Robotics

A robot that can fold laundry, pack medication, and pour tea - controlled by a single model - sounds like science fiction. But it’s exactly what’s needed for real deployment. The problem? The best robot controllers are either closed-source (π0.5), too slow (reasoning models that generate hundreds of tokens before moving), or tied to hardware most labs can’t afford. MolmoAct2 (Fang, Duan et al., Allen AI / UW / Stanford / NVIDIA / MIT, May 2026) solves all five problems at once: it’s fully open (weights, code, data), runs at 55.79 Hz, deploys on platforms costing under $6,000, and achieves 97.2% success on LIBERO - beating every open and closed baseline. The secret? Let the robot’s action generator peek into the language model’s brain at every layer, not just the final output. ...

A LiDAR on a self-driving car, a depth camera in a home robot, a satellite scanner, and a CAD model from a 3D printer — each produces a point cloud point cloud A set of 3D points (x, y, z) representing the shape of an object or scene. Each point can carry additional attributes: color, normal, intensity. , but with radically different density, scale, and geometry. Until now, each domain required its own model. The paper “Utonia: Toward One Encoder for All Point Clouds” breaks this pattern — one encoder, 137M parameters, five domains, and emergent behaviors nobody expected. ...

The quest for a universal robot—one that can seamlessly switch between tasks, platforms, and environments—has long been the holy grail of robotics research. The paper “Green-VLA: Staged Vision-Language-Action Model for Generalist Robots” brings us closer to that vision with a revolutionary five-stage training framework that enables a single policy to control humanoids, mobile manipulators, and fixed-base robotic arms alike. The Problem: One Robot, Many Bodies Today’s robotic systems are typically specialists. A robotic arm in a factory excels at assembly but cannot navigate a warehouse. A mobile robot can move around but lacks fine manipulation skills. Training a separate AI for each type of robot is expensive, time-consuming, and fundamentally limits scalability. ...

In a world where robots and AI systems increasingly learn through observation and interaction with humans, the efficiency of this process remains a key challenge. Traditional Imitation Learning methods often require a human teacher to constantly supervise and correct errors, which is time-consuming and costly. A team of researchers led by Jelle Luijkx proposes a groundbreaking solution in their latest paper, “ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning.” ...