Core Viewpoint - TRON1 is a cutting-edge research platform designed for educational and scientific purposes, featuring a modular design that supports multiple robotic forms and algorithms, catering to diverse research needs [1]. Group 1: Product Features - TRON1 supports humanoid gait development and is ideal for reinforcement learning research, with the EDU version allowing for external camera integration for navigation and perception tasks [6][24]. - The platform supports C++ and Python for development, making it accessible for users without C++ knowledge [6]. - It features a "three-in-one" modular design that allows for quick switching between bipedal, point-foot, and wheeled locomotion [1]. Group 2: Technical Specifications - The platform is compatible with major simulation platforms like NVIDIA Isaac, Mujoco, and Gazebo, enhancing validation efficiency and lowering research barriers [9]. - TRON1 can be equipped with a robotic arm for various mobile operation tasks, supporting both single-arm and dual-foot configurations [11]. - It integrates LiDAR and depth cameras for 3D mapping, localization, navigation, and dynamic obstacle avoidance [13]. Group 3: Hardware and Performance - The TRON1 standard version and EDU version share similar mechanical parameters, with a weight limit of approximately 10 kg and a maximum speed of 5 m/s for wheeled locomotion [26]. - The platform is powered by an 8-core Arm Cortex-A78AE CPU and features NVIDIA Ampere architecture GPU with AI computing power of 157 TOPS (sparse) and 78 TOPS (dense) [16][19]. - The battery supports a maximum power of 1000W, with a runtime of over 2 hours under rated conditions [26]. Group 4: User Support and Development - Comprehensive user manuals and development guides are provided, ensuring ease of use and support for new users [29][33]. - The platform offers a one-year after-sales service post-acceptance, with paid maintenance and parts support available thereafter [40].

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Core Insights - The article introduces ThinkAct, a dual-system framework designed to enhance the reasoning capabilities of multi-modal large language models (MLLMs) in physical environments by connecting high-level reasoning with low-level action execution [4][9][12] - ThinkAct aims to address the limitations of existing VLA models that struggle with long-term planning and adapting to complex tasks by utilizing reinforced visual latent planning [4][6][9] Group 1: Framework and Methodology - ThinkAct employs a structured approach to VLA reasoning tasks, where the model receives visual observations and textual instructions to predict actions, effectively linking abstract planning with low-level control [12][21] - The framework utilizes reinforcement learning to enhance the reasoning capabilities of MLLMs, encouraging them to generate low-level actions after reasoning through the task [13][19] - A novel action-aligned visual feedback mechanism is introduced to capture long-term goals and encourage visual associations during the planning process [14][18] Group 2: Performance Evaluation - ThinkAct demonstrates superior performance in various robotic operation tasks, achieving a top success rate of 84.4% on the LIBERO benchmark, outperforming other models like DiT-Policy and CoT-VLA [25][26] - In the SimplerEnv evaluation, ThinkAct outperformed baseline action models by significant margins, achieving overall scores of 71.5%, 65.1%, and 43.8% across different settings [25] - The framework also excels in embodied reasoning tasks, showing advantages in long-term and multi-step planning capabilities, as evidenced by its performance on EgoPlan-Bench2 and RoboVQA benchmarks [26][27] Group 3: Qualitative Insights - The article provides qualitative examples illustrating ThinkAct's reasoning process and execution in tasks, showcasing its ability to decompose instructions into meaningful sub-goals and visualize planning trajectories [30][31] - The framework's reinforcement learning adjustments significantly enhance its reasoning capabilities, allowing it to better understand tasks and environments compared to cold-start models [31][32] Group 4: Adaptability and Error Correction - ThinkAct demonstrates effective few-shot adaptation capabilities, successfully generalizing to unseen environments and new skills with minimal demonstration samples [35][37] - The framework's ability to detect execution errors and perform ego correction is highlighted, showcasing its structured reasoning to reconsider tasks and generate corrective plans when faced with failures [37][38]

