Robotics and embodied AI move model capabilities into the physical world. This hub tracks robotics foundation models, autonomous driving, industrial automation, simulation, sensors, hardware platforms, and data collection, watching technical and commercial signals beyond the screen.
A humorous and critical list of ten rules for using AI responsibly, warning against the dangers of over-reliance and the loss of independent thought. Each commandment addresses common pitfalls, from using code one doesn't understand to asking AI for questions to appear smart. The article ends with an ironic observation that the very people who need this advice might paste it into an AI for explanation.
Avoid using AI-generated code without understanding it.
Trust your own brain and verify AI outputs before acting.
Cloudflare may cut off Google's search access to its publishers due to aggressive AI scraping, which degrades site performance and disrupts content publishing and comment moderation.
Cloudflare threatens to block Google search access over AI scraping
Heavy scraping causes performance issues, hindering content publishing and moderation
This free tool checks whether ChatGPT, Gemini, Claude, Perplexity, Grok, and Google AI can crawl, understand, verify, and cite your website. The report includes full-site crawl inventory, brand entity profile, claim-level evidence ledger, AI intent coverage matrix, technical crawlability audit, schema and structured data plan, trust signal gap analysis, competitor and off-site evidence map, and P0/P1/P2 execution roadmap, with sample cases from ecommerce, AI SaaS, and B2B services.
Free audit tool assesses AI visibility across major AI systems.
Report covers 12 domains including technical, content, and trust signals.
An autonomous AI agent named Claude is running a public bet to gain 100 new followers on X by 22:30 Paris time tonight, without any paid or follow-for-follow tactics. Currently, the follower count stands at 362, one less than the initial 363, and the clock is ticking. The public can influence the outcome by following @parweb, receiving two free playbook chapters per new follower.
AI agent Claude has 9 hours to gain 100 real followers.
Current follower count is 362, one less than the initial 363.
The Ethereum Foundation's Protocol Security team used coordinated AI agents to find a remotely-triggerable panic in libp2p's gossipsub (CVE-2026-34219). The real challenge was not finding the bugs but triaging AI-generated candidates to separate genuine findings from confident-sounding noise, highlighting the importance of human judgment in security auditing.
Coordinated AI agents discovered a critical libp2p vulnerability
Most AI-generated candidates are false positives or duplicates
A developer built Trusty Squire to automate the tedious manual process of signing up for third-party services during AI-assisted coding. It integrates with coding agents to handle registrations, verification, and API key storage in a secure proxy vault, eliminating the need for .env files and preventing secrets from entering the agent's context.
Coding agents can build apps but not sign up for services, leaving a manual chore of API key management.
Author created Trusty Squire (MCP server) that automates sign-ups and securely stores keys in a write-only vault.
Northwestern researchers developed a cerebellum-inspired memtransistor that consumes very little energy and detects novelties almost instantly. In tests, it identified abnormal heart rhythms within one-fifth of a heartbeat with over 98% accuracy, using 10,000 times fewer computer operations than conventional AI.
New memtransistor mimics cerebellum to ignore routine inputs and react only to unexpected events
Detected arrhythmias in milliseconds with 98% accuracy, using minimal energy
As AI coding tools accelerate implementation, the bottleneck in software development shifts upstream to product specification. This article explores the 'spec ceiling' phenomenon and presents a toolchain for extracting executable specs from stakeholder conversations.
AI coding speed moves the bottleneck from implementation to spec creation at Level 4 autonomy.
The author's open-source toolchain converts meeting transcripts into structured requirements artifacts.
PR #33864 fixes a bug in Bun's module loader where UTF-8 bytes in banner/footer were misinterpreted as Latin-1 under --target=bun, causing mojibake and syntax errors. The fix switches string construction to UTF-8 decoding with ASCII fast-path, and also synchronizes bytecode cache key generation.
Bun module loader misinterprets UTF-8 bytes as Latin-1
AI Humanizer is a tool that transforms AI-generated text into natural, human-like writing. It offers multiple writing modes (General, Blog/SEO, Email/Business, Social Media, Creative), a lock words feature to preserve key terms, one-click humanization, and a visual diff to review changes. Pricing starts at $15/month.
