Robotics

DiscloAI is an open-source SDK for EU AI Act Article 50 compliance, enabling chatbot disclosures, deepfake labels, and AI content notices. It supports 24 EU languages and WCAG 2.1 AA, and can be integrated in under 10 minutes via CDN or npm.

Mistral AI is renaming its chatbot Le Chat to Vibe and bundling chat, coding agents and a new Work Mode under one brand. The Work Mode docks onto Google Workspace, Outlook, Slack or GitHub and processes tasks such as emails, reports or pull requests independently. The Pro tariff has been reduced from €17.99 to €14.99, although Mistral has not specified any concrete usage limits. The company is thus positioning itself more directly against the agent-based offerings from OpenAI, Google and Anthropic.

NBA Commissioner Adam Silver announced plans to introduce an automated AI and camera-based system for objective officiating decisions like out-of-bounds calls. The system, compared to Hawk-Eye in tennis, aims to determine possession instantly. Silver said referees will still handle subjective calls involving contact and fouls.

Money Printer Pro is an open-source AI content generator powered by Google Gemini and VEO 3.1, enabling photorealistic images and cinematic videos with identity preservation. It features 7 visual engines, autopilot batch generation, AI quality scoring, and a publish guard. Users pay Google directly with no markup or subscription.

Superpowers is a complete software development methodology for coding agents, built on composable skills and initial instructions. It emphasizes test-driven development, design-first approach, and subagent-driven iteration, supporting multiple coding assistants like Claude Code, Codex CLI, and Gemini CLI.

The Vatican's new encyclical by Pope Leo XIV defends human imperfection as a source of dignity and warns against outsourcing core human capabilities to AI, countering Silicon Valley's dismissal of human limitations.

Researchers propose a real-time asynchronous event-based monocular odometry for planetary rovers, using an Error-State Kalman Filter to process event camera data for robust ego-motion estimation under high dynamic range lighting and computational constraints.

This paper presents a transformer-based architecture called Trinity that jointly performs class-specific semantic segmentation and class-agnostic terrain segmentation in a unified network. It segments terrain regions based purely on visual appearance without predefined labels or robot-dependent traversability scores, enabling robot-agnostic visual terrain priors for downstream tasks. The authors extend the OAISYS simulator to create the RUGDSynth synthetic dataset and provide the EXTerra real-world dataset. Experiments demonstrate the approach's effectiveness in complex outdoor environments.

Many children face challenges in emotional regulation and social interaction, limiting their participation in therapeutic programs. This study explores engagement strategies for a tactile robot supporting children with anxiety disorders, comparing synthetic emotional feedback and point rewards. A preference study with 16 school children (ages 6-8) showed preference for emotional engagement, while a behavioral study with 14 university students (ages 20-27) found point-based systems yielded higher task accuracy (p<0.05) and sustained performance. These findings highlight age-related differences and the need to validate design assumptions through observed interaction.

A new benchmark called What-If World tests video generation models' causal reasoning by presenting paired prompts that differ in one physical detail and checking if videos diverge correctly. Evaluating nine state-of-the-art models, none exceed 52% on paired scores, with open-source models around 28%, indicating significant room for improvement. Performance correlates with visual prominence rather than physics tractability.

This paper presents a behavioral-level activity recognition method using head-mounted IMU, going beyond basic motion primitives. The authors define five behavioral categories, construct a 160K-sample dataset from Ego4D with a four-tier quality assurance framework, and propose HiT-HAR, a 703K-parameter hierarchical model that outperforms prior models on action and scenario recognition. Observability analysis reveals locomotion is reliably observable, while object transfer and task operation benefit from temporal context; scenario-dependent signal overlap remains a challenge. Results show that architectural choices exploiting temporal context and scenario structure outperform simply scaling model size.

Simple Wearable Report turns Oura data into a lab-style report. The free tool provides an option to upload to chatbots, allowing further AI analysis. Here's how I've been using it.

Pope Leo XIV's AI encyclical Magnifica Humanitas correctly identifies issues like algorithmic bias, water use, and data sovereignty, but fails to address AGI and catastrophic risks, offers no concrete solutions to mass unemployment, and is criticized as outdated and disappointing.

An eye exam produced a good distance prescription but a terrible computer prescription. Here's how AI helped decode the numbers and expose the mismatch.

LWN's article discusses the problem of AI scraper bots, highlighting how they infringe on website content and exploring countermeasures.

This paper presents a decentralized approach called R2P2 for collaborative box transport by multiple robots across flat, uphill, and downhill terrains with varying friction. Robots are assigned roles (push, support, prevent) based on rules and use proportional velocity control, reducing communication and synchronization needs. Evaluated in simulation with six robots and validated physically with four turtlebots moving a 1.2 kg box, R2P2 outperforms virtual-leader-follower methods in success rate.