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Core Viewpoint - The article discusses the development of Zebra-CoT, a large-scale and diverse dataset aimed at enhancing visual reasoning capabilities in multi-modal models, addressing the challenges of existing visual CoT performance and the lack of high-quality training data [3][4]. Dataset Construction - Zebra-CoT consists of 182,384 samples, providing logical interleaved text-image reasoning trajectories across four main task categories: scientific reasoning, 2D visual reasoning, 3D visual reasoning, and visual logic and strategy games [6][12]. - The dataset overcomes limitations of existing datasets by offering a diverse range of tasks and ensuring high-quality text reasoning data, unlike previous datasets that focused on single tasks or lacked clear reasoning structures [6][18]. Task Coverage - The dataset covers four major task categories: - Scientific reasoning includes geometry, physics, chemistry, and algorithm problems [9]. - 2D visual reasoning encompasses visual search and visual puzzles [9]. - 3D visual reasoning involves multi-hop object counting and robot planning [9]. - Visual logic and strategy games feature chess, checkers, mazes, and more [9]. Data Sources and Processing - Real-world data is sourced from online resources, ensuring high-quality problem extraction and addressing issues of logical connections between modalities [10]. - Synthetic data is generated using templates and visual language models (VLM) to enhance reasoning diversity and expressiveness [10]. Model Fine-tuning and Performance - Fine-tuning the Anole-7B model on Zebra-CoT improved accuracy from 4.2% to 16.9%, a fourfold increase, with notable improvements in visual logic benchmarks [14]. - The Bagel-7B model, after fine-tuning, demonstrated the ability to generate high-quality interleaved visual reasoning chains, showcasing the dataset's effectiveness in developing multi-modal reasoning capabilities [14]. Limitations - Despite its strengths, the dataset relies on template generation for synthetic data, which may limit the diversity and expressiveness of text reasoning [18]. - Some sub-tasks within the dataset have a small sample size, potentially affecting model performance in those areas [18]. - Model fine-tuning results may vary, with some tasks showing insignificant or even decreased performance, indicating a need for further optimization [18].

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Core Viewpoint - GeoScan S1 is presented as the most cost-effective 3D laser scanner in China, featuring lightweight design, one-click operation, and centimeter-level precision for real-time 3D scene reconstruction [1][5]. Group 1: Product Features - The GeoScan S1 can generate point clouds at a rate of 200,000 points per second, with a maximum measurement distance of 70 meters and 360° coverage, supporting large scenes over 200,000 square meters [1][28][31]. - It integrates multiple sensors, including a high-precision IMU and RTK, enabling it to handle complex indoor and outdoor environments effectively [33][46]. - The device supports various data export formats such as PCD, LAS, and PLY, and operates on Ubuntu 20.04, compatible with ROS [22]. Group 2: System Specifications - The system has a relative accuracy of better than 3 cm and an absolute accuracy of better than 5 cm [22]. - The device dimensions are 14.2 cm x 9.5 cm x 45 cm, weighing 1.3 kg without the battery and 1.9 kg with the battery, with a power input range of 13.8V to 24V [22]. - It features a battery capacity of 88.8 Wh, providing approximately 3 to 4 hours of operational time [22][25]. Group 3: Software and Usability - The GeoScan S1 offers a user-friendly interface with simple operation, allowing for quick scanning and immediate data export without complex setups [5][42]. - It includes a 3D Gaussian data collection module for high-fidelity scene restoration, enabling the digital replication of real-world environments [52]. - The software supports both offline and online rendering, enhancing the usability for various applications [5][61]. Group 4: Market Position and Pricing - The company offers multiple versions of the GeoScan S1, including a basic version priced at 19,800 yuan and a 3DGS offline version at 67,800 yuan, catering to diverse customer needs [61][64]. - The product is positioned as having the best price-performance ratio in the industry, integrating multiple sensors and advanced features [5][61].

Core Viewpoint - Humanoid robots are gaining unprecedented attention as multifunctional platforms for complex motion control, human-robot interaction, and general physical intelligence, but achieving efficient whole-body control remains a fundamental challenge [1][2]. Group 1: Overview of Behavior Foundation Model (BFM) - The article discusses the emergence of Behavior Foundation Model (BFM) as a solution to the limitations of traditional controllers, enabling zero-shot or rapid adaptation to various downstream tasks through large-scale pre-training [1][2]. - BFM is defined as a special type of foundational model aimed at controlling agent behavior in dynamic environments, rooted in principles of general foundational models like GPT-4 and CLIP, utilizing large-scale behavior data for pre-training [12][13]. Group 2: Evolution of Humanoid Whole-Body Control Algorithms - The evolution of humanoid whole-body control algorithms is summarized in three stages: model-based controllers, learning-based task-specific controllers, and behavior foundation models [4][6][7]. - Model-based controllers rely heavily on physical models and require complex manual design, while learning-based controllers exhibit poor generalization across tasks [6][7][8]. Group 3: BFM Methodology and Algorithms - The article categorizes current BFM construction methods into three types: goal-conditioned learning, intrinsic reward-driven learning, and forward-backward representation learning [13]. - A notable example of a goal-conditioned learning method is MaskedMimic, which learns foundational motor skills through motion tracking and supports seamless task switching [18][20]. Group 4: Applications and Limitations of BFM - BFM has potential applications in various fields, including humanoid robotics, virtual agents in gaming, industrial 5.0, and medical assistance robots, enabling rapid adaptation to diverse tasks [31][33]. - However, BFM faces limitations such as difficulties in sim-to-real transfer, where discrepancies between simulation and real-world dynamics hinder practical deployment [32][34]. Group 5: Future Research Opportunities and Risks - Future research opportunities include integrating multimodal inputs, developing advanced machine learning systems, and establishing standardized evaluation mechanisms for BFM [36][38]. - Risks associated with BFM include ethical concerns regarding training data biases, data bottlenecks, and the need for robust safety mechanisms to ensure reliability in open environments [36][39].

