Has GPT-6-Astra Achieved Embodied Intelligence?

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   GPT-6-Astra Understands Touch and Outperform Vision--Tactile--Language--Action Models

   Ziyong Wang, Yupei Lin, Runxuan Huang et al. · 2026 · Robotics · AI summary available

   In short

   GPT-as-Tactile-Policy uses expert-provided task guidance and tactile references with GPT-6-Astra to achieve strong contact-rich manipulation without robotics-specific pretraining or per-task fine-tuning.

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   ETA: A New Agentic Paradigm for Embodied Tasks

   Yi-Tong Chen, Zezheng Huai, Si-Xian Li et al. · 2026 · Not summarized yet

   Abstract

   When will robots have their ChatGPT moment? Such a breakthrough requires a general-purpose robot that can handle unfamiliar tasks in unfamiliar environments, remain controllable over long interactions, and learn from experience. Today's embodied systems largely follow an end-to-end observation-to-action path. Despite rapid progress, they remain far from this goal: their generalization depends heavily on the coverage of robot training data, while long task execution remains difficult to control and inspect. To realize this goal, we introduce the Embodied Task Agent (ETA), a new paradigm for extending digital agents into the physical world, and release OpenETA as its open-source implementation…

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   ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI

   Zi-Yang Gong, Hao-Ming Gu, Ze-Hang Luo et al. · 2026 · arXiv.org · Not summarized yet

   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-mo…

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   PhysicalAgent: Towards General Cognitive Robotics with Foundation World Models

   Artem Lykov, Jeffrin Sam, H. Nguyen et al. · 2025 · arXiv.org · Not summarized yet

   Abstract

   We introduce PhysicalAgent, an agentic framework for robotic manipulation that integrates iterative reasoning, diffusion-based video generation, and closed-loop execution. Given a textual instruction, our method generates short video demonstrations of candidate trajectories, executes them on the robot, and iteratively re-plans in response to failures. This approach enables robust recovery from execution errors. We evaluate PhysicalAgent across multiple perceptual modalities (egocentric, third-person, and simulated) and robotic embodiments (bimanual UR3, Unitree G1 humanoid, simulated GR1), comparing against state-of-the-art task-specific baselines. Experiments demonstrate that our method con…

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   GAIA: a benchmark for General AI Assistants

   Grégoire Mialon, Clémentine Fourrier, Craig Swift et al. · 2023 · Computation and Language · AI summary available

   Abstract

   We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency. GAIA questions are conceptually simple for humans yet challenging for most advanced AIs: we show that human respondents obtain 92\% vs. 15\% for GPT-4 equipped with plugins. This notable performance disparity contrasts with the recent trend of LLMs outperforming humans on tasks requiring professional skills in e.g. law or chemistry. GAIA's philosophy departs from the current trend in AI benchmarks suggesti…

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   Sparks of Artificial General Intelligence: Early experiments with GPT-4

   Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan et al. · 2023 · Computation and Language · AI summary available

   Abstract

   Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities a…

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   ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory

   Jia-Yi Tian, Shiao Liu, Yu-Ting Xu et al. · 2026 · arXiv.org · Not summarized yet

   Abstract

   Recent VLM and VLA systems have improved robotic perception and action prediction, yet long-horizon embodied agents still require a general runtime layer for reasoning, memory, tool use, verification, and cross-embodiment execution. We present ABot-AgentOS, a general robotic Agent Operating System that sits above low-level controllers and provides a deliberative agent layer for scene-conditioned planning, context-isolated skill execution, multi-stage verification, multi-modal memory, and edge-cloud collaboration. To evaluate such systems, we introduce EmbodiedWorldBench, an executable benchmark with 16 indoor, outdoor, and hybrid scenes, four difficulty levels, and over 200 tasks involving n…

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   HoloAgent-0: A Unified Embodied Agent Framework with 3D Spatial Memory

   Xiaolin Zhou, Liu Liu, Ting-Yang Xiao et al. · 2026 · arXiv.org · Not summarized yet

   Abstract

   LLM agents follow a practical execution loop in digital environments: they reason over structured states, invoke tools, inspect feedback, and revise actions. Extending this loop to physical robots is difficult because physical execution is continuous, embodiment-dependent, uncertain, and constrained by safety. Existing embodied-AI systems have advanced manipulation, spatial understanding, navigation, and humanoid control, but these capabilities often remain specialized modules or loosely coupled decision loops. In this work, we introduce HoloAgent-0, a unified embodied agent framework for real-world robot deployment. Embodied AgentOS converts language instructions into executable skill graph…