
Tesollo initiates IPO process while developing humanoid hands
Tesollo is developing high degree-of-freedom hands for use with any humanoid robot. | Credit: Tesollo Robotic hand developer Tesollo Inc. has officially begun preparations for an initial public offering, or IPO, in the coming year and has completed its Series B funding round to fuel its global expansion. The Incheon, South Korea-based company specializes in manufacturing core robotic components, advanced end effectors, and systems designed to enhance robot autonomy and human-robot collaboration.
A South Korea-based robotic hand developer has begun preparations for an initial public offering while completing a Series B funding round to support global expansion. The company specializes in manufacturing robotic hands for humanoid robots, with products ranging from two-fingered grippers to five-fingered models that weigh less than one kilogram and feature multiple independently driven joints. Humanoid robotic hands are a critical challenge in the robotics industry because they contain numerous micro-actuators and sensors that create multiple points of failure, leading to frequent breakage and repair needs during normal operations. The company has addressed this by designing its hand products to allow easy repair and replacement of individual fingers and joints, recognizing that durability will be essential for humanoid robots to survive industrial and commercial deployment.
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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