
Fennec Engineering earns T2 qualification for Advanced Safety Acceleration Platform
Fennec Engineering has worked with clients including Amazon Robotics. Source: Fennec Engineering Functional safety is critical for robots and related technologies to earn trust and adoption, and it must be considered as early as possible in development, according to Fennec Engineering. The company today said it has received independent certification from TÜV Rheinland for its Advanced Safety Acceleration Platform, or ASAP. The year-long Tool 2 (T2) qualification process demonstrated that ASAP me
Fennec Engineering has received independent certification from TÜV Rheinland for its Advanced Safety Acceleration Platform, or ASAP, a software tool designed to help companies build safety into robotics and autonomous systems from the early stages of development. The certification, called T2 qualification, confirms that ASAP meets rigorous international standards for software used in safety-critical development. This matters because functional safety is considered essential for robots and related technologies to earn public trust and adoption, and adding safety features early in development prevents expensive redesigns later. Fennec has already deployed the platform at Amazon Robotics since 2021 and is now launching it for the wider market, with the T2-qualified version now available.
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Can AI agents program industrial robots without training data? Berkeley says yes
The new RoboSimStudio is an open-source project that enables agentic AI coding of robotic simulation environments. | Credit: UC Berkeley UC Berkeley and NVIDIA have released open-source tools for a new approach: enabling teams of AI coding agents to write, simulate, and refine readable robot programs. Since the early days of industrial robots, programming, tuning, and troubleshooting robotic workcells has taken weeks to months. New research coming out of UC Berkeley has demonstrated that new age

Boston Dynamics adds wheels to Atlas humanoid to test whole-body control
Bridging the divide between deterministic physics-based control and policy-driven adaptation, Boston Dynamics has deployed a hybrid locomotion architecture on a wheeled variant of its Atlas humanoid. The “experiment” combined classical model-based motor control with whole-body reinforcement learning to maintain dynamic stability across unstructured, real-world terrain. In the video, Andy Wu, controls engineer for Atlas at Boston Dynamics, highlights some recent experiments that he

Practice Makes Precise: At CoRL, NVIDIA and Partners Reveal How Robots Rehearse Delicate Work in Simulation
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Memories.ai builds standardized egocentric video pipeline
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