evaluations. Compared with the corresponding Qwen3-8B thinking model, Athena-Brain-8B achieves comparable performance on general language and reasoning benchmarks while generating substantially shorter responses. On in-domain embodied benchmarks, Athena-Brain-8B consistently outperforms models of similar scale and surpasses several substantially larger frontier models evaluated zero-shot, demonstrating that compact language models can effectively integrate strong general intelligence with embodied capabilities.
Jialian Li, Junhong Liu, Yuchen Cao et al.· arXiv.org· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
