【具身智能之心论文辅导重磅上线！多模态大模型/VLA/强化学习/VLN/遥操作/数采/机器人仿 真/real2sim2real/端到端/diffusion等顶会方向1V1定制化辅导】 辅导区间 CCF-A到CCF-C 先看看具身的一些方向，vln、vla、强化、还有一些real2sim2real。很多小白不知道如何下手，选择强化学 习还是vla？传统slam还是vln？哪些方向需要较大算力，哪些不需要？除此之外，什么样的本体适合自己研 究，预算不够怎么办？仿真可以吗？ 对正在从事slam的同学，vln和vla都是一个比较好的切入方向。如果有机械臂，展开vla是一个不错的选择。 除此之外，没有硬件的同学可以尽量在仿真里面或者使用低成本的so-100等硬件完成实验。也有很多低成 本的科研平台，比如移动操作平台。四足和人形更适合强化，vla难度过高。 剩下就是一些方法论的问题了，有好的idea至关重要。对很多新人研究者，一个好的idea需要踩很多次坑。 如果你还是新人，不知道怎么入门，可以看看我们推出的论文辅导。 论文辅导上线了 最近收到很多小伙伴的咨询，其中不乏大模型、传统机器人、机械方向的同学。 ✅ 顶会/顶刊 ...

Group 1 - The establishment of a technical exchange group focused on embodied intelligence, covering areas such as VLA, VLN, remote operation, Diffusion Policy, reinforcement learning, VLA+RL, sim2real, multimodal large models, simulation, motion control, target navigation, mapping and localization, and navigation [1] - Interested individuals can add the assistant's WeChat AIDriver005 to join the community [2] - To expedite the joining process, it is advised to include a note with the institution/school, name, and research direction [3]

Core Insights - The article discusses the evolution and research directions in Visual Language Action (VLA), Visual Language Navigation (VLN), and reinforcement learning in robotics, highlighting the importance of these technologies in enhancing robot capabilities and performance [1][2][5][9]. VLA Direction - VLA systems consist of visual perception processing, language instruction understanding, and action strategy networks, categorized into three paradigms: explicit end-to-end VLA, implicit end-to-end VLA, and hierarchical end-to-end VLA [1][2]. - Explicit end-to-end VLA compresses visual and language information into a joint representation, which is then mapped to action space, leveraging various architectures and models to achieve good performance [1]. - Implicit end-to-end VLA focuses on interpretability by predicting future states using video diffusion models, enhancing the potential for scaling VLA models [2]. - Hierarchical end-to-end VLA aims to utilize the characteristics of large models to improve generalization while maintaining efficiency for downstream execution [2]. VLN Direction - VLN systems are composed of visual language encoders, environmental history representation, and action strategies, requiring effective information compression from visual and language inputs [5][6]. - The choice of encoder and whether to project visual and language representations into a common space are critical issues, with current trends favoring pre-trained models on large datasets and the use of large language models (LLM) for instruction decomposition [6]. - VLN robots operate in a sequential decision-making task, accumulating historical information to inform future actions, with implicit methods representing past information as latent variables [6]. - Object Navigation within VLN emphasizes identifying target objects based on category information, reducing the need for detailed instructions and enhancing exploration capabilities [7]. Reinforcement Learning & Legged Robots - Reinforcement learning is crucial for legged robots, covering various aspects such as kinematics, dynamics, multi-modal sensor fusion, and advanced algorithms for task adaptation [9][10]. - Key areas include gait planning, balance control for bipedal robots, and the application of deep reinforcement learning and imitation learning for multi-task training [10]. - Techniques like domain randomization and safety mechanisms are essential for ensuring successful real-world deployment of robotic systems [10]. Diffusion Policy - The introduction of diffusion models in robotics has led to significant advancements, with the Diffusion Policy achieving an average performance improvement of 46.9% in various simulation environments [21][22]. - The Robotic Diffusion Transformer (RDT), with 1.2 billion parameters, showcases strong zero-shot generalization capabilities and the ability to learn new skills with minimal examples [22]. - The application of diffusion strategies is expanding beyond robotic manipulation to areas like autonomous navigation and dexterous grasping, enhancing task success rates through real-time environmental adaptation [22][23]. - Recent developments in diffusion strategies include advancements in 3D applications and the integration of safety and online reinforcement learning, opening new research avenues [23].

Core Insights - The article discusses the increasing number of submissions to various conferences and the concerns of researchers regarding the suitability of different conferences and the preferences of reviewers [1] - It highlights the active research directions in embodied intelligence, including VLN, VLA, reinforcement learning, and real2sim2real, and provides guidance for newcomers on how to choose their research focus [1][3] - The article promotes a customized paper mentoring service aimed at helping researchers navigate the complexities of paper writing and submission [3][4][5] Group 1 - The article notes that many researchers are anxious about selecting the right conference and understanding which research directions are favored by reviewers [1] - It emphasizes that humanoid robots are particularly active in reinforcement learning and sim2real/real2sim2real research, suggesting that labs with relevant embodiments should explore these areas [1] - It mentions that mechanical arm embodiments are suitable for VLA, VLA+RL, and diffusion policy research, with a high computational power requirement for VLA [1] Group 2 - The article states that quadrupedal robots are also suitable for reinforcement learning research, although there may be fewer innovative points due to prior extensive work in this area [2] - It suggests that combining VLN and VLA with mobile manipulation could be a promising research direction [3] - The article introduces a paper mentoring service that offers one-on-one customized guidance across various top-tier conference topics, emphasizing the importance of having a good idea and navigating potential pitfalls for new researchers [3][4] Group 3 - The mentoring service covers a full process from topic innovation to experimental design, code debugging, paper writing, and submission strategy, aimed at producing high-quality results quickly [4] - It highlights the dual perspective of both industrial and academic value, focusing not only on publishing papers but also on practical applications [5] - The article offers a free matching service for the first ten inquiries, allowing researchers to have in-depth meetings with mentors based on their research direction and academic background [6]