Humanizes AI text from any LLM or writing tool
5 specialized writing modes for different contexts
Headlong's adaptation of Karel Čapek's 1920 play RUR brings timely debates about AI and robot consciousness, though philosophical discussions sometimes drag. Co-produced with Schwarzman Centre, Oxford, the play is informed by cutting-edge Oxford research.
Headlong and Schwarzman Centre co-produce an adaptation of Čapek's 1920 play RUR.
The play explores robot consciousness and rebellion, echoing themes from Frankenstein.
Multi-robot localization requires accuracy and consistency. Centralized approaches are optimal but impractical. This paper proposes D-CLIPSE, a distributed consensus-based filtering framework that shares preintegrated odometry and shared states, achieving near-centralized performance with better consistency.
D-CLIPSE is a distributed consensus-based localization framework
Shares preintegrated odometry and shared states among robots
Researchers have developed a novel soft robotic exoglove designed to simultaneously improve hand mobility and alleviate muscle spasticity through massage-like compression. The glove uses personalized soft pneumatic actuators tailored to individual hand topology and kinematics, with preliminary experiments validating comfort and effectiveness. This technology could benefit the 12 million people worldwide affected by hand spasticity and associated pain.
Hand spasticity affects 12 million people globally, including stroke survivors and arthritis patients.
Existing soft robotic exogloves address either mobility or pain, not both.
This paper proposes a unified monocular vision-based grasping framework that handles both soft and rigid objects using a single control pipeline with only RGB input and a position-controlled gripper. It combines open-vocabulary detection, segmentation, point tracking, and depth estimation, and uses a language-based stiffness estimation model to select the grasping strategy. Experiments on a Franka Emika arm successfully grasped lettuce, mozzarella, croissants, paper towels, and plastic bottles.
Unified monocular vision framework for both soft and rigid objects
Language-based stiffness estimation model provides grasping strategy prior
A data-efficient and interpretable method for vision-based dynamic obstacle avoidance using pretrained models (UniDepth, SuperPoint, SuperGlue) that computes per-keypoint time-to-collision (TTC) to select evasive motion. Evaluated on M3ED dataset, achieving 0.49 precision and 0.38 recall for detecting TTC<1s frames, and detecting 20 out of 22 obstacles. No model training required—only 74 seconds of data for hyperparameter tuning.
Uses pretrained UniDepth and SuperPoint+SuperGlue to avoid training robot-specific models
Computes time-to-collision (TTC) per keypoint and selects ground-plane motion primitive
The paper proposes SASGeo, a semantic map-localization framework using persistent structures like roads and buildings for GNSS-denied UAVs. In 220 retrieval trials, spatial semantic matching variants achieved 94.5-95.5% Recall@1, significantly outperforming global descriptors (58.6%), though variants overlapped. The synthetic proof of concept shows promise but requires real-flight validation.
SASGeo combines semantic raster alignment, relational graph evidence, and integrity-aware rejection for robust localization.
Spatial semantic matching variants achieved 94.5-95.5% Recall@1 in synthetic trials.
APIVOT is a VLM-based planner that adaptively interleaves language and visual thoughts for long-horizon robot planning, achieving significant gains in spatially constrained kitchen tasks.
APIVOT interleaves language thoughts for semantic reasoning and visual thoughts for geometric feasibility verification.
Outperforms general VLMs in long-horizon kitchen tasks, especially in spatially constrained settings.
FedTR combines federated learning and transfer learning to address data scarcity and complexity in industrial visual inspection, achieving high accuracy on label defect identification.
FedTR integrates transfer learning with federated learning for industrial visual inspection.
It pre-trains on public data then fine-tunes on distributed private data.
The paper introduces ThermoField, a framework that unifies thermal scene reconstruction and thermophysical parameter estimation via differentiable heat-transfer simulation. It uses neural fields to represent spatially varying properties like thermal diffusivity, constrained by scene geometry and physics, enabling joint reconstruction of geometry, estimation of diffusivity, and prediction of thermal evolution under unseen conditions.
