Schedule: May 7
Filter:
-
May 7
9:30 - 10:00
Keynote
Embodied AI -
May 7
10:00 - 10:30
Morning Coffee
Embodied AI -
May 7
10:30 - 11:10
Multilingualism of Qwen: From Foundation Model to Applications
AI ModelMultilingual and cross-lingual capabilities significantly boost the flexibility and usefulness of large language models (LLMs). Using Qwen as an example, we'll explore methods to enhance multilingual performance in LLMs, including pre-training, post-training, and evaluation strategies. Additionally, we'll examine the real-world applications of these advancements, demonstrating how multilingual capabilities can create practical solutions that overcome language barriers and promote smooth communication. -
May 7
10:30 - 11:10
AI Open Source for Good: Inclusive Access, Equitable Data, and Accessible Compute
AI InfraThis talk unveils how open source technologies act as catalysts for equitable AI across three pillars. First, inclusive access: We open-source voice datasets tailored for underrepresented groups—such as children and the elderly—to ensure multimodal AI systems understand diverse linguistic patterns and bridge generational divides. Second, equitable data: we have released nearly 100 globally accessible datasets, amassing over 680,000 downloads, empowering developers from any countries to innovate freely. Third, accessible compute: We present FlagOS, an open-source system software that facilitates AI development and deployment across diverse hardware ecosystems—including legacy GPUs and emerging accelerators—while significantly lowering the cost barrier to AI innovation. Collectively, these open-source efforts transform 'AI for Good' into a shared mission—breaking barriers of age, location, and resources to empower anyone to create and benefit from AI. -
May 7
10:30 - 11:10
Finding the Scaling Law of Agents
AI AppsThis talk explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents. To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named CAMEL. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of a society of agents. In particular, we conduct comprehensive studies on cooperation in multi-agent settings. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond: https://github.com/camel-ai/camel. -
May 7
10:30 - 11:10
How to Build Your Humanoid
Embodied AIIn 2021, desperate need for human connection led me to the creation of a 16-DOF data glove. Open-sourcing it made the glove find its way to Rob Knight, creator of the 16-DOF DexHand, who had just begun developing a humanoid with Rémi Cadène's LeRobot. -
May 7
11:10 - 11:50
Open Foundation Models: Scaling Laws and Generalization
AI ModelTo study transferable learning and generalization, derivation of reproducible scaling laws is crucial. We highlight why open foundation models and datasets are essential for this research and highlight challenges in properly measuring generalization. -
May 7
11:10 - 11:50
The Best Practice of Training and Inferencing on Ascend CANN
AI InfraThe AI-oriented, heterogeneous Compute Architecture for Neural Networks (CANN) is a key platform for improving the computing efficiency of Ascend AI processors. It serves as a bridge between upper-layer AI frameworks and lower-layer AI processors and programming. This topic will focus on OpenSource ecosystem about CANN, shows how CANN helps AI sofeware, such as pytorch, vllm and so on, efficiently running on Ascend. -
May 7
11:10 - 11:50
OpenManus: Empowering LLM-based Agent Applications Via Framework and Capability Evolution
AI AppsWe introduce OpenManus, a lightweight and versatile LLM-based multi-agent framework evolved from MetaGPT, designed to enhance adaptability, autonomy, and scalability through advanced reasoning, planning, and effective cross-environment operation. -
May 7
11:10 - 11:50
RoboBrain: A Unified Brain Model for Robotic Manipulation & RoboOS: A Hierarchical Collaborative Framework for RoboBrain and Robot Agents
Embodied AIRoboBrain is an MLLM-based model that enhances robotic manipulation by integrating task planning, object affordance, and trajectory prediction, addressing the limitations of MLLMs in robotic scenarios—particularly in long-horizon tasks—while achieving state-of-the-art performance. Building on RoboBrain’s planning capabilities, we propose the RoboOS hierarchical collaborative framework, which integrates the efficient execution of the robotic skills to enable cross-embodiment collaborative control of multiple robots. -
May 7
12:00 - 14:00
Lunch Break
Embodied AI -
May 7
14:00 - 14:40
