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Gartner outlines four AI tiers in warehouse automation

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Gartner reports that warehouse automation now spans four operational AI tiers as logistics operators transition from software trials to live facility deployments. In an analysis released this month, the research firm concludes that logistics infrastructure has reached a clear adoption threshold. Three pressures are driving this change across the sector. Persistent worker deficits make automated […]

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Reward AI Releases OM-1: A Robot Policy Trained on Human Demonstrations Only, With No Teleoperation or On-Robot Data

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Reward AI, a robotics startup whose team’s prior work includes DexCap, HumanPlus, and ALOHA, has released OM-1, short for Omnibody Model 1. OM-1 is a general-purpose manipulation policy that learns from humans wearing a sensorized glove, then runs on industrial arms and humanoids at human speed. The key findings that stands out: no teleoperation data

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NVIDIA Open-Sources OSMO: One YAML Orchestrates Physical AI Training, Simulation, and Robot Testing

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Robot developers do not have one compute problem. They have 3. A policy is trained on GB200 or H100 clusters, tested in Isaac Sim on RTX GPUs, then validated on a Jetson mounted inside a real robot. Each tier has its own cluster, its own scheduler, and its own glue scripts. NVIDIA OSMO is NVIDIA’s

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New method enables AI for safety-critical situations

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MIT researchers have developed a new technique that helps generative artificial intelligence models find solutions to high-stakes problems.In these settings, a plausible answer is not enough: The output often must also satisfy nonnegotiable safety, physical, or task-specific requirements, known as hard constraints.The researchers developed a method that helps generative models meet these strict requirements without

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MIT spinout turns plastic waste into resilient building materials

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The world needs more homes. The world also has too much plastic. Perhaps the only thing those two problems have in common is that they’re hard to solve.Atlas Building Composites, a spinout of MIT, is on a mission to address both problems with a single solution. The company has developed an AI-powered robotic manufacturing platform

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JD.com expands physical AI in logistics with 3 million robots

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JD.com is expanding AI and robotics across its logistics network under a new Physical AI Acceleration Plan, while reiterating a five-year target to procure 3 million robots, 1 million autonomous vehicles, and 100,000 delivery drones. The company launched the plan at JDDiscovery 2026 in Beijing. JD Logistics also unveiled its industrial Wolf Robot series, designed

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The robotics capability framework for physical AI by Arm.

Arm launches Total Design for Physical AI and robotics framework

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Arm has launched Arm Total Design for Physical AI alongside a new robotics framework to establish common standards across automated systems. Physical industries – spanning mining, agriculture, manufacturing, and global transport – account for trillions of dollars in economic activity and an estimated $200 billion annual compute opportunity by the 2030s. To address engineering fragmentation

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Axis Robotics Releases AXIS: A Browser-Based Data Engine With 207 Robot Manipulation Tasks and 50,129 Trajectories

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Robot manipulation datasets have grown far slower than the models trained on them, mostly because collection stays closed and centralized. Expert operators gather demonstrations on lab hardware, process them offline, and ship a fixed benchmark that never grows again. A research team from Axis Robotics, UC Berkeley, Georgia Tech, NTU… is proposing a different shape

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Motional and MIT AI explains self-driving car decisions

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Motional and MIT researchers have built a system that lets self-driving cars explain their decisions in real-time, tackling the black-box problem in autonomous vehicle AI. The work, published in Nature, comes from a team at Motional that includes CEO Laura Major, working alongside researchers from MIT’s Computer Science and Artificial Intelligence Laboratory. Their proposed method,

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System helps humans predict when self-driving cars will make mistakes

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Self-driving cars are often controlled by deep learning models that sometimes fail in unexpected situations. For instance, the car might inexplicably brake and block the path of an oncoming emergency vehicle. A human driver or passenger may need to react rapidly to prevent a collision.To help humans better anticipate a vehicle’s mistakes, researchers from MIT

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