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ETH Zurich researchers have developed a system where a WUJI robotic hand can crawl, recover from falls, and perform manipulation tasks without a separate mobility system. Using reinforcement learning, the hand learns to support its own weight and move using its fingers. The work was presented in a paper titled 'Fingers as Legs: Learning Self-Supported Locomotion and Manipulation with an Anthropomorphic Hand.'
Skild AI has developed a self-play training method that allows robots to acquire physical skills without human demonstrations. The approach, which transfers successfully to a physical humanoid robot, represents a step toward autonomous physical AI and could reduce the reliance on human-generated data for complex tasks.
Leju Robotics and Zhao Feng Machinery have deployed 15 humanoid robots in a complex industrial environment, achieving a 98% success rate in part handling and completing over 60,000 components in two months. The solution includes open-sourced real-world data and tools for skill development, aiming to lower the barriers for embodied AI research and deployment.
At the 2026 Sustainable Global Leaders Conference, Leju Robotics and Zhao Feng Machinery jointly launched an embodied intelligence solution for automotive parts loading. The 15 humanoid robots completed a full loading cycle in under 40 seconds with a 98% success rate, operating for over 1000 hours. The project also released a real-world data set for the OpenLET community to support further research and development in embodied intelligence.
At the 2026 Sustainable Global Leaders Conference, the collaboration between Luju Robotics and Zhao Feng Machinery revealed a transition from pilot testing to regular operation of humanoid robots in automotive manufacturing. The robots, operating in a dynamic production environment, completed a full workflow of preparation, material retrieval, transportation, and loading with high reliability and success rates. The project also open-sourced real production line data, addressing the scarcity of real-world industrial data and enabling continuous model improvement through real-world feedback.
RoboAtlas is a contextual Active SLAM framework that adaptively balances geometric exploration and semantic reasoning using a scalable 3D semantic mapping system. Evaluated in simulation and on a Unitree Go2 robot in large-scale real-world environments, it achieves a 100% task success rate and state-of-the-art performance on the GOAT-Bench 'Val Unseen' benchmark, with success rates of 90.6% using GPT-4o and 88.8% using Qwen2.5-VL-7B.
Spark-0 is a vision-tactile-language-action model that leverages tactile data to enhance robot dexterous manipulation. The model uses a 1,000-hour tactile dataset, HumanTouch, and combines it with visual data and simulation for pretraining. It achieves the highest mean success rate on SparkArena and improves success rates on both simulated and real-world tasks when tactile data is included.
This research presents X-Fly, a staged training framework for full-chunk aerial vision-language navigation. By executing complete predicted action chunks before replanning, the method reduces model inference time from 195 ms to 42 ms per waypoint and improves recovery from non-expert states. The approach combines Recovery DAgger labeling with Flow-GRPO refinement to jointly optimize a chunked flow controller and a Bernoulli STOP policy. On the OpenFly test-seen dataset, X-Fly achieves a 74.18% success rate compared to 16.45% for full-chunk behavior cloning and 32.04% for DreamFly.
This release introduces the lerobot/so101-tabletop-mujoco environment within the LeRobot EnvHub framework. It simulates an SO-101 arm interacting with four colored cubes and a green box on a table using MuJoCo physics. The setup includes front and wrist cameras, 6 joint positions for observation, and absolute joint targets for control at 30 fps. A corresponding Isaac Sim version is also available, allowing the same policies to run across both simulators. The environment supports seeded resets, noise injection, and headless execution via EGL, all under an Apache-2.0 license.
The LeRobot team released lerobot/so101-tabletop-isaac, an environment built natively in NVIDIA Isaac Sim 6.2 featuring an SO-101 arm, four wooden cubes and a green box on a table. The layout, cameras, observations and actions match the MuJoCo version, allowing the same policies to run against both simulators. The environment is Apache-2.0 licensed, includes bundled assets requiring no network access at runtime, and runs on an NVIDIA RTX GPU with PhysX physics and RTX rendering.
The article explores the integration of AI agents with robotics through edge computing, emphasizing the use of standardized protocols like MCP and the separation of cognitive and motor functions. It discusses the advantages of edge processing over cloud-based solutions, including reduced latency and cost predictability, and outlines a phased approach for deploying physical AI in industrial settings.
This article explores the evolution of embodied intelligence through world models。
CodeActionBench is a study that evaluates the performance of general multimodal agents in robot operation tasks, testing the strategy generation and execution capabilities of multiple models through a dual-arm simulation environment. The study highlights Astra's advantages in task success rate and code error rate, and analyzes differences in task coverage, execution trajectories, and completion judgment across different configurations.
In October 2026, 15 humanoid robots from Leju Robotics and Zhao Feng Machinery began operating in a car parts production line in Zhejiang, marking the first replicable embodiment of intelligent training in real-world scenarios. The robots perform a sequence of 10+ actions with 98% success rate, collecting over 40,000 data points monthly. The project includes open-source contributions to the OpenLET community, aiming to lower industry R&D barriers and enable scalable deployment.
This research presents LEGS, a hybrid simulator that combines a mesh foreground with a photorealistic 3D Gaussian Splatting background to train vision-language-action (VLA) policies for humanoid loco-manipulation without human teleoperation. The method uses a procedural motion-primitive generator to synthesize labeled demonstrations and a two-stage color calibration to align renders with the deployment camera. Policies trained on LEGS data match or exceed those trained on human teleoperation demos and outperform mesh-only baselines. The approach enables cost-effective re-rendering of demonstrations for new scenes and objects.
This research explores how agents fail to maintain goal alignment during distribution shift, identifying three distinct failure modes based on where the goal is lost within the agent's internal processing. The study shows that recovery methods vary depending on the depth of goal loss and demonstrates how steering and downstream wiring can improve goal reach in different scenarios.
This paper introduces DiffGIPC, a GPU-accelerated differentiable framework for inverse problems involving contact-rich interactions between deformable and nearly rigid bodies.
This paper presents Gaussian-LIC2, a photo-realistic LiDAR-Inertial-Camera Gaussian Splatting SLAM system that simultaneously addresses visual quality, geometric accuracy, and real-time performance. The system incrementally reconstructs a large-scale 3D Gaussian map using camera and LiDAR data, enabling high-quality novel view rendering. It employs a lightweight zero-shot depth model to address under-reconstruction in LiDAR-blind areas and uses sparse LiDAR data to supervise Gaussian map optimization. The system also improves odometry robustness through photometric constraints in a continuous-time factor graph optimization framework.
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