Automated Proof Generation for Rust Code Via Self-Evolution
AI ModelEnsuring correctness is crucial for code generation. Formal verification offers a definitive assurance of correctness, but demands substantial human effort in proof construction and hence raises a pressing need for automation. The primary obstacle lies in the severe lack of data—there are much fewer proofs than code snippets for Large Language Models (LLMs) to train upon. In this paper, we introduce SAFE, a framework that overcomes the lack of human-written proofs to enable automated proof generation of Rust code. SAFE establishes a self-evolving cycle where data synthesis and fine-tuning collaborate to enhance the model capability, leveraging the definitive power of a symbolic verifier in telling correct proofs from incorrect ones. SAFE also re-purposes the large number of synthesized incorrect proofs to train the self-debugging capability of the fine-tuned models, empowering them to fix incorrect proofs based on the verifier’s feedback. SAFE demonstrates superior efficiency and precision compared to GPT-4o. Through tens of thousands of synthesized proofs and the self-debugging mechanism, we improve the capability of open-source models, initially unacquainted with formal verification, to automatically write proofs for Rust code. This advancement leads to a significant improvement in performance, achieving a 52.52% accuracy rate in a benchmark crafted by human experts, a significant leap over GPT-4o’s performance of 14.39%. -
May 7
14:00 - 14:40
SGLang: Efficient LLM Serving Engine
AI InfraSGLang is a fast serving engine for LLMs and VLMs. It's fully open-source, incubated by LMSYS Org, with 300+ contributors worldwide. In this talk, we will introduce the key features and performance optimizations in SGLang. -
May 7
14:00 - 14:40
G1 Open Source Dataset and Humanoid Robot from Unitree Robotics
Embodied AIWith artificial intelligence technology move very fast in the past two years, humanoid robot have been one of the most import form to realized embodied AI and AGI, Unitree have been working for more than 8 years in leg robot and 1 year in humanoid robot area. There are three most important parts, algorithm, data and computing capability for realized AGI. Those three part will finally running on physical robots, we believe build robust physical humanoid robot system is key for this ecosystem, and World Large-Scale Model (most people called foundation model) is the key to bring Embodied AI for for humanoid robot, we will share the most important progressing have been made on industry and research side in the past one year, and expect and excited for new progressing will happening in next few years soon. In order to promote the development of the global embodied AI industry, the Unitree G1 robot operation data set is open sourced, adapted to a variety of open source solutions, and continuously updated. -
May 7
14:40 - 15:20
Demysifying LLM Training --- Towards Fully Open-source LLM from Pre-training to Reinforcement Learning
AI ModelRecently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities. Leading this evolution are proprietary LLMs like GPT-4 and GPT-o1, which have captured widespread attention in the AI community for their power and versatility. Simultaneously, open-source LLMs, such as LLaMA and Mistral, have made great contributions to the ever-increasing popularity of LLMs due to the ease to customize and deploy the models across various applications. Although LLMs offer unprecedented opportunities for research and innovation, its commercialization has raised concerns about transparency, reproducibility, and safety. Many open LLM models lack the necessary components (such as training code and data) for full understanding and reproducibility, and some use restrictive licenses whilst claiming to be “open-source”, which may hinder further innovations on LLMs. To mitigate this issue, we follow the Model Openness Framework (MOF), a ranked classification system that rates machine learning models based on their completeness and openness, following principles of open science, open source, open data, and open access. We present a truly open source LLM Moxin 7B and release pre-training code and configurations, training and fine-tuning data, and intermediate and final checkpoints, aiming to make continuous commitments to fully open-source LLMs. We also finetune the Moxin Base model with SOTA post-training framework and instruction data to obtain Moxin Instruct model. To improve the reasoning capability, we further finetune our model with chain-of-thought data distilled from DeepSeek R1, and then use Group Relative Policy Optimization, an efficient and effective reinforcement learning algorithm following DeepSeek R1, to finetune our model, leading to the Moxin Reasoning model. -
