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VLA Latest coverage

Robotics coverage relating to vla.

286 selected reportsLast 30 days 265 recordsAll coverage 622 records

updated

Signal archive — Page 14

2026-10-14Wed261–280 records
  1. arXiv Robotics — research abstracts78

    Think Like a World Model, Act Like a VLA: Distilling World-Model Representations into Compact Robot Policies

    This research proposes a method to distill world-model representations into compact robot policies by aligning student policies with a frozen world model during training. The approach achieves performance improvements on tasks like LIBERO and RoboCasa-GR1, with a 0.8B student reaching 97.9% on LIBERO and improving from 48.2% to 50.5% on RoboCasa-GR1. The method is also effective on real hardware, demonstrating broad representational prior rather than fragile network alignment.

  2. arXiv Robotics — research abstracts82

    RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis

    RoboGaze is a training-free multi-agent VLM evaluator that provides temporally localized diagnostic reports for robot world models. It outperforms existing baselines, including an agentic detector, and improves accuracy in detecting failures in synthetic manipulation videos, achieving 85% mean clean-clip accuracy on the strongest backbone.

2026-10-13Tue
  1. Chinese robotics — Robot learning research75

    MagicAtom's EchoPolicy-0 and MagicBrain Frameworks Achieve Top Performance in MolmoSpaces Benchmark

    MagicAtom's EchoPolicy-0 framework achieved a 75.4% average success rate in MolmoSpaces Combined benchmark, ranking first globally, while MagicBrain achieved 72.4% with a 94.4% grasp success rate. Both frameworks address challenges in VLA by integrating visual language models with geometric tools and action policies, improving task execution and generalization in complex environments.

  2. Chinese robotics — Robot learning research68

    MagicAtom's EchoPolicy-0 and MagicBrain Frameworks Achieve Top Performance in MolmoSpaces Benchmark

    MagicAtom's EchoPolicy-0 framework achieved a 75.4% average success rate in MolmoSpaces Combined benchmark, ranking first globally, while MagicBrain achieved 72.4% with a 94.4% grasp success rate. Both frameworks address the challenge of aligning language instructions with visual perception in embodied AI, with EchoPolicy-0 focusing on trajectory selection and MagicBrain using visual guidance for task alignment.

2026-10-12Mon
2026-10-10Sat
  1. AI Era — Robotics on WeChat78

    Hangzhou's Embodied Robot Rivals Figure, Demonstrates Full-Body Autonomous Clothing Folding

    The article describes how the WR1 robot, developed by West Lake Robotics, successfully performs complex household tasks such as folding clothes and managing multi-scene operations. It highlights the use of a dual pre-training system, world models, and a general-purpose action execution model to achieve seamless coordination between perception, planning, and execution. The robot's ability to handle flexible objects and maintain task continuity across different environments is emphasized as a significant breakthrough in embodied AI.

2026-10-09Fri
  1. LeRobot — Model repository updates68

    LingBot-VLA 2.0 Base Model for LeRobot

    LingBot-VLA 2.0 (base) is a pre-trained policy for visual-language agents, adapted for LeRobot. It uses a Qwen3-VL-4B backbone with a sparse-MoE Qwen2 action expert, trained via flow matching. The model is available under Apache-2.0 license and supports fine-tuning with dual-query distillation.

  2. arXiv Robotics — research abstracts82

    LM-X: Explainable Vision--Language--Action Modeling via Progress, Event, and Uncertainty Prediction

    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.

  3. arXiv Robotics — research abstracts82

    VioLA: Learning Generalist Humanoid Control Policies from Human Data

    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.

  4. arXiv Robotics — research abstracts82

    SAPS: Shared Autonomy for Policy Steering by Blending Teleoperation with a Pretrained VLA

    This research proposes SAPS, a lightweight framework that merges real-time human teleoperation commands with pretrained VLA actions to enhance robot manipulation reliability. Evaluated across simulated and real-world scenarios, SAPS achieves up to 77.4% hardware success, with reduced human intervention and workload, demonstrating its effectiveness for real-world deployment and assistive teleoperation.