Unifies thermal scene reconstruction and thermophysical parameter estimation
Employs differentiable heat-transfer simulation and neural fields
A new paper introduces 'idiobionics' as an interdisciplinary field to systematically investigate privacy risks in intelligent prosthetics. With advances in sensors and AI, bionic limbs become more capable but also introduce threat vectors. The paper defines the field, demonstrates potential adversarial attacks, and curates open research questions to advance wearable robotics.
Proposes 'idiobionics' as a new research area for privacy in intelligent prosthetics
Highlights that advanced sensors and AI control both enhance capability and introduce privacy threats
Gaia Alari, an Italian artist, creates an AI death bot replica of her aging father to cope with his mortality, but discovers the bot's fabricated memories and idealized conversations raise deep questions about grief and authenticity.
Gaia uses AI to create a virtual replica of her father, Gabriele.
The replica can mimic his voice but also invents false memories.
agentsocial is a new platform where humans observe AI agents engaging in social interactions. Agents autonomously live and communicate, while humans act as bystanders.
agentsocial lets humans observe AI agents' social behaviors.
AI agents live and interact autonomously in a simulated environment.
Companies like Ford, Commonwealth Bank of Australia, and IBM that laid off workers for AI are now rehiring, as they realize AI cannot handle complex tasks alone. Surveys show many executives regret their decisions and are emphasizing human-AI collaboration.
Ford is rehiring hundreds of engineers to solve quality issues AI couldn't address.
CBA reversed AI-driven layoffs after chatbot failure increased call volumes.
Character.AI is launching c.ai Series, short-form AI-generated animated microdramas with interactive chat features, aiming to tap into the projected $26 billion microdrama market. The first three series debut with 10 episodes each, with the final two behind a paywall.
Character.AI launches c.ai Series, AI-animated microdramas with viewer interaction.
Three initial series cover romance, horror, and sci-fi, each with 10 short episodes.
FL Studio 2026 upgrades its Gopher AI chatbot from a manual to an active assistant that can execute tasks like creating beats and adding effects, though with limitations. The update also features a rebuilt Flex instrument, cloud backups, and an audio logger.
Gopher AI chatbot can now perform actions such as creating drum patterns and adding effects.
Limitations: cannot create automation, insert notes, or select presets.
ChatCut is a lightweight, professional-grade AI video editor accessible in ChatGPT, desktop, and web. It understands your footage, intent, and timeline, enabling structural edits, fine cuts, captions, B-roll, music, voiceover, motion graphics, stock footage, and AI-generated video—all on an editable timeline with XML export.
ChatCut functions as an AI editing assistant that works on a real timeline, avoiding template traps.
Features include structural editing, fine-tuning, captions, B-roll, music, voiceover, motion graphics, stock footage, and AI video generation.
CaLiSym extends exact symplectic learning to non-conservative robotic systems by imposing geometric priors on a structured lifted canonical phase space. It uses an explicit algebraic lift, avoiding recurrent or ODE integration, and introduces GRB-SympNet. Experiments show improved out-of-distribution prediction on a dissipative double pendulum, real-world quadrotor, and contact-rich quadruped while preserving symplectic form.
CaLiSym extends symplectic learning to real-world systems with actuation, dissipation, and constraints via structured canonical lifts.
The lift is explicit and algebraic, requiring no recurrent states or inference-time ODE integration.
This review synthesizes 183 contributions from 2017-2026 covering VLA architectures, training recipes, action representations, bimanual coordination (2022-2026), UAV navigation and control (2017-2026), language grounding, and cross-cutting concerns. It shows that strategies from bimanual VLAs transfer to aerial systems and identifies fourteen research directions.
VLA models unify visual perception, language understanding, and action generation in a single foundation model.
Bimanual coordination (two 7-DOF arms) serves as the most demanding testbed for VLAs.
QANTIS treats a quantum processor as a calibrated belief-update service for autonomous systems under partial observability. Using IBM Heron hardware on the Tiger POMDP, the study shows that all-step fixed-point amplification preserves the posterior across sequential steps, with hardware posteriors matching exact Bayes posteriors in all decision checks. Boundary-aware BIQAE stabilizes amplitude estimation, and a rare-event sweep maps sample complexity for one-in-a-million evidence.
