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1. 1

   Push the Limits: Human‐Like Multimodal Extreme Embodied Intelligence for Autonomous Racing

   paper

   Embodied robot learning — abstract watchYanxin Zhou, Baichuan Lou, Wenhui Huang, Binbin Hu, Haohan Yang, Haochen Liu, Xiangkun He, Chee Kiong Ong, et al.October 22, 2026

   Autonomous racing serves as a platform for testing embodied intelligence under extreme control conditions. It requires systems to operate at the limits of speed, actuation, and decision‐making. Inspired by human multimodality, which is the ability to integrate sensory inputs, learn adaptively, and transfer knowledge across domains for holistic situational awareness and optimal performance, we present Apex, an embodied intelligent agent that is implemented on an autonomous super‐formula racing vehicle on the Abu Dhabi Yas Marina F1 circuit. Apex integrates physics‐informed neural network‐enhanc

   Original source ↗Frontier AI
2. 2

   Neostatic Bioframe BF-07 - Feasible Architecture for Embodied Intelligence - 2026 Audit

   paper

   Embodied robot learning — abstract watchRussell SaundersOctober 22, 2026

   Neostatic Bioframe BF-07 is a feasible modular architecture for embodied AI with 8 integrated subsystems: vessel architecture, Neostatic Brain Jelly (NBJ) field computer, sensory ASP skin, somatic emotional engine, distributed energy harvesting, self-healing nanobot mesh with metabolism-as-filtering, locomotion, and feasibility roadmap. 2026 Audit Updates:- VT MADE Lab GaIn-PDMS printable self-healing composite membrane- Zwitterionic bimodal elastomer P(BA-SB) 272% strain / 95 MV/m breakdown / 1520 W/kg (Nature Communications 2026 DOI 10.1038/s41467-026-72611-5)- Tesla US Pat App US 2026/03102

   Original source ↗Frontier AISoftware engineering
3. 3

   Bridging cognition, reasoning and action for polymorphic embodied agents towards human-heterogeneous multi-robot collaborative assembly with vision-language model and digital twin

   paper

   Embodied robot learning — abstract watchChangchun Liu, Dunbing Tang, Haihua Zhu, Qixiang Cai, Liping Wang, Qingwei NieOctober 22, 2026

   With the advent of Industry 5.0, human-robot collaborative assembly has become a core production paradigm that combines human cognitive flexibility with robotic operational efficiency. Single-robot systems cannot meet the diverse requirements of complex assembly tasks. Therefore, collaboration between human operators and heterogeneous multi-robot teams is required for flexible and efficient manufacturing. However, current collaborative systems are limited by fragmented perception-reasoning-execution pipelines. Poor generalization of general-purpose models in industrial environments is also obs

   Original source ↗Frontier AI
4. 4

   Gradient-interlayer–based flexible pressure sensors for high-performance robotic perception

   paper

   Embodied robot learning — abstract watchMingyuan Sun, G L, Shuai Dong, Erlong Wang, Shi‐Yang Tang, Xuanhan Chen, Yipu Guo, Xiaoqin Sun, et al.October 23, 2026

   Tactile perception systems based on flexible pressure sensors are indispensable for humanoid robots to perform dexterous manipulation, compliant interaction, and operation in visually challenging environments. Although strategies relying on surface or spatial microstructure engineering have improved sensor performance, the long-standing trade-off among sensitivity, sensing range, and linearity continues to hinder their practical deployment. Inspired by the gradient distribution of mechanoreceptors in human fingertip skin, we report a gradient-interlayer–based flexible iontronic pressure sensor

   Original source ↗Frontier AISoftware engineering
5. 5

   EBM-Nav: energy-based model for zero-shot visual navigation via implicit scene representation

   paper

   Embodied robot learning — abstract watchMing Tang, Juan Wan, Xuan DangOctober 23, 2026

   Abstract Zero-shot visual navigation enables agents to generalize to unseen environments without task-specific training and is core to service robotics, warehouse logistics, and autonomous exploration. Existing approaches are observed to fail to achieve genuine zero-shot generalization: explicit-map methods rely on accurate reconstruction and are sensitive to sensor noise; supervised policies memorize training-scene patterns and degrade under illumination, furniture, and layout shifts; implicit representations offer continuous, differentiable encoding but remain confined to passive reconstruct

   Original source ↗Frontier AI
6. 6

   Title: A Synthetic-Hybrid Framework for Cognitive Neuro-Robotics: Integrating Predictive Processing and Metacognitive Feedback for Adaptive Embodied Intelligence

   paper

   Embodied robot learning — abstract watchNoorolahi SiamakOctober 11, 2026

   Original source ↗Frontier AI
7. 7

   MARS: Systematic Multi-Level Acceleration for World Action Models

   paper

   Embodied robot learning — abstract watchZengyuan Zuo, Yongchang Zhang, Guangyu Zhao, Tianze Zhang, Biao Wu, Meng Fang, Yiwu ZhongOctober 12, 2026

