我们将提供高于行业平均水平的薪酬以及丰富的行业资源。 最近收到社区内很多同学关于VLA和RL相关内容的咨询，也希望具身智能之心能够有更深入的讲解。在 此，我们向全平台粉丝招募1名VLA+RL方向的课程讲师，和我们一起开发这个方向的在线课程。 详细内容欢迎添加微信：oooops-life咨询。 一些要求 需是VLA+RL的研究方向，学术界我们希望是博士及以上（包含在读），手握相关方向的顶会。工业界希 望您有一定的实战经验和真机调试经验。 待遇说明 具身智能之心是国内首个具身全栈技术交流社区，聚集了大量VLA和RL相关方向的同学。 ...

点击下方 卡片 ，关注" 具身智能 之心 "公众号 RLinf通过标准化的接口，支持主流VLA模型及基于CPU与GPU的模拟器，并率先实现了对π0和π0.5模型系列的强化学习微调，欢迎大家star&follow。 | | | Evaluation results on the four LIBERO task groups | | | | | | --- | --- | --- | --- | --- | --- | --- | | Model | | | | LIBERO | | | | | Spatial | Object | Goal | Long | Avg. | A Avg. | | Full Dataset SFT | | | | | | | | Octo | 78.9% | 85.7% | 84.6% | 51.1% | 75.1% | - | | OpenVLA | 84.7% | 88.4% | 79.2% | 53.7% | 76.5% | - | | Tlfast | 96.4% | 96.8% | 88.6% | 60.2% | 85.5% | - | | OpenVLA-OFT | ...

Core Insights - The article discusses the recruitment of a lecturer for an online course focused on VLA (Vision-Language Alignment) and RL (Reinforcement Learning) [1][2] - The community aims to enhance understanding and knowledge sharing in the field of embodied intelligence, specifically in VLA and RL [3] Recruitment Requirements - Candidates should have a research background in VLA and RL, preferably holding a PhD or being a doctoral student, with publications in top conferences [2] - Practical experience in the industry, including hands-on debugging with real machines, is also desired [2] Community Overview - The company, "Embodied Intelligence Heart," is identified as the first comprehensive technical exchange community in China, focusing on VLA and RL [3] - The community has attracted a significant number of individuals interested in these research areas [3] Compensation and Resources - The company offers compensation that is above the industry average, along with access to extensive industry resources [4]

Core Insights - The article discusses the importance of reinforcement learning (RL) in the development of embodied intelligent robots, highlighting its applications in various complex tasks such as stair climbing, running, and dancing [3][9] - It emphasizes the challenges faced by newcomers in the field of reinforcement learning, particularly in producing quality research papers due to the complexity and breadth of the subject [6][10] - To address these challenges, a specialized 1v6 mentoring course in reinforcement learning has been introduced, aimed at helping students produce publishable research papers [7][10] Group 1: Reinforcement Learning Applications - Reinforcement learning is crucial for gait control in humanoid and quadruped robots, enabling them to perform tasks in challenging environments [3][9] - The VLA+RL approach for robotic arms is gaining popularity in academia, enhancing the efficiency and smoothness of robotic operations [4][9] Group 2: Course Structure and Objectives - The 1v6 mentoring course is designed for graduate students and others needing guidance on research papers, featuring weekly live sessions and dedicated teaching assistants [8][10] - The course spans 14 weeks of intensive online training followed by 8 weeks of maintenance support, focusing on various aspects of research paper production, including idea confirmation, project implementation, and writing refinement [10][18] Group 3: Course Content and Deliverables - The curriculum includes topics such as reinforcement learning fundamentals, simulation environments, and writing guidance, with a focus on producing a research paper suitable for top conferences and journals [10][19] - Students will receive structured templates and support for writing and submission processes, ensuring they meet the standards of leading academic publications [10][29] Group 4: Instructor and Support - The course is led by experienced instructors with backgrounds in embodied intelligence and robotics, providing both theoretical knowledge and practical insights [27] - Continuous support is offered through a dedicated WeChat group for real-time Q&A, enhancing the learning experience [18][27]

