Papers & Evaluations Latest coverage

Token Usage Reduced by 74.9%: Real2Gym Turns Simulated Experience into Reusable Real-World Skills

Real2Gym is a research initiative proposed by institutions such as the Shanghai Artificial Intelligence Laboratory, aiming to construct interactive simulation environments through video, enabling robots to learn and transfer skills to the real world. This method combines geometric constraints, interaction keyframe correction, and physics engine validation, significantly improving task success rates and efficiency while reducing model invocation frequency.

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.

Revealing the Scaling Trends of Human-Robot Co-Training. Euler Wanxiang Collaborates with Hong Kong University of Science and Technology (Guangzhou) to Open-Source Embodied UniWAM

Euler Wanxiang and the Hong Kong University of Science and Technology (Guangzhou) jointly released UniWAM, a Unified World and Action Model, which enables unified learning of semantic understanding, future visual prediction, and action generation. UniWAM achieved state-of-the-art results on benchmarks such as LIBERO, LIBERO-Plus, and RoboTwin 2.0, and its capabilities in instruction following, complex interaction, and long-term task execution were validated through real-world experiments. The model code and weights are fully open-sourced, supporting further research and application.

Pi API Beta Launches Latest Robotic Foundation Models for Deployment

Pi has released its latest robotic foundation models through the Pi API beta, enabling developers to fine-tune and deploy models for real-world applications. The API supports a range of industries and hardware, allowing companies to iterate on autonomy with reduced data and infrastructure requirements. Early deployments highlight the potential for practical robotic applications, with plans to continue updating models through the API.

Science Cover: Venus Flytrap Doesn't Rely on Pumps or Muscles, Offers New Actuation Method for Soft Robots

A research team revealed the mechanism behind the Venus flytrap's closure of its trapping jaws through multiscale experiments, finding that it does not rely on water transport or muscle contraction, but rather on the rapid softening of the epidermal cell walls, which releases pre-stored elastic stress. This discovery offers a new actuation paradigm for soft robots, enabling rapid and controllable active bending through local material stiffness regulation, bypassing the physical limitations of fluid transport rates.

Global First Robot Task Native 3D World Model: Cross-Dimensional UNITAS Enables Robots to Learn Physical Intuition, Sweeping Multiple Benchmarks

UNITAS is the world's first robot task-native 3D world model, achieving cross-perspective and cross-body generalization capabilities by unifying observation, action, and environmental changes within a three-dimensional physical space. The model performs exceptionally well in multiple benchmark tests, including LIBERO, LIBERO-Plus, and RoboTwin 2.0. With just 1.7B parameters, it outperforms larger parameter models such as Fast-WAM (6B) and Motus (8B).

Embodied Intelligence Begins to Evolve Systems: RoboFoundry Enables Robot Autonomous Evolution

RoboFoundry is a new framework for embodied agents that treats the entire system as a self-evolving policy. By freezing the base model and allowing the surrounding system to evolve, it achieves significant improvements in task success rates across benchmarks like EmbodiedBench, RoboMemArena, and LIBERO-PRO. The system enables zero-shot transfer and online evolution on real robots, with results showing substantial performance gains compared to baselines.

ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies

This paper presents ForeAct3, a framework for policy-grounded future world modeling within VLA policies. It uses learnable geometric queries to decode depth, semantic segmentation, and camera pose from policy representations, enabling accurate prediction of future 3D scene states. The method improves manipulation performance through semantic 3D supervision, physical consistency, and action conditioning, achieving 98.3% average success on LIBERO and 37.8% success in real-world tasks.

NovaSplat: Model-Free 6D Object Pose Estimation via 3D Gaussian Splatting Onboarding and Refinement

NovaSplat is a model-free pipeline for 6D object pose estimation that trains a 3D Gaussian Splatting model from a short smartphone video. It serves as both a template for detection and a differentiable backbone for pose refinement, outperforming model-based methods on a household-object benchmark and model-free competitors on an industrial benchmark with textureless and specular surfaces. A faster inference variant, NovaSplat-Lite, is also introduced.