May 7
14:40 - 15:20
Open-source Intelligent Computing Integrated Management and Utilization Foundational Software - SCOW and CraneSched
AI InfraThe Peking University Computing Center is dedicated to developing general foundational software for both supercomputing (HPC) and intelligent computing (AI computing). In the field of HPC and AI computing, it has developed several flagship foundational software systems, including SCOW and CraneSched. OpenSCOW (https://github.com/PKUHPC/OpenSCOW) provides a graphical user interface (GUI) that allows developers to flexibly manage supercomputing and AI computing resources for AI model training and inference. It has already been deployed across 56 computing centers, including 34 universities and 12 enterprises in China. CraneSched ( https://github.com/PKUHPC/CraneSched) is a high-performance scheduling and orchestration system for HPC and AI computing tasks. It supports large-scale model training with exceptional performance and has been adopted by 8 universities and 1 enterprise in China. -
May 7
14:40 - 15:20
OAKS: The Open Agentic AI Knowledge Stack
AI AppsIn this talk, we present an OSS AI architecture for Agentic AI+Knowledge. Encapsulating business knowledge is key for agents, and focusing on AI memory and scalable frameworks around Knowledge Graphs is a good foundation to build an OSS AI ecosystem for agents. -
May 7
14:40 - 15:20
Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation
Embodied AITo accelerate continuous skill acquisition through reusable motion-aware primitives, we propose Primitive Prompt Learning (PPL). PPL enables lifelong robot manipulation by optimizing new prompts alongside pretrained ones and demonstrates superior performance in both simulated and real-world tasks. -
May 7
15:20 - 15:40
Afternoon Coffee
Embodied AI -
May 7
15:40 - 16:20
Small but Mighty: How MiniCPM Made Breakthroughs in the Global Open-Source AI Landscape
AI ModelMiniCPM, nicknamed 'ModelBest's Little Steel Cannon'—which includes the large language model MiniCPM and the multimodal large model MiniCPM-V—has gained widespread recognition in the global AI community due to its highly efficient and cost-effective nature, embodying the principle of 'punching above its weight' These projects have cumulatively received over 26,000 stars on GitHub, with total downloads exceeding 7 million across the web, becoming benchmark works in the field of on-device AI. -
May 7
15:40 - 16:20
Verl: Hybrid Controller-based RLHF System
AI Infraverl is a flexible, efficient and production-ready RL training library for LLMs. This talk will share the ideas in designing a hybrid-controller system and the benefits of this system in efficient large-scale RL training. -
May 7
15:40 - 16:20
TBD
Embodied AI -
May 7
15:40 - 16:20
Database for Agents Memory, The Right Way
AI AppsIn this session, we will explore best practices for leveraging serverless SQL databases to support the sophisticated memory requirements of AI agents. We will delve into essential technical requirements, including schema design considerations, efficient indexing strategies, consistency vs. availability trade-offs, handling real-time updates, and seamless integration with AI workflows. Additionally, we'll discuss common pitfalls, performance optimization techniques, and how to achieve cost-efficiency without sacrificing responsiveness or data integrity. Attendees will gain actionable insights into architecting robust, scalable memory storage solutions that enhance the capability, adaptability, and overall effectiveness of AI agents in production environments. -
May 7
16:20 - 17:00
Pre-training of Smol and Large LLM
AI ModelExplaining what's new in pre-training: optimization tricks, MoE, stability hacks, and handling long contexts—everything you need to build better LLMs. -
May 7
16:20 - 17:00
Datasets and Infrastructure for DeepSeek-R1 Style Reinforcement Learning (GRPO)
AI InfraWe will walk through everything you need to know about the latest in reinforcement learning for LLMs, datasets and infrastructure, down to training your own small reasoning LLM that can write code locally. -
May 7
16:20 - 17:00
Agentic Search
AI AppsThe talk covers basic concepts and use-cases of agentic search. -
May 7
16:20 - 17:00
