Cross-embodiment

LeRobot@LeRobotHFSignalEditorial score6565

The author explores the LeRobot community dataset, using embeddings and FiftyOne to analyze 497 episodes across 50 robot types. They identify near-duplicates, filter sessions, and reveal cross-embodiment patterns like cloth folding. A curated subset is available for further exploration.

Can Embodied Intelligence Be Trusted? Sergey Levine Points the Way

At the 2026 World Robotics Conference, Sergey Levine pointed out that the robotics industry is undergoing a shift from controlling the body to developing decision-making capabilities. He emphasized that robots need to combine prior knowledge with real-world experience to achieve true intelligence. Although robots are still in the foundational technology stage, through data flywheels and scaling, they may eventually enable broader applications.

Amazon Invests US$100 Million in a Robot Factory: A Cross-border Logistics Arms Race?

Amazon announced an investment of $100 million to build a high-end robot manufacturing plant in Indiana, USA, expected to start production in 2028, focusing on automated equipment production. At the same time, Chinese logistics giants such as Cainiao and JD Logistics are accelerating their robot automation layouts in overseas warehouses, using self-developed technology to improve cross-border logistics efficiency. The article analyzes the demand for robotic automation in the cross-border logistics industry, as well as how major companies are enhancing their competitiveness through technological upgrades and self-research and self-production models.

Diffusion models in robotics: a comprehensive review

This paper presents a comprehensive review of diffusion models in robotics, focusing on their application in robot learning, data scaling, reinforcement learning, and imitation learning. The authors conducted a systematic literature review, selecting peer-reviewed articles and high-impact preprints to analyze the current state of diffusion model research in robotics. The paper discusses various applications of diffusion models, including semantic-level data augmentation, cross-view and morphology synthesis, automated simulation asset and task generation, trajectory optimization, policy representation, hierarchical planning, and real-time deployment. It also addresses the challenges and limitations of current approaches, such as computational cost, inference latency, and data efficiency, while highlighting promising future directions for research and development in the field.

Building the general-purpose robotic brain

Skild AI introduces their omni-bodied robotics foundation model, the Skild Brain, which is trained across diverse morphologies and data sources to enable generalization across tasks and hardware. The model uses a hierarchical architecture with low- and high-frequency policies, and is pre-trained using large-scale simulation and internet video data, with post-training on real-world data. This model represents a significant step toward creating a general-purpose robotic brain capable of operating on various robot types.

The case for an omni-bodied robot brain

Skild AI presents a research paper detailing the development of an AI model trained across a vast array of robot bodies, enabling it to adapt to unpredictable scenarios without prior exposure. The model demonstrates zero-shot control and in-context learning, showing resilience in scenarios like limb loss, joint failure, and morphological changes. The work highlights the importance of adaptability in embodied AI for real-world applications.

Boston Dynamics unveils new dexterous hands for Atlas humanoid robot

Boston Dynamics has launched a new generation of dexterous hands for its Atlas humanoid robot, featuring 13 degrees of freedom, direct actuation, and design for sim-to-real reinforcement learning. The hand is built for industrial and logistics tasks, with a focus on dexterity, strength, and mass production.

Motors Don't Need Upgrades, Robots Can Run Faster! Beihang University Science Subjournal Reveals New Logic for Quadruped Locomotion

Professor Shi Qing's team from Beijing Institute of Technology was inspired by the high-speed running mechanism of the elephant shrew. They designed a micro quadruped robot named FLEXOR equipped with a dual-joint coupled spine. Through dynamic spine-leg coordination, the robot achieved a 31.6% increase in speed and a 32.2% reduction in energy consumption without upgrading its motors. The study reveals the critical role of the timing coordination between the spine and legs in locomotion performance and verifies the universality of this mechanism across different robot morphologies, providing new insights for highly mobile embodied intelligent robots.

Destro AI’s secret sauce is getting robots and humans on the same page

Destro AI, a startup with an $8 million seed round, is redefining robotics deployment by creating an AI intelligence layer that coordinates both human workers and robots in logistics settings. Their system, called Mothership, streamlines cross-docking operations by directing humans, carts, and trucks through a unified workflow, offering a more efficient alternative to traditional automation and competing robotic startups.