Teleoperation is key for robot data collection, but novices often produce suboptimal demonstrations. The DQAF framework provides immediate post-episode feedback to improve quality.

RCSP is a predictive planning layer that addresses the near-miss commitment problem in mobile robot navigation by evaluating candidate commands against plausible short-horizon obstacle futures. Simulations show it enhances safety and path quality but adds latency, revealing its role as a complementary module for existing navigation stacks.

A framework for heterogeneous robot collaboration under bandwidth constraints, using β-Sparse Gaussian Processes for task-aware point selection and balancing exploration, achieving 18% path cost reduction and 76% information reduction in simulations.

The paper proposes Lie group embedded dynamical neural networks (LieEDNN) that leverage adjoint action on Lie algebra to overcome incompatibility with addition and non-Euclidean dynamics, enabling stable learning on manifolds. Experiments on SE(3) for telescopic manipulators validate the approach.

Hyper is an AI-powered personal knowledge management tool that integrates context from apps like Notion and Obsidian to provide intelligent assistance. The founders previously built robots at Matic and attempted to fine-tune GPT-2 in 2020; now they have launched a self-serve version.

This post demonstrates AgentWatch, a proactive AWS monitoring solution that checks infrastructure every 15 minutes, summarizes CloudWatch metrics, logs, and alarms across multiple accounts, and delivers actionable reports to Slack. It responds to natural language queries and implements three human-in-the-loop patterns to balance automation with oversight.

This episode of The Good Robot explores how feminist principles and decentralized infrastructure could transform cloud infrastructure from a corporate service into a public commons. Friederike von Franqué, policy advisor at Wikimedia Germany, discusses examples from Frankfurt's energy-intensive data centres to Stockholm's municipally owned fibre network, advocating for environmental accountability and community-driven design.

BobCA is a sovereign agent that learns to code with your preferences.

Robotic assistants in long-term human-robot collaboration need to assist users under partial observations while leveraging cross-day interaction history. Since human traits are often unknown initially, passive infer-then-act is ineffective. We propose PACT, an ask-or-act framework that evaluates contextual sufficiency to decide whether to seek clarification before acting. Using reinforcement learning, PACT improves assistance accuracy and clarification utility over passive baselines in multi-day embodied scenarios.

Planetary rovers face mobility challenges on varying terrains. Researchers introduce a multimodal wheel that continuously adjusts grouser height. In 750 trials across four surfaces, adaptive deployment reduced slip by 30-58% and improved travel time and energy by up to 77.4% on granular terrains, highlighting limitations of fixed wheels.

This paper proposes a reinforcement learning framework that modulates a constant reference trajectory to perform compact, position-constrained quadrotor inversions while remaining compatible with traditional trajectory generation and tracking. In simulation, the method reduces position RMSE by 32% and settling time by 57% relative to the strongest optimization-based baseline. Hardware experiments demonstrate successful inversion across multiple yaw configurations with position RMSE below 0.35m.

A retrofit of a custom series elastic element to a black-box actuator improved force control bandwidth from 10.32 Hz to 30.32 Hz (2.93x), outperforming a commercial sensor by 7.63% at a cost of 25 GBP.

A new visual navigation method called MASt3R-Nav uses pixel-relative connectivity to build geometrically accurate maps without requiring global consistency, enabling more capable navigation than traditional topological graphs.

The Ecovacs X8 Pro Omni won the ZDNET Lab Award for best pickup performance in a robot vacuum, and it's currently $67 off for Memorial Day.

Google Deepmind's AlphaProof Nexus has autonomously solved nine open Erdős problems, including two that stumped mathematicians for 56 years, for just a few hundred dollars per problem in inference costs. Unlike OpenAI's natural-language approach, the system uses the Lean compiler to verify every proof step automatically. Still, the overall success rate sits at just 2.5 percent.

Sam Kriss fiercely criticizes the proliferation of AI-generated text, which he finds empty and homogenized. Through his experience searching for a caterer online, he illustrates how AI writing produces generic, meaningless content that lacks real information. He argues that even if AI could write well, a world with only one literary voice would be a nightmare. Kriss emphasizes that AI writing is fundamentally gibberish, easily detectable, and warns that those who rely on it will be caught. He also mentions AI's mathematical achievements but notes its failure in expressing human emotions.

We present SAGE, a system for open-vocabulary exploration in unknown 3D indoor environments that preserves coverage-oriented behavior while allowing semantic cues to reprioritize frontier selection. In simulations, SAGE outperforms baselines in object discovery and achieves 13.7x speedup over FTU. Real-world drone flights confirm its effectiveness.

This paper presents four progressively complex state estimators for legged robots that use foot-contact information to mitigate IMU drift, including a contact-aided invariant EKF, factor graph, fixed-lag smoother with contact-episode footholds, and a variant with evolving IMU bias. Implementations are available in GTSAM and ROS2.