AI Empowers IoT Devices to Drive the Dual Engines of Industrial Transformation
Embodied AIAmidst the contemporary surge of digital transformation, the symbiotic convergence of artificial intelligence (AI) and IoT devices has emerged as a pivotal catalyst for industrial evolution. AI's infusion of autonomous learning, intelligent decision-making, and seamless interaction capabilities into intelligent hardware has redefined the paradigm, elevating conventional tools to the status of sophisticated, intelligent collaborators. This technological metamorphosis is evident across a spectrum of applications, from the bespoke experiences delivered by smart home ecosystems to the pinpoint precision of operations within industrial automation frameworks. The ramifications of this fusion extend beyond mere enhancement; it has become a driving force propelling the digital reinvention of traditional industries and the emergence of new sectors. In this presentation, we will delve into the intricate dynamics of the integration trends between AI and IoT devices, explore groundbreaking technological innovations, examine a diverse array of application scenarios, and assess the profound and far-reaching impacts on industrial transformation. By doing so, we aim to peer into the future, where the potential for growth and innovation is boundless, and to chart a course that offers novel insights and strategic directions for the continued advancement of our industry. -
May 7
17:00 - 18:00
Spotlight Demos
Embodied AIGOSIM AI Spotlight Finalists will present their projects in a short pitch. -
May 7
18:00 - 21:00
Social Gathering
Embodied AI
-
May 7
9:30 - 10:00
Keynote
AI Model -
May 7
10:00 - 10:30
Morning Coffee
AI Model -
May 7
10:30 - 11:10
Multilingualism of Qwen: From Foundation Model to Applications
AI ModelMultilingual and cross-lingual capabilities significantly boost the flexibility and usefulness of large language models (LLMs). Using Qwen as an example, we'll explore methods to enhance multilingual performance in LLMs, including pre-training, post-training, and evaluation strategies. Additionally, we'll examine the real-world applications of these advancements, demonstrating how multilingual capabilities can create practical solutions that overcome language barriers and promote smooth communication. -
May 7
11:10 - 11:50
Open Foundation Models: Scaling Laws and Generalization
AI ModelTo study transferable learning and generalization, derivation of reproducible scaling laws is crucial. We highlight why open foundation models and datasets are essential for this research and highlight challenges in properly measuring generalization. -
May 7
12:00 - 14:00
Lunch Break
AI Model -
May 7
14:00 - 14:40
Automated Proof Generation for Rust Code Via Self-Evolution
AI ModelEnsuring correctness is crucial for code generation. Formal verification offers a definitive assurance of correctness, but demands substantial human effort in proof construction and hence raises a pressing need for automation. The primary obstacle lies in the severe lack of data—there are much fewer proofs than code snippets for Large Language Models (LLMs) to train upon. In this paper, we introduce SAFE, a framework that overcomes the lack of human-written proofs to enable automated proof generation of Rust code. SAFE establishes a self-evolving cycle where data synthesis and fine-tuning collaborate to enhance the model capability, leveraging the definitive power of a symbolic verifier in telling correct proofs from incorrect ones. SAFE also re-purposes the large number of synthesized incorrect proofs to train the self-debugging capability of the fine-tuned models, empowering them to fix incorrect proofs based on the verifier’s feedback. SAFE demonstrates superior efficiency and precision compared to GPT-4o. Through tens of thousands of synthesized proofs and the self-debugging mechanism, we improve the capability of open-source models, initially unacquainted with formal verification, to automatically write proofs for Rust code. This advancement leads to a significant improvement in performance, achieving a 52.52% accuracy rate in a benchmark crafted by human experts, a significant leap over GPT-4o’s performance of 14.39%. -
May 7
14:40 - 15:20
Demysifying LLM Training --- Towards Fully Open-source LLM from Pre-training to Reinforcement Learning
