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Open Artifacts with links to supporting source material.

782 selected reportsLast 30 days 706 recordsAll coverage 2,428 records

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Signal archive — Page 38

2026-10-08Thu741–760 records
  1. Chinese robotics — Dataset and collection discovery68

    GitHub Trending Daily Observation: Teleoperation with Robots and Open-Source Project Evaluation Guide

    This article analyzes the GitHub daily chart to explore the rising popularity of teleoperation in robotics and the dynamics within the open-source community. The author points out that evaluating projects should focus on direction rather than just star counts, emphasizing key technical aspects such as URDF models, kinematics, input device abstraction layers, and ROS communication systems. Additionally, the article provides five dimensions for evaluating repositories and a five-minute checklist to help readers filter high-quality projects.

2026-10-07Wed
  1. NVIDIA — Robotics model repository updates68

    NVIDIA Releases Cosmos3-Nano Policy Model for LIBERO-10 Environment

    NVIDIA has released a trained policy model for the Cosmos3-Nano framework, specifically adapted for the LIBERO-10 environment. The model was trained on a combination of real and synthetic data, achieving success rates of 93.9% in standard scenes and 84.7% in perturbed scenes. The model is part of a broader study on restyling demonstrations and is available under the NVIDIA Open Model License.

  2. NVIDIA — Robotics model repository updates68

    NVIDIA Releases Cosmos3-Nano Policy Model for LIBERO-10 Sim-to-Real Transfer

    NVIDIA has published a policy model trained on LIBERO-10 data as part of the Cosmos Cookbook, enabling sim-to-real transfer and policy evaluation. The model is exported as a checkpoint with EMA weights and can be loaded by the cosmos-framework policy server. Training data and restyled demonstrations are sourced from the LIBERO_LeRobot_v3 repository.

2026-10-06Tue
  1. ROS Discourse — Community updates75

    DialoStack: Task-Oriented Spoken Dialogue for ROS 2 with LLM Integration

    DialoStack is a set of ROS 2 packages that enables structured spoken dialogue for robots, using Python for dialogue flow and LLMs for interpretation and response generation. It is open-source (MIT), supports slot-filling, explanation, and quiz dialogue types, and includes speech and vision I/O. The tool is designed for use in human-robot interaction scenarios and is available on GitHub.

  2. Chinese robotics — Dataset and collection discovery82

    TouchScale: 500 Hours of Human Vision and Touch for Visual-Tactile Learning

    TouchScale is a 500-hour human vision-touch dataset collected using a unified sensor setup, enabling scaling benefits in both perception and robotic manipulation. The dataset improves robot task success rates through mid-training with human data, demonstrating the potential of large-scale human experience in training embodied agents.

  3. Chinese robotics — Dataset and collection discovery68

    GitHub Daily Chart Deep Dive: AI Agent and Robot Teleoperation Practical Analysis

    This article provides an in-depth analysis of the popular projects on the GitHub Trending list on September 24, 2026, covering AI Agents, robot teleoperation, quantitative data services, and life efficiency tools. The article dissects the technical implementations and application scenarios of several trending projects, while also addressing common issues developers face when using GitHub, such as access latency and expired student authentication. Additionally, it offers practical advice on how to effectively use daily chart projects for learning and contribution.

2026-10-05Mon
  1. Robotics — Robot learning news discovery68

    Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines

    This paper presents a systematic analysis of Vision-Language-Action (VLA) research, focusing on datasets, benchmarks, and data engines. It identifies key challenges in data fidelity, scalability, and evaluation, and proposes a structured approach to address these issues. The authors release an open-source repository to support the community.

  2. Robotics — Robot learning news discovery68

    A Curated List of Vision-Language-Action (VLA) Models for Robotics and Embodied AI

    This GitHub repository is a curated list of Vision-Language-Action (VLA) models, architectures, benchmarks, and datasets for robotics and embodied AI, with a focus on 2025–2026. It includes foundational models like RT-1, RT-2, and OpenVLA, as well as advanced models such as π0, GR00T N1, and Gemini Robotics. The repository also features architectural innovations, benchmarks like LIBERO and RoboTwin 2.0, and open-source code for many of the models.

  3. LeRobot68

    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.

