A live discussion titled "Can Dems Take the Senate?" is available via Prof G Media on Substack. The event features candidate Josh Turek from Iowa (D-IA) alongside Jessica Tarlov. Viewers can access and watch the full video content at the source. No further details regarding specific talking points or outcomes are provided in this announcement.
Generative AI tools are permitted for developing and exploring ROS software, but contributions made on platforms like Discourse or GitHub must retain a human element. Contributors must fully own and defend every line of submitted code, as maintainers cannot act as validators for LLM output. Submissions containing hallucinations, such as non-existent APIs, will be immediately rejected. Furthermore, while disclosing the use of AI is required, PR descriptions must be concise, human-authored explanations of changes rather than generic AI text. Maintainers reserve the right to close submissions deemed low-effort, unverified LLM dumps or responses that fail to address technical critiques personally.
A daily newsletter called TLDR AI provides a concise digest of top artificial intelligence breakthroughs. It summarizes news, research papers, and new tools in an easy-to-read, five-minute format. With 1.1 million subscribers, it is noted as one of the world's largest AI newsletters, delivered every weekday. The content is highly dense and engineering-focused, appealing particularly to engineers and researchers. This technical depth distinguishes it from more general or "how-to" oriented AI summaries.
Mecka AI acquired Vancouver-based Docula in 2026 to enhance its capability to process massive amounts of human motion data. Mecka's business involves collecting physical, three-dimensional movement data from contributors across numerous international locations using specialized cameras and iPhones. This raw data requires extensive processing to become usable for training humanoid robots and robotic arms. Docula provides the necessary AI engine to ingest, normalize, validate, and format these petabytes of complex motion records at high speed. The acquisition addresses the critical "data infrastructure problem" facing the development of physical AI systems.
Firefly Aerospace successfully tested an NVIDIA Jetson onboard a satellite placed into lunar orbit. The mission marked the first time this specific hardware platform operated in such an environment. Utilizing the Jetson's capabilities, researchers conducted real-world testing of its embedded processing power outside Earth's atmosphere. This demonstration is crucial for validating edge computing solutions for deep space exploration. Success paves the way for more sophisticated AI and autonomous systems on future lunar missions.
Sonair has achieved safety certification for its ADAR One 3D ultrasonic sensor. This certification confirms the device's functionality as a human detection sensor. Assessment was performed according to the IEC 61496 standard, which governs electrosensitive protection devices. The announcement of this safety clearance appeared on The Robot Report.
Brennand Pierce, founder and CEO of Kinisi Robotics, addressed the company's strategy regarding physical artificial intelligence. He also discussed the recent acquisition of Kinisi by Bear Robotics. The insights were originally published on The Robot Report platform.
Figure 02, a preceding robotic model, successfully assisted in manufacturing over 30,000 BMW X3 vehicles during an eleven-month period in South Carolina. Following these successful deployments, the BMW Group has now put Figure 03 into service. This transition marks the deployment of the newer humanoid robot model after it underwent testing phases. The initial report detailing this advanced deployment appeared on The Robot Report.
MBody AI has expanded its service robotics operations across numerous locations. It recently deployed its hardware-agnostic Orchestrator platform into Florida and California. Furthermore, the company initiated a pilot program in Ontario, Canada. This expansion brings MBody AI's services to eleven states within the United States.
Hypoid gearing provides engineers with a balanced solution for right-angle gearing that often forces trade-offs between torque capacity and efficiency. It offers a favorable combination of mechanical efficiency, load-carrying capability, and a suitable single-stage ratio range. These attributes provide an advantage when compared to traditional alternatives like worm, bevel, and bevel-planetary gears.
Describes an architectural pattern for managing autonomous drone navigation in PX4's Offboard mode using ROS 2 (Jazzy) and Micro XRCE-DDS. A key challenge is that PX4 requires a continuous stream of setpoints at $>2\text{Hz}$ before accepting the transition to Offboard mode. The solution avoids blocking code, such as `std::this_thread::sleep_for()`, by using an rclcpp::Node wall timer. This timer drives a state machine, which first publishes dummy setpoints for one second to satisfy PX4's failsafe requirement before sending the actual Offboard and Arm commands. The resulting system maintains constant publishing at $20\text{Hz}$ without halting the ROS 2 executor thread.
New documentation and options are available for axis conventions within the unreleased `pyspacemouse` library. Users can currently access this by installing directly from GitHub. The long-term goal is to switch the default axis convention to HID. This change aims to eliminate the current "insane" LEGACY convention that requires manual fixes from users. PySpaceMouse itself is a cross-platform Python library for 3DConnexion SpaceMouse devices.
Today's ROS Discourse General group meeting is canceled due to insufficient member attendance. A follow-up session is scheduled for Monday, July 13, 2026, from 4:00 PM to 5:00 PM UTC. During this upcoming meeting, the group will collaboratively edit an AI-generated first draft of the Logging and Observability guide. This initial draft was created using a previously established skeleton and transcripts from past recordings. No recording is planned for this editing session due to the expected amount of reading and work.
Proposes a standardized interface for translating large language model (LLM) outputs into robotic actions across various platforms. The system defines 12 standard motion primitives, mapping natural language commands like "grasp" or "navigate_to" to specific ROS/2 topics and message types. This approach aims to solve the current problem where different robotics companies use inconsistent interfaces for the same tasks. Integration requires robots to adhere to publishing and subscribing on specified topics for components like arms, grippers, and base movement. Developers are seeking community feedback on the sufficiency of the 12 primitives and potential improvements over existing ROS/2 mechanisms.
Dürr is constructing a turnkey paint shop for Volkswagen Autoeuropa alongside deploying a comprehensive software solution. This project integrates advanced application and drying technologies specifically to maximize CO2 reduction efforts. A key component is the innovative SCADA integration, which unifies monitoring across three distinct paint shops into one system. The overall goal is efficiency improvement while aiming to process 236,100 vehicles in 2024.
