
Designing long-travel axes: When rack and pinion outperforms ball screws and linear motors
Sponsored by GAM. A gearbox, pinion, and rack system provide the 7th axis, or lateral movement, for an articulated robot. (Image: GAM.) Pull a tape measure out a few inches and the blade is rigid. Keep pulling and at some point it starts to flex and wobble, then eventually buckles. It is a problem engineers know well when designing long-travel axes. There are three main ways to create precise straight-line motion in machines: rack and pinion, ball screws, and linear motors. In this article, we’l
Three main technologies create precise straight-line motion in machines: rack and pinion, ball screws, and linear motors. Rack and pinion systems, which use a rotating gear engaging against a toothed bar, outperform ball screws for long distances because ball screws experience a phenomenon called whip at lengths around 2-3 meters that can cause vibration and failure, while rack and pinion can scale to much longer distances by assembling multiple sections together. Compared to linear motors, rack and pinion systems offer lower cost per meter, higher stiffness, higher feed forces, and require no continuous power to hold position, though linear motors excel in clean room environments where rack and pinion's need for lubrication is problematic. The choice among these technologies depends on travel distance, cost constraints, environment, and precision requirements.
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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
Simulation, which has long been the proving ground for robots learning to walk, roll and hop, is now helping researchers teach robots tasks that require a finer touch: aligning gears, connecting cables and assembling small components. The shift from locomotion to manipulation is taking center stage at this week’s Conference on Robot Learning (CoRL) in Austin, Texas, alongside the rise of agentic robotics — a new paradigm that integrates frontier agents to create interpretable code for physical

Memories.ai builds standardized egocentric video pipeline
Memories.ai models label every clip: Memories-H0 for hand pose, EgoAction for action segments. | Credit: Memories.ai Memories.ai is building a data pipeline for robotics companies that combines standardized video collection with AI-powered annotation to turn everyday egocentric footage into training data for robots. The San Francisco-based company is developing software and AI to help robots understand the physical world. It builds video-understanding models and data infrastructure, and it colle