AI ModelRecently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities. Leading this evolution are proprietary LLMs like GPT-4 and GPT-o1, which have captured widespread attention in the AI community for their power and versatility. Simultaneously, open-source LLMs, such as LLaMA and Mistral, have made great contributions to the ever-increasing popularity of LLMs due to the ease to customize and deploy the models across various applications. Although LLMs offer unprecedented opportunities for research and innovation, its commercialization has raised concerns about transparency, reproducibility, and safety. Many open LLM models lack the necessary components (such as training code and data) for full understanding and reproducibility, and some use restrictive licenses whilst claiming to be “open-source”, which may hinder further innovations on LLMs. To mitigate this issue, we follow the Model Openness Framework (MOF), a ranked classification system that rates machine learning models based on their completeness and openness, following principles of open science, open source, open data, and open access. We present a truly open source LLM Moxin 7B and release pre-training code and configurations, training and fine-tuning data, and intermediate and final checkpoints, aiming to make continuous commitments to fully open-source LLMs. We also finetune the Moxin Base model with SOTA post-training framework and instruction data to obtain Moxin Instruct model. To improve the reasoning capability, we further finetune our model with chain-of-thought data distilled from DeepSeek R1, and then use Group Relative Policy Optimization, an efficient and effective reinforcement learning algorithm following DeepSeek R1, to finetune our model, leading to the Moxin Reasoning model. -
May 7
15:20 - 15:40
Afternoon Coffee
AI Model -
May 7
15:40 - 16:20
Small but Mighty: How MiniCPM Made Breakthroughs in the Global Open-Source AI Landscape
AI ModelMiniCPM, nicknamed 'ModelBest's Little Steel Cannon'—which includes the large language model MiniCPM and the multimodal large model MiniCPM-V—has gained widespread recognition in the global AI community due to its highly efficient and cost-effective nature, embodying the principle of 'punching above its weight' These projects have cumulatively received over 26,000 stars on GitHub, with total downloads exceeding 7 million across the web, becoming benchmark works in the field of on-device AI. -
May 7
16:20 - 17:00
Pre-training of Smol and Large LLM
AI ModelExplaining what's new in pre-training: optimization tricks, MoE, stability hacks, and handling long contexts—everything you need to build better LLMs. -
May 7
17:00 - 18:00
Spotlight Demos
AI ModelGOSIM AI Spotlight Finalists will present their projects in a short pitch. -
May 7
18:00 - 21:00
Social Gathering
AI Model
-
May 7
9:30 - 10:00
Keynote
AI Infra -
May 7
10:00 - 10:30
Morning Coffee
AI Infra -
May 7
10:30 - 11:10
AI Open Source for Good: Inclusive Access, Equitable Data, and Accessible Compute
AI InfraThis talk unveils how open source technologies act as catalysts for equitable AI across three pillars. First, inclusive access: We open-source voice datasets tailored for underrepresented groups—such as children and the elderly—to ensure multimodal AI systems understand diverse linguistic patterns and bridge generational divides. Second, equitable data: we have released nearly 100 globally accessible datasets, amassing over 680,000 downloads, empowering developers from any countries to innovate freely. Third, accessible compute: We present FlagOS, an open-source system software that facilitates AI development and deployment across diverse hardware ecosystems—including legacy GPUs and emerging accelerators—while significantly lowering the cost barrier to AI innovation. Collectively, these open-source efforts transform 'AI for Good' into a shared mission—breaking barriers of age, location, and resources to empower anyone to create and benefit from AI. -
May 7
11:10 - 11:50
The Best Practice of Training and Inferencing on Ascend CANN
AI InfraThe AI-oriented, heterogeneous Compute Architecture for Neural Networks (CANN) is a key platform for improving the computing efficiency of Ascend AI processors. It serves as a bridge between upper-layer AI frameworks and lower-layer AI processors and programming. This topic will focus on OpenSource ecosystem about CANN, shows how CANN helps AI sofeware, such as pytorch, vllm and so on, efficiently running on Ascend. -
May 7
12:00 - 14:00
Lunch Break
AI Infra -
May 7
14:00 - 14:40
SGLang: Efficient LLM Serving Engine
AI InfraSGLang is a fast serving engine for LLMs and VLMs. It's fully open-source, incubated by LMSYS Org, with 300+ contributors worldwide. In this talk, we will introduce the key features and performance optimizations in SGLang. -