Core Insights - Reinforcement Learning (RL) remains a significant field with ongoing developments and applications in various domains, including robotics and product optimization [1][2][3] - The importance of gait control in embodied intelligent robots is highlighted, with RL being the primary method for achieving complex movements [2][8] - The complexity of RL poses challenges for newcomers, necessitating structured guidance to facilitate entry into the field and successful paper publication [5][9] Group 1: Importance of Reinforcement Learning - RL is not an outdated discipline; it continues to be relevant with numerous applications in robotics, such as humanoid and quadruped robots [1][2] - Companies like Yushun and Zhiyuan utilize RL for training robots to perform various challenging tasks, including climbing stairs and running [2][8] - The integration of RL with Variable Length Action (VLA) in robotic arms is gaining traction in academic research, enhancing the efficiency of robotic operations [3][8] Group 2: Challenges in Learning and Research - The extensive and complex nature of RL makes it difficult for beginners to navigate, often leading to frustration and abandonment of studies [5][9] - A lack of a comprehensive learning framework can result in repeated mistakes and missed opportunities in research [6][9] - The introduction of a specialized 1v6 mentoring course aims to address these challenges by providing structured support for students in the RL field [6][9] Group 3: Course Structure and Offerings - The course spans 14 weeks of intensive online guidance followed by 8 weeks of maintenance support, focusing on producing a publishable paper [10][11] - Weekly live sessions will cover various topics, including RL fundamentals, simulation environments, and writing guidance, with a focus on practical applications [17][21] - Participants will have the opportunity to work on specific ideas related to quadruped, humanoid, and robotic arm research, with a structured approach to project development and writing [18][25]

Core Insights - The article discusses the launch of RLinf, a large-scale reinforcement learning framework aimed at embodied intelligence, highlighting its flexibility and efficiency in system design [2][3]. Group 1: System Design - RLinf-VLA provides a unified and efficient platform for VLA+RL research, achieving a throughput improvement of 2.27 times compared to baseline platforms [2][5]. - It supports multiple simulators (LIBERO and ManiSkill), allowing for integrated training across different environments [5]. - The system allows for easy switching between various VLA models and RL algorithms, reducing the workload for model adaptation [5]. Group 2: Performance Overview - A single unified model achieved a success rate of 98.11% across 130 tasks in LIBERO and 97.66% in 25 pick & place tasks in ManiSkill [6]. - The RLinf-VLA framework demonstrates superior zero-shot generalization capabilities when deployed on real robotic systems compared to strategies trained with SFT [6][45]. Group 3: Algorithm Design - The framework introduces several design optimizations, including lightweight critics and trajectory length normalization, which significantly enhance training efficiency [9][21][25]. - It supports three levels of output granularity (token-level, action-level, chunk-level) for both advantage and log-probability calculations, allowing for flexible training strategies [12][14][22]. Group 4: Experimental Results - In multi-task experiments, the OpenVLA model showed performance improvements of 45% to 70% over baseline models in ManiSkill tasks [31]. - The RLinf-VLA framework demonstrated high efficiency in training, with significant reductions in training time compared to baseline methods [43][44]. Group 5: Real-World Application - The RLinf-VLA framework was successfully deployed on the Franka Panda robotic arm, showcasing its ability to generalize from simulation to real-world tasks [45].

Group 1 - The core viewpoint is that the most important aspect for the robotics industry in the next 2 to 5 years is the development of end-to-end embodied intelligent AI models [1][24] - The current challenge in the robotics field is not the hardware performance, which is deemed sufficient, but rather the inadequacy of embodied intelligent AI models [1][18] - There is a misconception that the data issue is the primary concern; however, the real problem lies in the model architecture, which is not yet good or unified enough [1][21] Group 2 - The VLA (Vision-Language-Action) model combined with Reinforcement Learning (RL) is seen as insufficient and requires further upgrades and optimization [2][21] - The company has developed various models of quadruped and humanoid robots, with the quadruped model GO2 being the most shipped globally in recent years [3][4] - The humanoid robot G1 has become a representative model in the humanoid robot sector, achieving significant sales and market presence [5][6] Group 3 - The company emphasizes the importance of making robots capable of performing tasks rather than just for entertainment or display purposes [9][14] - Recent advancements in AI technology have led to improved performance in robot movements, including complex terrain navigation [11][12] - The company has focused on developing its core components, including motors and sensors, to enhance the performance and cost-effectiveness of its robots [10][24] Group 4 - The robotics industry is experiencing significant growth, with many companies reporting a 50% to 100% increase in business due to rising demand and supportive policies [16][17] - The global interest in humanoid robots is increasing, with major companies like Tesla planning to mass-produce humanoid robots [17][18] - The future of robotics will likely involve distributed computing to manage the computational demands of robots effectively [25][26]