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UMI-WM is an action-conditioned bimanual world model that jointly generates both wrist views from observation history, per-arm relative camera poses, and gripper widths, with cross-view consistency, action following, and visual fidelity. The model outperforms baselines in action following and generates paired wrist videos 6.9 times faster than the Teacher model.
Isaac Lab 3.0 is now generally available, offering a unified framework for robot learning with support for multiple physics, rendering, and visualization backends. Key features include Newton physics, OV libraries, and Isaac Sim integration, along with improved simulation tools and reinforcement learning compatibility.
Pulkit Agrawal@pulkitologySignalEditorial score7878Every robot already carries contact sensors: its own joints. When a robot touches something, its joints drift slightly off their commands, and the encoders pick it up. Legged robots and humanoids already leverage this signal to walk blind - but is it enough for blind precise assembly? We find that it is! Our policies can insert pegs, mesh gears, and thread nuts using only the robot's joint positions - no camera, no tactile, and no object poses. And the peg policy works zero-shot in the real world! 🧵 w/ my amazing co-first author @younghyo_park, and advised by @kushalk_ and @pulkitology.
Swift-ROS2 3.0 is a stable release of a native Swift client library for ROS 2, enabling direct integration with ROS 2 graphs using Zenoh or DDS. It includes generated common interfaces, a new GitHub organization, and support for multiple platforms, with improved compatibility and a migration guide for users upgrading from 2.x.
The U.S. Army has introduced a new warrant-officer job, Military Occupational Specialty 390A, training robotics technicians to build, reprogram, and sustain robotic systems. The first 10 graduates completed a 19-week course at Fort Bragg, focusing on technical skills and adaptability in contested environments. The role is part of a broader Army push for autonomy and robotics integration across all units.
This article observes the technical paths and scene deployment progress of five Chinese companies in the field of world models and embodied intelligence. It evaluates their core competencies and development prospects, emphasizing the transition from parameter scale to system-level performance and the importance of real-world data feedback in model iteration.
Menlo Research has released the Python SDK for its Asimov 1 humanoid robot, enabling developers to control movement, access sensors, and integrate media. While the SDK is open-source under Apache 2.0, the control layer Asimov Edge remains unverified. This marks a shift in the industry toward software-driven development, with challenges in real-world integration and safety.
The MMLab team at the University of Hong Kong proposed the EgoWild2Dex method, using uncurated real human first-person operation data to train robots, successfully completing complex tasks such as unpacking packages, using a hot melt glue gun to fix figurines, and making cold drinks, with an average success rate of 96.7%. The study aligns human and robot perspectives through the GeoFormer module and adopts a progressive human-robot joint training paradigm, unifying open scene data, task-related data, and real robot data into the robot's action space.
This paper introduces the self-building laboratory (SBL), a system that designs, configures, and operates its own laboratory setup given a high-level scientific goal. SBL can start from an empty room or an existing self-driving laboratory and build missing capabilities including lab layout, digital twin, robot skills, and experimental workflows. The system is demonstrated across different robots and setups, scaling from a single workstation to multi-robot laboratories. Validation is provided through a real-world quantum-dot synthesis experiment, showing that SBL shifts human researchers from manually configuring device automation to specifying scientific objectives, making scientific automation more accessible, flexible, and scalable.
This paper presents HexaShell, a thin, task-agnostic harness designed to test general tools for robot coding agents.
This research presents LAW, a closed-loop framework that combines vision-language models and world-action models to enable agentic physical intelligence. The system uses semantic subtask progress for planning, action evaluation, and execution monitoring. LAW achieves 94.21% average success on 50 RoboTwin tasks and 65% on real-robot tasks, outperforming existing methods like $pi_{0.5}$ and Fast-WAM.
This research presents RAX, an open-source framework for robot agents that enables continuous self-improvement through online exploration. The framework allows agents to acquire experience, synthesize executable programs, and perform complex tasks without human demonstrations. RAX demonstrates significant performance improvements in both simulation and real-world settings, with results showing a +37.4% average improvement over top baseline models.
China has established over 70 training fields for embodied AI, with additional projects in planning, driven by government policies and industry investment. These centers aim to generate high-quality data for robot training, but face challenges such as data quality, sustainability, and market dynamics. The article explores both successful initiatives and failed projects, highlighting the evolving landscape of data collection and its role in advancing robotics.
NVIDIA announced at GTC Berlin the expansion of its Isaac open robot development platform, including a humanoid robot reference platform, new tools, and datasets. The Agile ONE SN, set for early 2027, integrates simulation, training, and deployment tools, with hardware and software designed for research and development. The platform includes a Jetson Thor compute platform, WUJI Hand 2 hands, and integration with NVIDIA's Halos safety system.
RoboPrompt is a robot control framework proposed by a team from Shanghai Jiao Tong University, which allows users to intuitively guide robot actions through sparse inputs such as points, trajectories, or directional commands. The framework employs a two-stage design, translating human intent into action drafts and then refining their execution through a lower-level policy. Experiments show that RoboPrompt significantly improves the success rate of robot tasks and reduces the need for manual intervention.
Researchers from Zhejiang University propose EmbodiedMemory-Bench (EMem-Bench), a benchmark to evaluate long-term memory in embodied agents, and introduce a memory system (EMem) and model (EMem-8B) to enhance memory management. The work identifies key challenges in retaining and using memory across dynamic environments and demonstrates performance improvements across multiple models.
A research team from Shanghai Chuangzhi College, Yuance Future, Hong Kong University, and NVIDIA has developed PingpongDuo, a system that enables two Unitree G1 humanoid robots to perform table tennis doubles. The system combines perception, team coordination, and whole-body motion control, using reinforcement learning for planning and risk assessment. Real-world testing showed improved performance over traditional rule-based strategies, with the robots achieving 18 consecutive alternating returns in a human-robot doubles match.
UBTECH Thinker-WM achieved first place in three major benchmark evaluations: WorldOlympiad, EWMBench, and WorldArena1.0 Track-1, demonstrating its advantage in cross-robotic form and perspective generalization capabilities.
This research paper evaluates GPT-6-Astra's embodied intelligence capabilities across 12 simulated benchmarks and 8 real-world robot platforms. While it achieves high scores in structured tasks like LIBERO-Long++ and SIMPLE, real-world execution reveals significant limitations, including safety issues, mechanical load constraints, and failure to handle complex, unstructured tasks. The study highlights the gap between simulation and physical deployment, and proposes improvements through better execution systems and task-specific adaptations.
Jim Fan@DrJimFanSignalEditorial score6868
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