Meituan is making a major bet on embodied intelligence. - 36 Kr
<a href="https://news.google.com/rss/articles/CBMiU0FVX3lxTE16NkZ2Y080VENhM2J4MWFnY2txU2tnN1RpSlNuajE3YldhYmpWS3lYSmRlaVZtZU05emVCRHVnRm5tYUxoVXZnekItQldBZklsbDh3?oc=5" target="_blank">Meituan is making a major bet on embodied intelligence.</a> <font color="#6f6f6f">36 Kr</font>
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embodied
Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity
arXiv:2603.29332v1 Announce Type: new Abstract: The embodied learning of human motor control requires whole-body neuro-actuated musculoskeletal dynamics, while the internal muscle-driven processes underlying movement remain inaccessible to direct measurement. Computational modeling offers an alternative, but inverse dynamics methods struggled to resolve redundant control from observed kinematics in the high-dimensional, over-actuated system. Forward imitation approaches based on deep reinforcement learning exhibited inadequate tracking performance due to the curse of dimensionality in both control and reward design. Here we introduce a large-scale parallel musculoskeletal computation framework for biomechanically grounded whole-body motion reproduction. By integrating large-scale parallel
Baidu VP: Embodied AI Costs Will Drop Drastically, May Be Charged by Token in Future - AASTOCKS.com
<a href="https://news.google.com/rss/articles/CBMihgFBVV95cUxQaEQ0aUU3VHlkeC02RXllTzh0ZWo5UTdGUmJHN25QN1MwZGFMdVd0V2MxVWw4YzhnYjMxal9sbFlQeWgwUTVRdXJ3ekdzZ1A2clNUM0h6Q1JpVXYxVUlUSzljVjA3WGRocnFtRUhINk5GeVhxRHhNRkYwbjRTUTkxZ1h3T3NEUQ?oc=5" target="_blank">Baidu VP: Embodied AI Costs Will Drop Drastically, May Be Charged by Token in Future</a> <font color="#6f6f6f">AASTOCKS.com</font>
Owl-AuraID 1.0: An Intelligent System for Autonomous Scientific Instrumentation and Scientific Data Analysis
arXiv:2603.29828v1 Announce Type: new Abstract: Scientific discovery increasingly depends on high-throughput characterization, yet automation is hindered by proprietary GUIs and the limited generalizability of existing API-based systems. We present Owl-AuraID, a software-hardware collaborative embodied agent system that adopts a GUI-native paradigm to operate instruments through the same interfaces as human experts. Its skill-centric framework integrates Type-1 (GUI operation) and Type-2 (data analysis) skills into end-to-end workflows, connecting physical sample handling with scientific interpretation. Owl-AuraID demonstrates broad coverage across ten categories of precision instruments and diverse workflows, including multimodal spectral analysis, microscopic imaging, and crystallographi
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Nvidia's CEO Says AGI Is Here, But Don't Get Too Excited - PCMag Middle East
<a href="https://news.google.com/rss/articles/CBMisAFBVV95cUxPMXhmQUp6VERtX1N1eFF3YWpzV1dvdXl0SUptbUxMbTVJUHN0Nkh6NnpydzI4SnFFaFh3andXejVWeHYxN0lHaWFoNVlzODd4YmkyNkRiUnNLTHZodl9hSnJRdmJ2RWtFQ3ZocVliS3k1M0xBOHI4WEVYNURKMmowSGsxV3YtdHRmcHlpXzJHVVY0bWtxaTVvaXVhei1WdXEwam5ENFdwR0h2Qm1GbkRMcg?oc=5" target="_blank">Nvidia's CEO Says AGI Is Here, But Don't Get Too Excited</a> <font color="#6f6f6f">PCMag Middle East</font>
The Architecture of Forgetting.
<p>S. M. Gitandu, B.S. | Nairobi-01 | PADI Sovereign Bureau</p> <p>━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</p> <p>PECULIAR CATALOG</p> <p>━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</p> <p>CALL NO: 006.3 / PADI / 2026</p> <p>TITLE: The Architecture of Forgetting</p> <p>AUTHOR: Artificial Intelligence Systems</p> <p>CLASSIFICATION: AI Systems: Memory Architecture</p> <p>STATUS: Active</p> <p>DATE: April 2026</p> <p>CONDITION: Critical</p> <p>NOTE: Modern AI systems are architecturally designed to forget. This produces a systemic loss of context between interactions. The condition is defined here as ontological drift: the collapse of meaning across sessions.</p> <p>→ See also: 025.04 (Information Storage & Retrieval)</p> <p>→ Related: 004.67 (Data Communication Systems)</p> <p>→ Status
DCReg: Decoupled Characterization for Efficient Degenerate LiDAR Registration
arXiv:2509.06285v2 Announce Type: replace Abstract: LiDAR point cloud registration is fundamental to robotic perception and navigation. In geometrically degenerate environments (e.g., corridors), registration becomes ill-conditioned: certain motion directions are weakly constrained, causing unstable solutions and degraded accuracy. Existing detect-then-mitigate methods fail to reliably detect, physically interpret, and stabilize this ill-conditioning without corrupting the optimization. We introduce DCReg (Decoupled Characterization for Ill-conditioned Registration), establishing a detect-characterize-mitigate paradigm that systematically addresses ill-conditioned registration via three innovations. First, DCReg achieves reliable ill-conditioning detection by employing Schur complement dec
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