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Papers & Evaluations Latest coverage

Papers & Evaluations with links to supporting source material.

556 selected reportsLast 30 days 491 recordsAll coverage 2,632 records

updated

Signal archive — Page 26

2026-10-09Fri501–520 records
  1. Machine Heart — Robotics on WeChat68

    Aether AI's CRIS-0: Causal-Driven Robot Intelligence for Real-World Adaptation

    Aether AI introduces CRIS-0, a causal-driven robot intelligence system that enables robots to adapt to unexpected disturbances in real-world environments. The system uses causal reasoning to understand task-relevant variables, predict the consequences of actions, and adjust strategies dynamically. CRIS-0 is structured into two layers: a high-level agent for task planning and a causal world model for predicting physical consequences. The research demonstrates the effectiveness of causal reasoning in dynamic, human-robot collaborative settings, with experiments showing improved robustness and task completion compared to traditional prediction-based approaches.

  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 abstracts85

    ARC: A Reasoning Recipe for Robot Foundation Models

    This research proposes ARC, a reasoning framework that improves zero-shot task performance of existing robot foundation models (RFMs) through structured reasoning traces, automatic labeling, and model adaptation. The method achieves state-of-the-art results on benchmark tasks like RoboLab-120 and MolmoSpaces, with up to 50 percentage point gains on RoboLab-Reasoning-50 and an 82.2 percentage point improvement in task success on real robots.

  4. arXiv Robotics — research abstracts78

    NavGPT-3: Harnessing Context in a Hierarchical Navigation Runtime

    NavGPT-3 connects language models with action policies through a hierarchical runtime system, enabling autonomous agents to navigate complex environments. The system uses a reasoning loop and an action policy trained on 19.28M examples, achieving state-of-the-art results on R2R-CE and matching human performance on RxR-CE. The paper also explores the interaction between the models and the impact of tools on navigation performance.

  5. arXiv Robotics — research abstracts75

    Multi-Agent Coordination via Support-Preserving Distillation

    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.

  6. arXiv Robotics — research abstracts82

    Long-WAM: Scaling the Context of World-Action Models

    This research presents Long-WAM, a model-system framework that scales the context of causal world-action models under real-time control constraints. By using autoregressive pretraining, the model improves task success rates on benchmarks like RoboCasa GR-1 and LIBERO-Long. It also supports real-time deployment on hardware platforms, enabling dynamic and long-horizon manipulation tasks.

  7. arXiv Robotics — research abstracts75

    MagCilia: A Compact Magnetociliary Tactile Sensor with 3D Force Sensing for Robotic Contact Perception and Grasping Feedback

    This paper introduces MagCilia, a compact tactile sensor that integrates a flexible magnetic-cilia structure with a Hall sensor to enable three-dimensional force sensing. Using quasi-static finite element analysis and causal history fusion regression (CHFR), the system reconstructs forces from coupled magnetic channels with root-mean-square errors of 0.40 N, 0.57 N, and 0.69 N for Fx, Fy, and Fz respectively, achieving coefficients of determination between 0.90 and 0.93. Robotic experiments demonstrate the sensor's ability to guide gripper adjustments based on tangential forces and monitor multi-axis loads under external perturbations. Additionally, frequency-domain features allow the system to distinguish six surface categories with 99.39% accuracy in cross-validation grouped by acquisition session, and an online demonstration successfully identifies all six tested surfaces.

2026-10-08Thu
  1. Chinese robotics — Dataset and collection discovery80

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

    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.

  2. Machine Heart — Robotics on WeChat82

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

    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.

  3. Chinese robotics — Robot learning research78

    OpenWAM: An Open Framework for Composable World-Action Models

    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.

  4. ROS Discourse — Community updates75

    Kyvern: signed, verifiable records of why your robot did what it did (ROS 2 example)

    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.

  5. Robotics & Automation News68

    European TAL,OS project demonstrates autonomous robots for solar plant maintenance

    The European TALOS project has completed three years of testing autonomous robots and AI for solar power plant maintenance, demonstrating the technology at sites in Spain, Portugal, and the Netherlands. The system monitors solar plants, identifies performance issues, and recommends maintenance tasks to human operators. It uses production, weather, and field data alongside a digital twin to prioritize tasks. Human oversight remains central, with plant managers approving missions and adjusting parameters. The project reports potential reductions in human risk, maintenance windows, and operational costs, while citing regulatory challenges as a barrier to wider deployment.

  6. Robotics & Automation News72

    Odyssey Launches World Model to Control Robots, Humanoids, Vehicles and Drones

    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.

  7. 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-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.

  8. RoboSpeak — WeChat68

    Danish Team Creates 'Ionic' Tactile Sensor: Generates Power Without Electronics, Produces Voltage by Pressing

    A research team from Aarhus University in Denmark has developed a new type of tactile sensor that operates based on ionic rather than electronic signal transmission. The sensor mimics the ionic signal mechanism of human mechanoreceptors, generating voltage signals through the flow of NaCl solution in microchannels. The study demonstrates its potential applications in prosthetics and neural interfaces, and validates its frequency and sensitivity characteristics in detecting mechanical stimuli.

  9. arXiv Robotics — research abstracts82

    PhysEvo: Astra Can Act, Let It

    This paper presents PhysEvo, a framework for physical recursive self-improvement (RSI) that allows a frozen model to adapt and improve through task execution and meta-agent feedback. The framework enables joint-level control, evidence-seeking observation, and reusable manipulation skills without model-weight updates or a separately trained action policy. Evaluated on 42 RoboDojo tasks and 8 manipulation tasks, PhysEvo achieves an average score of 68.14/100 and 62.00% success, significantly outperforming existing methods. Deployment on real-world tasks shows 84.00% success across 25 trials.

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 discovery68

    The Machines that Make the Machines

    This paper describes the development of intelligent robots for assembling GB300 tester trays, a complex manufacturing task requiring high precision and adaptability. The authors explore challenges such as uncertainty in part geometry and performance requirements, and present a technical strategy combining classical robotics, imitation learning, and simulation-based reinforcement learning. The work emphasizes the importance of real-world data and infrastructure for achieving reliable automation in industrial settings.

  4. Tech Xplore — Robotics78

    Feather star-inspired underwater robot gets 3D maneuverability from only two actuators

    Researchers have developed an aquatic soft robot inspired by feather stars that can move in all three dimensions using only two actuators. The design utilizes mechanical intelligence to achieve efficient 3D maneuverability through three modes: jellyfish, fish, and rotor. The robot can explore underwater spaces, lift objects, and work collaboratively with other robots, with future plans to develop a fully wireless version.

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