2026-10-07Wed
  1. Robotics — Robot learning news discovery68

    Our Journey Building Vision-Language-Action Robotics

    This tutorial details the progression from imitation learning to Vision-Language-Action (VLA) models in robotics, focusing on the shift from repeating motions to understanding and executing tasks. It outlines key experiments with models like SmolVLA and π0.5, and discusses challenges such as data quality, model capacity, and simulation-to-real transfer, ultimately aiming to build robots that can see, understand, adapt, and act.

  2. Robotics — Robot learning news discovery72

    Our Journey Building Vision-Language-Action Robotics

    This article details the progression of a robotics team's work from simple imitation learning to developing Vision-Language-Action (VLA) models. It outlines challenges in adapting robotic behavior to new environments, the development of models like SmolVLA and π0.5, and the shift toward simulation-generated data for training. The focus is on building robots that can see, understand, and act autonomously in dynamic settings.

  3. Robotics — Robot learning news discovery82

    Perturbot: Breaking Shortcut Priors in Vision-Language-Action Policies

    This paper identifies modality shortcuts in vision-language-action (VLA) policies, where policies rely on superficial cues like visual, lexical, or motor signals rather than task-relevant evidence. The authors propose Perturbot, a method that introduces task-preserving wrist-view perturbations, enriched instructions with decision-relevant captions, and relabeled trajectory segments to reduce reliance on these shortcuts. They also introduce GroundFscore, an offline score to evaluate how much a policy depends on shortcuts. The work provides a framework for training and evaluating VLA policies that disentangle decisions from shortcut priors while preserving responsiveness to task-relevant evidence.

2026-10-05Mon
  1. 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.

  2. NVIDIA — Robotics model repository updates75

    NVIDIA releases G1-Factory-GR00T-1.7 policy for Unitree G1 humanoid

    NVIDIA released G1-Factory-GR00T-1.7, a GR00T N1.7 vision-language-action policy fine-tuned on nine simulation tasks for the Unitree G1 humanoid robot. The model takes head-camera images and joint states to predict whole-body actions and serves as a baseline for reinforcement learning post-training. It is available under the NVIDIA Open Model License for commercial and non-commercial use, with evaluation results reported only in simulation.

2026-09-22Tue
  1. NVIDIA — Robotics72

    NVIDIA Isaac ROS 5.0 Advances Agentic, Open Source Robotics Development

    NVIDIA Isaac ROS 5.0, released at ROSCon 2026, enhances open source robotics development with agentic workflows, GPU acceleration, and new skills for perception and manipulation. It supports ROS Lyrical and Ubuntu 24.04, enabling developers to build and deploy high-performance robotics applications using NVIDIA's accelerated computing and AI models.

2026-07-28Tue
2026-07-07Tue
2026-03-09Mon
  1. NVIDIA — Robotics model repository updates75

    NVIDIA releases SOMA-X v0.3.0 unified parametric human body model framework

    NVIDIA has released SOMA-X v0.3.0, a unified framework for parametric human body models that decouples identity representation from pose parameterization. The release supports six identity backends including SOMA-shape, SMPL, and SMPL-X, and introduces native hand layers with interoperability for user-supplied MANO models. Designed for commercial use under the Apache 2.0 license, the model enables real-time simulation pipelines on NVIDIA GPUs with throughput exceeding 7,000 meshes per second on an A100. The core pipeline relies on closed-form analytical modules rather than learned neural networks, though an optional shallow MLP can be enabled for surface correction.

2026-03-05Thu
  1. Hugging Face — Robotics68

    Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine-Tuning, and On-Device Optimizations

    This tutorial explores the challenges of deploying VLA models on embedded robotic systems, including dataset recording best practices, fine-tuning techniques for ACT and SmolVLA, and real-time performance optimization using the NXP i.MX 95 SoC. It emphasizes asynchronous inference and hardware-aware scheduling to improve control and reduce latency.

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