Hirebotics launched its first explosion-proof collaborative robot (cobot) solution for industrial painting. This system utilizes Hirebotics' no-code Beacon platform with Fanuc CRX-10iA/L Paint hardware. The new Cobot Painter offers metal fabricators and manufacturers a practical alternative to traditional methods. It is designed for high-mix, low-volume production environments. This development aims to improve upon previous limitations in automated painting applications.
Agibot, a global leader in embodied AI, reached a significant production milestone with its 15,000th robot. The newly produced unit is the Agibot G2, an industrial-grade embodied task robot. This model is specifically designed for operational use in both industrial settings and real-world scenarios. Achieving this mark follows previous milestones at the 5,000th and 10,000th units rolled off the line. The overall development emphasizes Agibot's increasing capability in robotics production.
Modern robotic pool cleaners' performance evaluation is shifting beyond mere suction power and debris collection capacity. Navigation capabilities are becoming increasingly critical to overall cleaning efficiency. Robots with random movement patterns can leave certain areas unclean or repeatedly clean already serviced zones. Advanced navigation systems ensure comprehensive coverage throughout the entire pool area. This improved mobility ensures consistent and thorough cleaning performance.
Digital twins offer risk-free simulation for autonomous driving but conventional methods incur high computing and communication costs due to constant data synchronization. This research proposes a query-driven DT architecture where the twin actively requests necessary environmental data based on its simulation outputs. An optimization problem is formulated to minimize positioning error while managing DT fidelity and strict communication limits. A progressive, cross-time-step query mechanism is introduced to further boost efficiency. Simulation results demonstrate that this approach reduces communication overhead by 40% and improves planning accuracy compared to traditional methods.
A controlled diagnostic protocol was developed to study how event-conditionings organize latent physical structure within passive object-state world models. Testing with free-motion, collision, and occlusion events, the research evaluated recurrent, attention-based, and state-space transition models. Results show that event contexts systematically reweight kinematic, contact, and object-permanence field readouts based on the current physical regime. Furthermore, causal field effect analysis demonstrated that suppressing specific field directions degrades event-relevant prediction, particularly for contacts during collisions. Findings support event-conditioned organization and functional sensitivity of these latent physical fields without requiring explicit, isolated physical modules.
Robot initiative poses a significant challenge in human-robot collaboration because unprompted contributions can disrupt coordination, while waiting for direction might cause missed opportunities. Researchers tested this using a collaborative escape room with two models: reactive (responds only when addressed) and proactive (listens continuously and contributes autonomously). Results showed the proactive model increased interaction frequency, but the reactive model achieved a higher overall success rate in the study setup. Notable variations appeared based on user characteristics; those with prior LLM experience were faster in the reactive condition. Ultimately, robot initiative's impact is determined by a complex interplay of user experience, personality, and group needs.
Simultaneous localization and mapping (SLAM) is a critical task for mobile robots, often relying on Extended Kalman Filter (EKF)-based algorithms which suffer from divergence due to nonlinearities and linearization errors. This research addresses this limitation by applying an effective transformation using a simple compass. The transformation successfully converts the non-linear state space model into a linear one. By utilizing the standard Kalman Filter (KF) on this linearized model, a new method called LMKF SLAM is proposed. Experiments demonstrate that LMKF SLAM surpasses existing methods in accuracy, convergence, and computational efficiency while also exhibiting improved stability against sensor uncertainty.
Humanoid control faces challenges from dynamics mismatch when operating in different environments or with payloads. To address this, FADA introduces a three-stage Planner-Inverse Dynamics Model (Planner-IDM) framework for few-shot adaptation. Initially, an oracle policy trains the system, and its behavior is distilled into the deployable IDM student using DAgger. At deployment, only the IDM is fine-tuned using minimal target-domain data—specifically, pairs of actions and observations from a brief rollout. This method aligns the action generation with the actual dynamics observed in the target domain, significantly outperforming existing adaptation techniques on real humanoid robots.
Embodied foundation models are increasingly used for robot generalization, often employing lossy efficiency techniques like quantization. Current efficient-inference studies inadequately account for the unique closed-loop effects inherent in repeated environmental interactions of embodied tasks. The paper proposes TISED, an analytical framework that unifies various inference optimization methods and decomposes their impact on static versus dynamic tasks. Research reveals paradoxical findings: optimization can sometimes increase overall completion time for static tasks while decreasing per-step latency. Furthermore, moderate lossy optimization may unexpectedly boost task success rates in dynamic settings, with these effects being sensitive to hardware configuration.
Developed a robotic arm system using spectral sensing to non-destructively measure strawberry sweetness, addressing limitations of conventional sampling methods. The system integrates perception via YOLOv11s detection with precise target localization through RGB-ToF calibration and custom hand-eye calibration. A closed-loop pipeline guides the robot to position the sensor optimally for measurement after detecting a strawberry. Experiments demonstrated strong end-to-end performance, achieving 88.10% success across 42 trials, alongside robust detection rates. This research establishes a scalable baseline for integrating perception and robotic manipulation into agricultural quality assessment tools.
Physical caregiving robots struggle due to systems being tightly coupled to specific environments and rigid robot designs. The proposed $E^2$-CARE framework addresses this by enabling context-aware adaptation for varied physical assistance. It achieves this using interaction templates representing primitive skills, which are reshaped online during execution. $E^2$-CARE models the environment, robot, and human within a unified 3D dynamic scene graph to explicitly capture interaction contexts. This allows safe, zero-shot reuse of skill templates across diverse settings and different robotic embodiments, validated in simulations and user studies.