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NVIDIA has released a trained policy model for the Cosmos3-Nano robot, fine-tuned on LIBERO-10 tasks. The model demonstrates success rates of 93.6% in standard scenes and 87.1% in perturbed scenes, with training data and evaluation details provided.
The article discusses BYD's development of a humanoid robot, analyzing its design, technical specifications, and strategic positioning in the market. It highlights the company's focus on service-oriented robots for retail environments, emphasizing the balance between mechanical design, functional capabilities, and cost efficiency. The patent details and technical breakdown offer insight into the engineering challenges and design choices made for the robot's deployment in 4S stores.
The article discusses the '觅蜂派' (MeiFeng Pai) platform, which allows ordinary people to earn money by performing everyday tasks like cleaning and cooking。
NVIDIA has released a robotics policy model, Cosmos3-Nano LIBERO-10, trained on the LIBERO-10 environment. The model demonstrates success rates of 93.4% in standard scenes and 82.9% in perturbed scenes, with performance evaluated across 10 tasks and 50 initial states. The model is available under the NVIDIA Open Model License.
LeRobot@LeRobotHFSignalEditorial score7272Xingdong Jiyuan's self-developed world action model VPP2 has topped the RoboDojo simulation leaderboard with an average success rate of 32.26% and a score of 39.26, outperforming models like GPT-6-Astra. VPP2 improves prediction and action generalization by decoupling video prediction from action learning through a phased training strategy.
LM-X is a new VLA foundation model that learns three directly supervised online signals—return-to-go (RTG), event-to-go (ETG), and heteroscedastic action-flow variance—to provide explainable control. Pretrained on over 20,000 hours of real-robot trajectories, LM-X achieves 74.1% success on RoboTwin2.0 tasks and outperforms GR00T N1.7 in both simulated and real-world settings. The model's signals track progress, anticipate motion, and detect high-error actions, establishing it as a transparent and effective VLA framework.
This paper presents VioLA, a generalist humanoid policy that predicts body and hand motion latents instead of joint commands. By mapping human and robot motion into the same latent space, VioLA enables zero-shot task execution on real robots, achieving 100% success in locomotion and 88.6% in manipulation without task-specific fine-tuning. The method is validated across multiple models and datasets.
The article outlines the operations of a humanoid robot training center that generates high-quality data for embodied intelligence applications. It details the data collection process, the role of open-source datasets like RoboMIND, and the importance of standardized, high-value data in driving the development of autonomous robotic systems.
This research introduces UniCross, a unified framework for dexterous manipulation that integrates four canonical skills—grasping, relocation, in-hand rotation, and in-hand translation—into a single relational task formulation. By modeling these skills with shared constraints and objectives, the approach achieves cross-skill compatibility, enabling distillation of a single policy without skill identifiers. The method supports long-horizon skill chaining and generalizes to unseen objects and hand morphologies, demonstrating that distinct manipulation skills can be instantiated from a common relational formulation.
This research paper addresses failure modes in offline multi-agent reinforcement learning (MARL) where standard flow-based teachers route noise samples to conflicting coordination modes. The authors propose Mode-Support Semi-Discrete Optimal Transport (MoSDOT), a method that summarizes multimodal replay into finite mode support with prescribed capacities and uses conditional semi-discrete optimal transport to assign each noise sample to a single mode before teacher training. Additionally, a shared-randomness variant is studied using a shared noise component at execution to expose the residual gap intrinsic to strict-product execution. On controlled diagnostics and offline MARL benchmarks, MoSDOT improves endpoint quality and routing consistency, particularly on datasets exhibiting multimodal joint behavior.
TouchScale is a 500-hour human vision-touch dataset collected with unified sensors and protocols, enabling scaling benefits in perception and robotic manipulation. The dataset improves cross-sensor generalization and robot task success rates without using action labels, demonstrating the potential of large-scale human touch data for embodied AI.
TouchScale is a 500-hour human visual-tactile dataset collected with unified sensors and synchronization, showing scaling benefits in both perception and robotic manipulation. The dataset enables cross-sensor generalization and improves robot task success rates from 22.5% to 57.5% through mid-training. The study highlights the potential of large-scale visual-tactile data to enhance embodied AI systems.
This paper presents OpenWAM, an open framework for world-action models (WAM) that aims to standardize the evaluation of different WAM designs. The framework includes a three-stage training process, a shared MoT architecture, and four configurable interaction modes. It introduces modular components like IDM and FDM, trained on a large-scale 'counterfactual' dataset, leading to improved task success rates in both simulation and real-world robotic tasks.
Kyvern is an Apache-2.0 Python library that records a robot's decisions in a signed, hash-linked chain, allowing external verification. It supports ROS 2 with an example safety controller and provides tools for generating compliance reports. While not yet a certified safety system, it offers a framework for accountability and transparency in autonomous systems.
Developers are using NVIDIA Omniverse libraries combined with frontier AI models to build simulation applications for robotics. The article details workflows for creating animated 3D assets, testing robotic disassembly in CAD environments, reconstructing editable building models from site scans, analyzing industrial equipment operating scenarios, assembling simulation scenes with open-source assets, and stress-testing warehouse simulations at scale. NVIDIA writers describe these projects as demonstrations of agent-assisted creation and simulation capabilities within the Omniverse ecosystem.
Odyssey, an AI research company, has released Odyssey-3, its most powerful world model to date. This model enables intelligent systems to understand, predict, and interact with both physical and virtual environments. It has been adopted by companies like Flexion and demonstrated across six domains, including robotics, humanoids, vehicles, AI training, drones, and games.
NVIDIA has released a trained policy model for the Cosmos3-Nano robot, fine-tuned on the LIBERO-10 environment. The model achieves 93.3% success in standard scenes and 83.2% in perturbed scenes. It is part of a synthetic data study and is licensed under NVIDIA's Open Model License.
Amazon has launched the Physical AI Toolchain on AWS, an open-source platform that combines architecture guidance, deployment automation, and ready-to-use code. It integrates with NVIDIA's Physical AI stack to support the full development lifecycle of physical AI, enabling machines to perceive, understand, and act in the real world. The toolchain includes synthetic data generation, model training, simulation, edge deployment, and continuous improvement, powered by AWS services and NVIDIA's physical AI ecosystem.
TouchScale is a 500-hour human vision-tactile dataset collected with unified sensors and synchronization, showing scaling benefits in both perception and robotic manipulation. The dataset enables cross-sensor generalization and improves robot task success rates without action labels, suggesting a new path for embodied AI training.
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