Core Insights - The article discusses the advancements in vision-language-action models (VLAs) and the challenges faced in the robotics field, particularly in complex dexterous manipulation tasks due to data limitations [3][4]. Group 1: Research Background and Motivation - Current large language models and multimodal models have made significant progress, but the robotics sector lacks a transformative moment akin to "ChatGPT" [3]. - Existing VLAs struggle with dexterous tasks due to reliance on synthetic data or limited remote operation demonstrations, especially in fine manipulation due to high hardware costs [3]. - Human videos contain rich real-world operational data, but learning from them presents challenges such as data heterogeneity, hand motion quantization, cross-modal reasoning, and robot control transfer [3]. Group 2: Core Methodology - The article introduces Physical Instruction Tuning, a paradigm that consists of three phases: pre-training, physical space alignment, and post-training, to transfer human hand movement knowledge to robotic operations [4]. Group 3: Pre-training Phase - The pre-training phase uses human hands as ideal manipulators, treating robotic hands as simplified versions, and trains a foundational VLA on large-scale human videos [6]. - The input includes visual information, language instructions, and parameterized hand movements, optimizing the mapping from vision and language to motion [6][8]. Group 4: Physical Space Alignment - Physical space alignment addresses the interference caused by different camera parameters and coordinate systems through weak perspective projection alignment and motion distribution balancing [10][12]. - The model adapts to specific robots by projecting the robot's proprioceptive state into the model's embedding space, generating executable actions through learnable query tokens [13]. Group 5: Key Technologies - The article discusses motion tokenization and cross-modal fusion, emphasizing the need to retain fine motion precision while discretizing continuous movements [14][17]. - The hand movements are decomposed into wrist and finger movements, each tokenized separately, ensuring reconstruction accuracy through a combination of loss functions [18]. Group 6: Dataset and Experimental Results - The UniHand dataset, comprising over 440,000 task trajectories and 1.3 billion frames, supports large-scale pre-training and includes diverse tasks and data sources [21]. - Experimental results show that the Being-H0 model outperforms baseline models in hand motion generation and translation tasks, demonstrating better spatial accuracy and semantic alignment [22][25]. Group 7: Long Sequence Motion Generation - The model effectively generates long sequences of motion (2-10 seconds) using soft format decoding, which helps maintain trajectory stability [26]. Group 8: Real Robot Operation Experiments - In practical tasks like grasping and placing, Being-H0 shows significantly higher success rates compared to baseline models, achieving 65% and 60% success in unseen toy and cluttered scene tasks, respectively [28].

Core Viewpoint - The article discusses the integration of "brain," "cerebellum," and "body" in embodied intelligent systems, emphasizing the need for improved collaboration and data acquisition for advancing artificial general intelligence (AGI) [2][3][4]. Group 1: Components of Embodied Intelligence - The "brain" is responsible for perception, reasoning, and planning, utilizing large language models and visual language models [2]. - The "cerebellum" focuses on movement, employing motion control algorithms and feedback systems to enhance the naturalness and precision of robotic actions [2]. - The "body" serves as the physical entity that executes the plans generated by the "brain" and the movements coordinated by the "cerebellum," embodying the principle of "knowing and doing" [2]. Group 2: Challenges and Future Directions - There is a need for the "brain" to enhance its reasoning capabilities, enabling it to infer task paths without explicit instructions or maps [3]. - The "cerebellum" should become more intuitive, allowing robots to react flexibly in complex environments and handle delicate objects with care [3]. - The collaboration between the "brain" and "cerebellum" requires improvement, as current communication is slow and responses are delayed, aiming for a seamless interaction system [3]. Group 3: Data Acquisition - The article highlights the challenges in data collection, noting that it is often difficult, expensive, and noisy, which hinders the training of intelligent systems [3]. - There is a call for the development of a training repository that is realistic, diverse, and transferable to enhance data quality and accessibility [3]. Group 4: Expert Discussion - A roundtable discussion is planned with experts from Beijing Academy of Artificial Intelligence and Zhiyuan Robotics to explore recent technological advancements and future pathways for embodied intelligence [4].