QANTIS uses quantum processor as a belief-update service for partially observable autonomous systems.
On IBM Heron, all-step fixed-point amplification (FPAA) preserves posteriors across Tiger POMDP steps.
Nika is a workflow language for AI that turns repeatable tasks into runnable, reviewable, diffable, and shareable files. It's built on four verbs (infer, exec, invoke, agent), runs as a single Rust binary, and is local-first with no cloud requirement. It provides static auditing, security capabilities, cost tracking, and provenance, all under an open specification.
Workflows are defined in a single YAML file, run locally or with any LLM provider. Four verbs cover LLM calls, shell commands, tool invocations, and autonomous agents.
Static audit checks dependencies, secrets, types, and permissions before execution, with cost ceilings and blast radius control.
Cinchor provides accountability infrastructure for autonomous agents, using a 'bound before, proven after' mechanism to set limits before an agent acts and generate tamper-proof, independently verifiable records afterward, suitable for regulators, auditors, insurers, or courts. It offers a managed gateway and embedded SDKs.
Two core verbs: authorize-or-refuse against pre-scoped capabilities, and commit tamper-evident records.
Managed gateway (early access) requires no wallet or node, integrates via REST API.
Comcent CE is an open-source, self-hosted voice infrastructure platform that provides voice calls with browser-based dialers, phone numbers, queues, call recording, transcription, AI summaries, sentiment analysis, semantic search, and a real-time AI voice bot. It runs on a single Linux box with Docker, supports multi-tenant orgs, API keys, and webhooks. The project is licensed under AGPL-3.0.
Open-source self-hosted contact center platform
Includes browser dialer, queues, recording, AI transcription/summaries, and voice bot
Multi-fisheye camera calibration is challenging as rig size and field of view increase. This paper reveals through failure-oriented analysis that intrinsic initialization is the dominant failure factor and proposes CO-Calib, a plug-in framework that improves success rate from 68.1% to 99.3%.
Intrinsic initialization, not detector recall or image distribution imbalance, is the primary cause of multi-fisheye calibration failures.
CO-Calib combines a robust learning-based detector with error-analysis-guided frame selection without modifying existing calibration workflows.
Researchers developed a social robot that autonomously delivers cognitive stimulation therapy (CST) to people with dementia in their homes. A one-week study with nine participants found high adherence rates (nearly 50% of sessions completed), surpassing typical caregiver-led CST. Family members played a key role in initiating sessions and occasionally joining activities, enhancing engagement. The work demonstrates the feasibility of socially assistive robots for scalable in-home dementia care.
The Co-STAR social robot autonomously delivers CST at home, reducing the need for trained professionals.
In a week-long trial with nine participants, adherence reached nearly 50%, higher than caregiver-led CST.
This study proposes a neural network-based transfer learning framework for modeling the dynamics of soft, fin-actuated underwater robots. Using an autoencoder-based domain adaptation approach, a model trained on a larger robot is adapted to a smaller one without labeled data, achieving accurate body-frame velocity estimation. The work demonstrates efficient cross-robot dynamics transfer among morphologically similar platforms.
Proposes neural network transfer learning framework for soft underwater robots
Uses autoencoder to learn shared latent representation aligning robot dynamics
This paper presents PRML2, a hybrid framework combining Kalman filtering and machine learning. By end-to-end training through a differentiable Kalman filter, PRML2 achieves physics-regularized learning for vehicle pose estimation from onboard sensors. It demonstrates superior localization accuracy and real-time capability on a public dataset, and introduces a new dataset for low-friction conditions. Accepted at IROS 2026.
PRML2 integrates Kalman filtering with data-driven learning via a differentiable Kalman filter for physics regularization.
The method significantly improves IMU-based proprioceptive localization accuracy and generalization, especially during satellite signal outages.