When a Robot Reaches for a Cup, It's Moved Away: LIBERO-MAX Uses 224,000 Simulations to Test Dynamic Reliability

LIBERO-MAX is a study that evaluates the adaptability of robots in dynamic environments by designing paired tasks and mid-task changes to assess the performance of different strategies under environmental changes. The study found that the success rates of all tested strategies decreased, and the impact of different types of changes on the models varied significantly, indicating that robots need more robust perception and planning mechanisms to handle uncertainties in real-world scenarios.

USTC Team Puts a 'Reverse Thinking' World Model into Robots, Secures Millions in Funding in Just Three Months

Bai Ze Tong Jing is a less than six-month-old company based in Hefei, and its developed Causal World Model AWM enables robots to perform causal reasoning about environmental changes. AWM significantly improves data efficiency and task success rates by learning object, motion, and interaction patterns from unlabeled videos.

Are robots better at soldering? A study verifying whether 'learning' in industrial robots can change factories

A research paper explores the use of learning-augmented robotic automation in industrial settings, focusing on a three-phase cable insertion and soldering process. The hybrid system combines routine movements with learning-based control, using visual servoing and safety monitoring to achieve a 99.4% success rate in a factory test. While the robot is slower than humans, its consistency and safety features suggest potential for future industrial applications.

Weiyi Zhizao Passes HKEx Listing Hearing: Calculating the ROI of Industrial Embodied Intelligence

Weiyi Zhizao has passed the Hong Kong Stock Exchange listing hearing, marking a shift in the industrial embodied intelligence sector from conceptual validation to real-world ROI. The company's financial performance, including revenue growth, improved margins, and customer adoption, demonstrates its ability to deliver scalable solutions. Its 'Physical AI Brain' technology, combining AI models, industrial expertise, and real-world data, enables continuous improvement and deployment efficiency, forming a positive feedback loop between technical capability and commercial success.

Add a Stool if You Can't Reach. Li Hengyang's Team Launches Real-World Robot Achievements After Six Months of Startup

The Yuance Future team demonstrated their robot's performance in complex tasks, including reaching high places to retrieve objects, picking up bottles from low positions, clearing tables, and operating dishwashers. The article introduced their 'Whole-Body Intelligence' (Whole-Body Intelligence, WBI) technology approach, emphasizing the integration of body coordination and task execution. By collecting human motion data, real-machine training, and cross-body validation, the robot gradually adapts to various challenges in real-world scenarios.

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.

Robotic Systems Lab@leggedrobotics· 04:32SignalEditorial score6868

Researchers from the ETH Robotic Systems Lab present a world model for quadruped robots that allows zero-shot policy transfer across different robot morphologies without fine-tuning, retraining, or warm-up. The model conditions policies on dynamics rather than directly training policies, enabling generalization across heterogeneous quadrupeds.

Two-armed robot throws and catches balls with human-like movements

AthenaZero, a new dual-arm robotic system developed by researchers at the Robotics and AI Institute, demonstrates agile, human-like movements in tasks such as throwing, catching, and batting. The robot's design incorporates low inertia and backdrivability, enabling more natural and controlled interactions with objects. The study explores the challenges in robotic manipulation and presents a novel approach to improving dexterous interaction with the environment.

LeRobot@LeRobotHF· 13:34SignalEditorial score7272

LeRobot's new streaming loader is designed for training on large-scale robot datasets, allowing efficient processing of 100 TB+ data without full local video copies. It reads episodes directly from Hugging Face or Buckets, prepares samples on-the-fly, and balances memory and throughput for varied training batches.

Humanoid Robot Uses Dexterous Hand to Fold Clothes Autonomously, West Lake Robot Achieves It

West Lake Robot's WR1 humanoid robot demonstrates the ability to autonomously fold clothes using its dexterous hand, marking the first global instance of such capability. The robot performs a series of complex tasks, including retrieving corn from a fridge, moving to an air fryer, organizing toys, and folding clothes, all while maintaining smooth motion and avoiding task interruptions. This is achieved through a novel 'general brain' architecture that integrates perception, decision-making, and motion control, enabling seamless, dynamic task execution in real-world environments.

Tsinghua's Embodied Model Tops Global Rankings. Outperforming GPT-6 and NVIDIA Without Hacks or Extra Data

Xingdong 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.