GPT-6 Astra Connects to Unitree G1, Cleans the Kitchen!

The Stanford team's HomeBody project demonstrates how GPT-6 Astra controls the Unitree G1 robot to complete kitchen cleaning tasks through a three-step process: spatial exploration, digital twin simulation, and task execution. The system uses pre-trained models and a skill library to perform complex operations in new environments without additional training.

Deployment Year Begins: Zhixuan Deploys 100 Robots in Retail Stores

Zhixuan Robotics has deployed 100 units of the Lingxi X2 humanoid robot in 100 retail stores of Aishida, achieving a 39% increase in store sales. The robots, named 'Aibao,' engage customers by understanding their cooking needs and guiding them toward suitable products, while also seamlessly transferring conversations to human staff when necessary. This marks a milestone in the deployment of embodied AI in real-world retail environments.

The 'War' of Dexterous Hands: 8 Embodied Companies Show Off Their Skills

This article explores the complexity of dexterous hands in real-world tasks, pointing out that evaluation criteria have shifted from single parameters to overall operational capabilities. Eight companies analyze, from standards, hardware, perception, data, models to commercialization, how to achieve the reliability and practicality of dexterous hands. The article emphasizes the integration of vision and tactile sensing, the importance of data loops, and the challenges between model training and real-world deployment.

LAMP Helps Robots Find a Way Through

Carnegie Mellon University researchers have developed LAMP, a system that allows multiple robots to work together to move objects through crowded spaces. LAMP combines learned models with search-based planning to enable efficient navigation and coordination, with successful testing in complex environments and a demonstration at the 2026 IROS conference.

Robots Are Learning to Feel

This article explores how tactile data is helping robots improve their dexterous manipulation skills. Researchers are creating large tactile datasets and models that can use tactile feedback to enhance robot performance in tasks like folding laundry or turning keys. Challenges include the difficulty of integrating tactile data with vision-based models and the need for more diverse and scalable datasets to achieve significant improvements in robot dexterity.

Unitree@UnitreeRoboticsSignalEditorial score6363

Unitree has fully open-sourced its UnifoLM-WLA-1.0 embodied foundation model, achieving new state-of-the-art results across multiple benchmarks. The model supports cross-task and cross-end-effector generalization, enabling whole-body coordination for both desktop and mobile manipulation.

Cyborg Roaches Can Stab You With Needles

Researchers at the University of Queensland have developed 'Paraborgs'—cyborg cockroaches equipped with needles and cameras to assist in search-and-rescue missions. These insects, grafted with electronic interfaces, can navigate rubble, deliver drugs, and operate in teams. The study addresses challenges in autonomy, communication, and human interaction with the technology.

NVIDIA Releases GR00T N1.7 Model for Generalized Humanoid Robot Reasoning and Skills

NVIDIA has released a cross-embodiment foundation model for humanoid robots, leveraging a Cosmos-Reason2/Qwen3-VL backbone and a flow-matching action transformer. The model is trained using LeRobot and is available for training and deployment under the Apache 2.0 license.

NVIDIA Releases Cross-Embodiment Foundation Model for Humanoid Robots

NVIDIA has released a cross-embodiment foundation model for humanoid robots, leveraging a Cosmos-Reason2/Qwen3-VL backbone and a flow-matching action transformer. The model is trained using LeRobot and is available for training and deployment via Hugging Face.

NVIDIA Releases Cross-Embodiment Foundation Model for Humanoid Robots

NVIDIA has released the 'gr00t17-lerobot-libero_object-640' model, a cross-embodiment foundation model for humanoid robot reasoning and skills. It uses a Cosmos-Reason2/Qwen3-VL backbone and a flow-matching action transformer, trained with LeRobot and available under the Apache 2.0 license. The model supports training and deployment through LeRobot, with detailed instructions provided.

NVIDIA Releases Cross-Embodiment Foundation Model for Humanoid Robots

NVIDIA has released a cross-embodiment foundation model for humanoid robots, leveraging a Cosmos-Reason2/Qwen3-VL backbone and a flow-matching action transformer. The model is trained using LeRobot and is available for training and deployment via the Hugging Face Hub.