
Skild AI unveils S1 flagship robot foundation model
Skild AI said S1 enables in-context learning for robotics. | Source: Skild AI Skild AI recently unveiled S1, the company’s flagship robot foundation model. Since it was founded in 2023, the company has raised nearly $1.7 billion in funding to create a general-purpose robot brain. With S1, robots can learn new, complex tasks from watching a single video, Skild AI claimed. To do this, the company‘s model uses in-context learning, Deepak Pathak, Skild AI co-founder and CEO, told The Rob
A company recently unveiled S1, a robot foundation model that enables robots to learn new complex tasks by watching a single video. The model uses in-context learning, meaning a human demonstration video can be added to the system's prompt and the robot can then perform the task, including long and intricate actions lasting up to 10 minutes. The approach matters because it contrasts with the current robotics practice of requiring lengthy post-training for each new task, which slows scaling. The foundation model achieves this by training on all four major types of robot training data together: teleoperation data, human videos, simulation, and data-capture gloves, with each source compensating for the limitations of the others.
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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