    Quoted postharpreet@DataScienceHarp

    you balanced your robot dataset by embodiment. the embeddings say you balanced it by recording session i pulled 10 episodes from each of the 50 robot types in lerobot's community dataset and embedded every clip with qwen3-vl 1. 495 of 497 clips have their nearest neighbor in the same session 2. 292 score below 0.1 uniqueness 3. the two "most unique" clips are single-frame recordings, 0.02 seconds long filter the session out and the cross-embodiment signal shows up: a cloth fold on one rig lands next to so100 arms folding cloth i indexed all 497 episodes in fiftyone so you can search by text, cut the near-duplicates, and export a curated lerobot v3 subset one prompt to an agent with fiftyone skills built the whole thing start here, read the full blog: https://huggingface.co/blog/harpreetsahota/fiftyone-now-reads-lerobot-50-embodiments-497-epis read the dataset card: https://huggingface.co/datasets/Voxel51/community_v3_10per_embodiment @LeRobotHF @Alibaba_Qwen

  4. Robotics — Robot learning news discovery82

    Fine-Grained Annotated Robotic Trajectory Dataset and Vision-Language-Action Policy

    This paper presents FineART, a densely annotated bimanual manipulation dataset with 40,543 episodes and 533,913 subtask labels, totaling 1,718 hours. It also introduces FineART-VLA, a vision-language-action policy that improves task success rates through dense subtask training. The dataset and model are open-sourced, offering a new benchmark for long-horizon bimanual manipulation tasks.

  5. Chinese robotics — Dataset and collection discovery68

    Dual-Arm Embodied Intelligence Teleoperation Platform - Ai Cai Gou Source Bible

    The Cobot Magic platform features a modular, high-precision design with Tracer differential chassis and PiPER six-axis arms, supporting ROS frameworks and open-source algorithms. It integrates multi-modal perception systems and enables data collection for tasks like precision assembly and home care, with a 50% reduction in development time and 30-50% cost savings compared to traditional solutions.

2026-10-03Sat
  1. Machine Heart — Robotics on WeChat75

    CoRL 2026|Making Robots More Agile: Starting with Learning to Walk

    This paper presents LeaP, a learnable source prior for generative robot policies, which replaces fixed standard Gaussian distributions with state-dependent random initialization. LeaP jointly learns the mean and variance of an initial Gaussian distribution, improving action generation in robotic tasks. The method achieves an average success rate of 81.6% in simulation and 80.0% on real robotic arms, outperforming existing baselines like A2A and VITA. The paper is accepted at CoRL 2026, and the code is open-sourced.

2026-10-02Fri
  1. Sanctuary AI — Research and announcements82

    TIGER: Task-Space Imitation Guidance for Efficient Reinforcement Learning

    Sanctuary AI introduces TIGER, a reinforcement learning framework that uses imitation policies as dense reward signals to improve efficiency and safety. TIGER reduces interaction steps and safety resets on both simulated and real-world tasks, demonstrating its potential for deployment in industrial robotics.

2026-10-01Thu
  1. International Journal of Robotics Research — Journal metadata75

    GrandTour: A legged robotics dataset in the wild for multi-modal perception and state estimation

    The GrandTour dataset addresses the lack of large-scale, real-world legged-robot data by providing time-synchronized sensor data from multiple modalities, including LiDAR, RGB cameras, depth sensors, and GNSS. It supports research in SLAM, state estimation, and multi-modal learning, with data available in ROS and HuggingFace formats.

2026-09-23Wed
  1. Animesh Garg78

    FLUX 3 Action is an open-source 7B parameter world action model that achieves first place on the RoboLab benchmark. It outperforms previous models by 6.1 percentage points with 56% fewer parameters and runs 3.95x faster. The model supports fine-tuning for specific robots and tasks and is integrated into LeRobot with deployment on NVIDIA Jetson.

    Quoted postBlack Forest Labs@bfl_ai

    Introducing FLUX 3 Action. An open weights 7B World Action Model that achieves first place on the RoboLab benchmark. It outperforms the previous best open model by 6.1 percentage points while using 56% fewer parameters and running up to 3.95x faster.⁠⁠ FLUX 3 Action removes the usual trade-off between world action model performance and VLA speed: it still predicts video and actions together, but plans more than twice as far ahead and runs faster per second of robot motion than the strongest open VLA. Teams can fine-tune FLUX 3 Action on their own demonstrations to create policies for a particular robot and task. Together with @nvidia, we also integrated FLUX 3 Action natively into @huggingface's LeRobot, with fine-tuning recipes included and edge deployment on NVIDIA Jetson. Beyond robotics, we’re also seeing promising results training task-specific policies for acting in simulated environments like gaming, controlling a vehicle, computer use, and wherever else a model needs to understand a visual environment and then choose what to do next. FLUX 3 Action builds on the same image, video, and audio pretraining as FLUX 3, but uses a smaller architecture designed for practical deployment. In midtraining, we trained the model to predict actions and future frames together. We’re releasing the weights, code, fine-tuning recipe, benchmarks, and reproducible examples so researchers and developers can build on the model with their own robots, environments, and tasks (see below).

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