   World Action Models (WAMs) have emerged as a promising alternative for embodied intelligence by jointly predicting future world states and robot actions. However, their heavy visual encoding and iterative generative inference incur substantial latency, limiting their applicability to high-frequency closed-loop control. Existing training-free acceleration methods typically optimize a single stage, yielding limited speedups or degraded control under aggressive reuse. In this work, we systematically profile the inference overhead of WAMs and observe abundant redundancy across Transformer computat

   Original source ↗Frontier AI
8. 8

   Thinker-WM: A Unified World Model with Shared Multimodal Representations for Embodied Intelligence

   paper

   Embodied robot learning — abstract watchThinker Team, Peng Gao, Jichao Jiao, Chen Liao, Daqin Luo, Yiming Ma, Baiyu Pan, Hailin Shi, et al.October 13, 2026

   World models have emerged as a promising paradigm for embodied AI, yet existing systems often remain fragmented in the modalities they model and the functions they support. In particular, visual dynamics, geometric structure, and executable action are commonly coupled through separate interfaces or downstream modules rather than being jointly represented and evolved within a shared dynamics model. In this work, we present Thinker-WM, a unified multimodal world model that jointly models and generates RGB, metric depth, and robot action through a shared intermediate Unified Dynamics Transformer

   Original source ↗Frontier AI
9. 9

   From Intelligent Control to Integrated Autonomy: Reflections on Advanced Intelligent Robotics and Autonomous Control Systems

   paper

   Embodied robot learning — abstract watchTamás P. Haidegger, Levente Kovács, Péter GalambosOctober 13, 2026

   Robotics is advancing toward embodied intelligence and high-level autonomy, requiring the seamless integration of sensing, computation, communication, control, and human interaction to be designed as a coherent system. This Editorial synthesizes ten contributions to this Special Issue, entitled “Advanced Intelligent Robotics and Autonomous Control Systems—Celebrating the 75th Birthday of Professor Imre J. Rudas”, and relates their findings to computational intelligence and autonomous systems. Spanning robot navigation, human–robot safety, laboratory automation, digital pathology, manufacturing

   Original source ↗Frontier AI
10. 10

    Agentic Embodied AI for Human–Robot Collaboration: Architectures, Technologies, and Future Directions

    paper

    Embodied robot learning — abstract watchZhihao LiuOctober 13, 2026

    Human-robot collaboration has relied on task models that are specified in advance for each product. Embodied foundation models and agentic artificial intelligence change this premise, because a robot can now interpret an open goal, plan, act, monitor the outcome and recover from failure. This review examines what that change means for collaboration. A critical narrative review was conducted over 231 sources. Embodied intelligence, agentic intelligence, autonomy and collaboration are distinguished, and four collaboration modes are defined by the roles of the human and the robot. Four system arc

    Original source ↗Frontier AI
11. 11

    Tactile-Guided Robot Learning: From Representation Learning to Foundation Models

    paper

    Embodied robot learning — abstract watchKiyanoush Nazari, Luchen Li, Haoshu Fang, Nima Fazeli, Zhibin Li, Fumiya Iida A, Thomas George ThuruthelOctober 14, 2026

    Tactile sensing is becoming central to robust, adaptive, and dexterous robotic manipulation. Beyond its traditional role in contact detection and reactive control, touch is increasingly used to learn transferable representations, condition manipulation policies, and support embodied physical reasoning. Yet existing surveys largely emphasize tactile hardware and perception, leaving the rapid shift toward learning-based tactile manipulation insufficiently synthesized. This paper reviews tactile-guided skill learning from a learning-centric perspective, organized by the functional role of touch a

    Original source ↗Frontier AI
12. 12

    THE NATURE OF THE SOUL (Volume 1) An Interdisciplinary Postdoctoral Inquiry into Consciousness, Selfhood, Mind, Matter, and Personal Survival

    paper

    Panpsychism and analytic idealismMajid Karimi BaghmalekiOctober 13, 2026

    GENERAL PREFACE THE NATURE OF THE SOUL An Interdisciplinary Philosophical Inquiry into Consciousness, Personhood, Identity, Agency, Meaning, and the Architecture of Becoming The question of the soul is among the oldest and most persistent questions in human intellectual history. Long before consciousness became an object of neuroscience, before psychology emerged as an independent discipline, before computation provided a language for information and artificial intelligence, human beings asked what makes a living person more than a collection of physical parts. What is the principle by which a

    Original source ↗Consciousness & philosophyFrontier AIScience & psychologySoftware engineering