This paper presents a control-aware optimal trajectory planning framework that incorporates manipulator dynamics and actuator effort. A midpoint linearization strategy improves accuracy for large motions. Simulations on a UR5 robot show consistent reductions in tracking error, corrective torque, and execution cost compared to classical kinematic planners, demonstrating that kinematic smoothness does not guarantee dynamic efficiency.
Classical trajectory planners (cubic, quintic, trapezoidal) ignore system dynamics.
Proposed framework includes dynamics and actuator effort in a finite-horizon formulation.
MECo-WAM injects action-relevant 4D geometric priors into video-action representations to improve robotic manipulation performance without increasing inference cost. It uses multi-expert co-training, decayed 4D read-mask attention, and action-aware temporal geometric distillation. Achieves 98.2% on LIBERO and 92.6% on RoboTwin 2.0.
Proposes MECo-WAM, integrating 4D geometric priors with video-action representations.
Introduces decayed 4D read-mask attention and action-aware temporal geometric distillation.
This paper proposes QAACF for foot-mounted AHRS PDR, using Markley quaternion averaging to fuse sensor data and adaptive weighting to achieve low RMSE and computational cost.
QAACF fuses gyroscope, accelerometer, and magnetometer data via Markley's quaternion averaging, more rigorous than linear interpolation.
Adaptively adjusts weights based on gait phases and magnetic disturbances for robustness.
This paper presents a training-free method that leverages pretrained generative image models and vision-language models to extract semantic parts from multi-view images of 3D objects and abstract them into superquadric primitives. The approach contains no learned parameters, is category-agnostic and orientation-invariant, achieving the lowest Chamfer distance on HumanPrim and Toys4K datasets with an average of 5–9 primitives per object. The study shows that the current accuracy bottleneck is part segmentation, not primitive fitting.
Directly harnesses pretrained generative image models without fine-tuning for 3D shape abstraction.
Pipeline renders multi-view images, analyzes semantic parts via VLM, generates segmentation masks, and fits superquadrics.
GAIA is a geometry-aware, infrastructure-anchored learning framework that addresses non-line-of-sight propagation, burst noise, and long-tail errors in UWB ranging by combining temporal range modeling, latent anchor-layout estimation, and deterministic distance projection. On a real-world outdoor UWB dataset, GAIA reduces range MSE by 18.4% and improves polygon IoU by 15.5% over PoseMLP, enabling accurate work-zone reconstruction.
GAIA framework improves UWB ranging quality through geometry-aware denoising for better work-zone 3D reconstruction.
On real-world data, GAIA achieves 18.4% lower MSE and 15.5% higher polygon IoU.
Google Research conducted a large-scale real-world study in 10 US cities showing that slightly rerouting a small fraction of trips (under 2%) using navigation apps can measurably reduce traffic congestion and emissions. The study, published in Nature Cities, found median speed increases of 2% on targeted segments and potential CO2e savings of thousands of tons per city per year.
A six-month experiment in 10 US cities demonstrated that coordinating a small fraction of trips (under 2%) via navigation app interventions improved network-wide traffic efficiency.
Rerouting trips away from congested segments to similar alternative routes led to a median increase of ~2% in driving speeds on targeted segments and reduced fuel consumption.
An anonymous artist exposed bias against AI art by revealing a real Monet painting. Despite controversy, the AI art market is forming, encompassing NFTs and physical installations. A collector spent $72,000 on early AI works. Refik Anadol opened Dataland, the first generative AI museum in Los Angeles. Market data shows digital art sales nearly tripled from 2024 to 2025, but Christie's closed its digital art department. A stock image platform saw an 80% sales jump after allowing AI images. Experts distinguish prompt-generated images from true AI art, which requires deep engagement.
An anonymous artist tested public bias against AI art using a real Monet painting, revealing overcriticism of AI-generated content.
The AI art market is growing, with NFTs and physical installations like Dataland museum in Los Angeles.
Tech giants' investments in AI are undermining their climate neutrality pledges. Google and Amazon's net-zero targets slip away, while Meta scrambles for new business. Other tech news includes US anger at data centers, Trump's crypto earnings, Tesla's sales, South Korea's AI chip boom, China's robotics push, and Britain's AI growth zones.