Group 1 - The article discusses suitable research directions for publishing papers, particularly in the fields of embodied intelligence, including vln, vla, reinforcement learning, and real2sim2real [1] - For researchers currently engaged in SLAM, vln and vla are recommended as good entry points, especially for those with robotic arms [1] - The article emphasizes the importance of having a good idea for research, noting that new researchers may need to navigate various challenges to develop innovative concepts [1] Group 2 - A new paper guidance service has been launched, offering customized one-on-one mentoring in various advanced topics such as multimodal large models, VLA, reinforcement learning, and more [2] - The mentoring team consists of PhD holders and researchers from top universities and companies, providing comprehensive support from topic selection to publication strategy [2] - The service aims to bridge the gap between academia and industry, focusing not only on paper publication but also on practical application value [3] Group 3 - The article promotes a free matching service for the first ten inquiries, allowing students to have in-depth meetings with mentors based on their research direction and academic background [5]

Group 1 - The article introduces a new research guidance service focused on embodied intelligence, addressing common challenges faced by newcomers in selecting research topics and methodologies [1][2] - The guidance covers various advanced topics such as multimodal large models, reinforcement learning, and robot simulation, providing tailored one-on-one support [2][3] - The service is backed by a team of experienced mentors from prestigious institutions and leading companies, ensuring high-quality assistance throughout the research process [2][3] Group 2 - The program emphasizes a dual perspective from both industry and academia, aiming not only for publication but also for practical application and value [3] - An introductory offer is available for the first ten inquiries, allowing students to receive personalized mentorship and tailored advice on suitable conferences and journals [4]

Core Viewpoint - The company is seeking collaboration with global practitioners in the embodied intelligence field to enhance capabilities in various areas such as technical services, training, course development, and research guidance [1]. Group 1: Collaboration Opportunities - There is an increasing demand from partners and small companies for the company to empower them through solutions, data collection, technology upgrades, and corporate training [1]. - The company is inviting outstanding partners to join in driving significant industry progress [1]. Group 2: Compensation and Resources - The company will offer high compensation and abundant industry resources to collaborators [2]. Group 3: Focus Areas - Key focus areas for collaboration include but are not limited to: VLA, VLN, Diffusion Policy, Reinforcement Learning, VLA+RL, remote operation, motion capture, sim2real, multimodal large models, simulation, motion control, end-to-end systems, and 3D perception [3]. Group 4: Job Description - The positions are primarily aimed at embodied course development, solution research and development, hardware development, and training collaboration, targeting both B-end (enterprises, universities, research institutes) and C-end (students, job seekers) [4]. Group 5: Contact Information - Interested parties can add WeChat oooops-life for further inquiries [5].

Group 1 - The establishment of the Embodied Intelligence Heart Technology Exchange Group focuses on various advanced technologies including VLA, VLN, remote operation, Diffusion Policy, reinforcement learning, VLA+RL, sim2real, multimodal large models, simulation, motion control, target navigation, mapping and localization, and navigation [1] - Interested individuals can add the assistant's WeChat AIDriver005 to join the community [2] - To expedite the group entry process, it is advised to include a note with the institution/school, name, and research direction [3]

Group 1 - The article announces the recruitment of teachers for embodied intelligence training, targeting both B-end (business) and C-end (consumer) training services, with compensation above industry standards [1] - The training covers various advanced topics including VLA, VLN, remote operation, Diffusion Policy, reinforcement learning, sim2real, multimodal large models, simulation, motion control, and target navigation [2] - B-end training is aimed at enterprises, universities, and research institutions, while C-end training focuses on students and job seekers, with responsibilities including curriculum design and material preparation [3] Group 2 - Candidates are required to have a doctoral degree or higher (including those currently enrolled), with a preference for those who have published two papers in A-level or Q1 journals/conferences, or have two years of industry experience [3] - Interested individuals can add a specified WeChat contact for further inquiries [4]

Group 1 - The article announces the availability of 1v1 paper guidance in the field of embodied intelligence, specifically offering three slots focused on vla, reinforcement learning, and sim2real directions, primarily targeting A and B conferences [1] - Major conferences mentioned include CVPR, ICCV, ECCV, ICLR, CoRL, ICML, and ICRA, indicating the relevance of the guidance to prominent events in the academic community [2] - Interested individuals are encouraged to add a specific WeChat contact for inquiries or to scan a QR code for consultation regarding the embodied paper guidance [3]