Researchers propose a method to certify reachable Cartesian steps under joint limits, achieving zero violations and 100% goal reaching in adversarial scenarios.

Robots learning reward functions from demonstrations often suffer from underspecified features due to imperfect demonstrations. This paper proposes a framework that detects underspecified features by analyzing variation across demonstrations (low variation indicates well-specified, high variation indicates underspecified). The robot then explains its uncertainty in natural language and requests targeted corrective demonstrations. Evaluations in simulation and with a real Franka robot show that explanation-guided queries significantly improve reward recovery over random querying and passive data collection.

Event cameras are well suited for visual odometry under high-speed motion and challenging lighting conditions due to their low latency, high temporal resolution, and high dynamic range. Deep Event Visual Odometry (DEVO) demonstrated that monocular event-only odometry can achieve strong performance by combining sparse patch tracking, learned patch selection, recurrent correspondence refinement, and differentiable bundle adjustment. In this project, we extend DEVO with a sparse point-cloud export pipeline. Rather than modifying the core odometry formulation, our approach exposes the internal 3D structure already estimated by DEVO and converts it into an explicit point-cloud representation for visualization and further processing. In addition, we implement a practical workflow for data export, format conversion, and point-cloud cleanup. The resulting system preserves the original visual odometry pipeline while enabling sparse geometric scene output. Experiments on the BOARD SLOW sequence show that the exported sparse cloud is locally consistent with EMVS reconstructions, achieving high precision at a 5 cm threshold, while also highlighting the expected limitations in density, completeness, and sensitivity to accumulated odometry noise.

GEM-4D is a geometry-grounded video world model that improves robot manipulation by injecting dense 4D correspondence supervision distilled from a pretrained geometry foundation model. It jointly captures appearance and geometric structure without additional inference cost. An inverse dynamics module converts consistent video rollouts into executable robot trajectories. GEM-4D achieves state-of-the-art performance on video prediction and geometric consistency, boosting real-world manipulation success from 61% to 81%.

FusionSense is a fusion-aware intelligent sensing framework for energy-constrained autonomous edge systems. It uses a three-step training procedure to create lightweight near-sensor classifiers that jointly reduce compute and communication while scaling linearly with sensor count. On a SynDrone dual-modality setup, it achieves up to 33x lower energy at 1% FoI prevalence, 92.3% reduction in quality loss at 30% data reduction, and 1.5x higher energy savings than prior baselines.

Memorial Day Weekend is here. As ZDNET's deals editor, I've curated the best offers from major retailers, featuring ZDNET-tested products including Apple, Samsung, Hisense, Ninja, and more. Save on tech, home, outdoor gear, and more.

South Korea's Deputy Prime Minister Bae Kyung-hoon stated that wealth generated by AI must benefit the public, expressing concerns about inequality and job losses. He referenced recent labor conflicts at Samsung Electronics as part of broader AI era trends and emphasized building an "AI-inclusive society." He also commented on market concentration and industrial robot integration.

A Florida community has deployed AI-powered robotic beehives to monitor hive health and protect bees from threats, claiming a 70% reduction in colony collapse, which is crucial since bees pollinate most crops.

Pope Leo XIV's new encyclical on AI will be unveiled with Anthropic co-founder Chris Olah, sparking debate. The Vatican's decade-long engagement with tech and Anthropic's collaboration with Catholic ethicists aim to shape ethical AI, though critics warn of 'pope-washing'.

This is one of the best times of the year to buy a robot vacuum, thanks to these Memorial Day deals.

Bees and other pollinating insects play vital roles in food webs and crop pollination, yet monitoring them has proven difficult. That’s why researchers have developed a radar system that could lead to a cost-effective, non-invasive way to track pollinators.

AI-DECLARATION.md is an open specification that enhances transparency by declaring the degree of AI involvement in software projects. It defines six levels from no AI to fully autonomous, with optional granularity for processes like design, implementation, and testing. The standard aims to establish a trustworthy social contract in the developer community.

Google's AI Overviews are malfunctioning: searching for terms like 'disregard' triggers chatbot-like responses instead of summaries. Google is aware and working on a fix.

A recap of the 2026 I/O Dialogues, where leaders discuss the future of AI, quantum computing, robotics and creativity.

This paper presents an analytical and experimental force analysis of a linear soft sleeve actuator (LSSA) for wearable robotics. A quasi-static model was developed and validated with experiments showing force decreasing from ~112 N at zero extension to near zero at 40 mm under 125 kPa. Static loading delays and reduces force output.

This paper presents a distributed multi-coverage algorithm for drone swarms that ensures redundant coverage of critical assets using only local sensing and communication, without global coordination. It is accepted at ANTS 2026.