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Robotics roadmaps from around the world spotlight of the month: China
China is targeting the next industrial revolution by doubling down on embodied AI and humanoid robotics, building directly on its vast manufacturing infrastructure. By Ellen H. Rumley and Allison Okamura Beyond the Robot Olympics Chinese robots have quickly entered global public consciousness in recent years, appearing as background robo-dancers in pop concerts and as humanoid [ ]
Jev AI Claims 193.6x Faster. I Checked the Math and Got 1.19x
A week after TypeSafe AI launched Jev, the number attached to it almost everywhere is 193.6x. TypeSafe puts it on its home page next to Continue reading on Medium »
I Stopped Building AI Chatbots… Then One AI Agent Changed Everything
The biggest mistake I made wasn t ignoring AI.Continue reading on Medium »
Jev: The Decision Layer AI Agents Have Been Missing
Why TypeSafe s System One approach could add a specialized decision layer between LLMs and the software that has to act on their outputContinue reading on Medium »
We Chose Restraint with the Bomb. With AI, We Stepped on the Gas.
Why Cold War game theory kept us alive, and why that same wisdom completely fails in the age of autonomous intelligence.Continue reading on Medium »
‘We’re already fighting yesterday’s battle’: Greece’s prime minister gets candid about AI
Most leaders on a trade mission stick to the pitch, but when I interviewed Greek Prime Minister Kyriakos Mitsotakis this week, he also admitted that no government is ready for what AI is about to do.
Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach
arXiv:2609.25082v1 Announce Type: new Abstract: Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-frontier candidate beyond purely classical approaches. Hybrid quantum-classical models operationalize this potential by embedding a parameterized quantum circuit within a model where all other components remain classical-a design already applied to chemistry simulation, financial modeling, and image classification. However, their deployment in privacy-sensitive, multi-party settings is constrained by the need to avoid centralizing raw data and by the requirement that modern quantum circuits remain parameter-efficient to stay trainable at scale. In this paper, we address these constraints by evaluating federated learning (FL) as a means of combining a hybrid quantum-classical active party with a classical passive party, using Sherpa.ai's Blind Vertical FL (SBVFL) protocol to avoid centralizing
Cosserat Modeling of Trimmed Helicoid Soft Arms with a Separated-Section Constitutive Law
arXiv:2609.25264v1 Announce Type: new Abstract: Cosserat rod models for soft robots usually construct sectional stiffness by summing material properties over a common cross-section. This assumption becomes inaccurate for trimmed helicoid arms, where load-bearing helix domains are separated and connected only through sparse fused crossings. This paper formulates a separated-section constitutive law that evaluates each helix domain in its local frame and pulls its constitutive response back to the backbone, yielding an effective backbone stiffness. Sparse-fusion mechanics captures the additional compliance caused by relative motion between neighboring domains and determines channel-wise reduction profiles $\eta_c(s/L)$ for bending, torsion, and extension. The resulting effective sectional stiffness is strongly anisotropic: bending and extension are reduced by about one order of magnitude, whereas torsion remains close to the effective backbone stiffness. The resulting sectional law is em
Didactic knowledge or Clinical Cases? How Data Types Shape Medical Large Language Models
arXiv:2609.22161v1 Announce Type: new Abstract: Medical large language models are commonly trained on mixtures of didactic data (e.g., textbooks) and clinical data (e.g., patient records), yet how these data types differentially shape model capabilities remains unclear. We address this issue with token-matched experiments that vary the didactic-to-clinical ratio and analyze how data composition affects performance, capability profiles, and error patterns across knowledge-intensive and clinic-oriented tasks. We uncover an asymmetric transfer across task types: clinical data improves clinic-oriented tasks while remaining competitive on knowledge-intensive ones, whereas didactic data mainly improves knowledge-intensive tasks. Error analysis suggests a knowing-doing gap, where improvements in knowledge recall do not reliably generalize to clinical reasoning. We further observe that modest amounts of clinical data yield most of the gains on EHR-grounded tasks, while the optimal mixture rati
An Affordable AI-Integrated Smart Cane for Multimodal Mobility Assistance of Visually Impaired Users
arXiv:2609.22277v1 Announce Type: new Abstract: Visual impairment affects over 2.2 billion people worldwide, yet conventional white canes cannot detect elevated hazards or provide semantic environmental context. Existing AI-assisted navigation systems typically rely on expensive hardware or cloud connectivity, limiting accessibility in resource-constrained settings. This paper presents an affordable (\$88 USD), fully offline AI-integrated smart cane designed for multimodal mobility assistance on an ultra-low-power Raspberry Pi Zero 2W. The system fuses RGB vision sensing with Time-of-Flight (ToF) distance estimation, pairing an INT8-quantized SSD MobileNet V1 model with distance-aware vibrotactile feedback and real-time audio alerts. To ensure operational robustness on constrained hardware, a multiprocessing architecture isolates sensor acquisition, neural inference, and haptic feedback into independent processes with fail-safe sensing support. Experimental evaluation across indoor mob
Towards Adaptive Interaction Strategies for Human Companion Robot via Deep Reinforcement Learning
arXiv:2609.25031v1 Announce Type: new Abstract: In the field of Human-Robot Interaction (HRI), achieving flexibility in human-accompanying within real-world environments holds great potential for various applications but also poses significant challenges. Traditional methods typically restrict robots to fixed positions relative to humans, such as tracking from behind, in front, or side-by-side, which limits robot adaptability in dynamic workspaces. This study introduces a novel human-companioning strategy that uses Reinforcement Learning (DRL) to enable mobile robots to dynamically adjust their tracking positions according to varying conditions. An interaction space is defined to capture the relationship between the human and the robot while considering the environment, which serves as the basis for state spaces in DRL to assist the robot in adapting to environmental changes. A human-robot companion controller is developed by integrating Model Predictive Path Integral (MPPI) control wi