May 7
14:40 - 15:20
Open-source Intelligent Computing Integrated Management and Utilization Foundational Software - SCOW and CraneSched
AI InfraThe Peking University Computing Center is dedicated to developing general foundational software for both supercomputing (HPC) and intelligent computing (AI computing). In the field of HPC and AI computing, it has developed several flagship foundational software systems, including SCOW and CraneSched. OpenSCOW (https://github.com/PKUHPC/OpenSCOW) provides a graphical user interface (GUI) that allows developers to flexibly manage supercomputing and AI computing resources for AI model training and inference. It has already been deployed across 56 computing centers, including 34 universities and 12 enterprises in China. CraneSched ( https://github.com/PKUHPC/CraneSched) is a high-performance scheduling and orchestration system for HPC and AI computing tasks. It supports large-scale model training with exceptional performance and has been adopted by 8 universities and 1 enterprise in China. -
May 7
15:20 - 15:40
Afternoon Coffee
AI Infra -
May 7
15:40 - 16:20
Verl: Hybrid Controller-based RLHF System
AI Infraverl is a flexible, efficient and production-ready RL training library for LLMs. This talk will share the ideas in designing a hybrid-controller system and the benefits of this system in efficient large-scale RL training. -
May 7
16:20 - 17:00
Datasets and Infrastructure for DeepSeek-R1 Style Reinforcement Learning (GRPO)
AI InfraWe will walk through everything you need to know about the latest in reinforcement learning for LLMs, datasets and infrastructure, down to training your own small reasoning LLM that can write code locally. -
May 7
17:00 - 18:00
Spotlight Demos
AI InfraGOSIM AI Spotlight Finalists will present their projects in a short pitch. -
May 7
18:00 - 21:00
Social Gathering
AI Infra
-
May 7
9:30 - 10:00
Keynote
AI Apps -
May 7
10:00 - 10:30
Morning Coffee
AI Apps -
May 7
10:30 - 11:10
Finding the Scaling Law of Agents
AI AppsThis talk explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents. To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named CAMEL. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of a society of agents. In particular, we conduct comprehensive studies on cooperation in multi-agent settings. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond: https://github.com/camel-ai/camel. -
May 7
11:10 - 11:50
OpenManus: Empowering LLM-based Agent Applications Via Framework and Capability Evolution
AI AppsWe introduce OpenManus, a lightweight and versatile LLM-based multi-agent framework evolved from MetaGPT, designed to enhance adaptability, autonomy, and scalability through advanced reasoning, planning, and effective cross-environment operation. -
May 7
12:00 - 14:00
Lunch Break
AI Apps -
May 7
14:00 - 14:40
TBD
AI Apps -
May 7
14:40 - 15:20
OAKS: The Open Agentic AI Knowledge Stack
AI AppsIn this talk, we present an OSS AI architecture for Agentic AI+Knowledge. Encapsulating business knowledge is key for agents, and focusing on AI memory and scalable frameworks around Knowledge Graphs is a good foundation to build an OSS AI ecosystem for agents. -
May 7
15:20 - 15:40
Afternoon Coffee
AI Apps -
May 7
15:40 - 16:20
Database for Agents Memory, The Right Way
AI AppsIn this session, we will explore best practices for leveraging serverless SQL databases to support the sophisticated memory requirements of AI agents. We will delve into essential technical requirements, including schema design considerations, efficient indexing strategies, consistency vs. availability trade-offs, handling real-time updates, and seamless integration with AI workflows. Additionally, we'll discuss common pitfalls, performance optimization techniques, and how to achieve cost-efficiency without sacrificing responsiveness or data integrity. Attendees will gain actionable insights into architecting robust, scalable memory storage solutions that enhance the capability, adaptability, and overall effectiveness of AI agents in production environments. -
May 7
16:20 - 17:00
Agentic Search
AI AppsThe talk covers basic concepts and use-cases of agentic search. -
May 7
17:00 - 18:00
Spotlight Demos
AI AppsGOSIM AI Spotlight Finalists will present their projects in a short pitch. -
May 7
18:00 - 21:00
Social Gathering
AI Apps
-
May 7
9:30 - 10:00
Keynote
Embodied AI -
May 7
10:00 - 10:30
Morning Coffee
Embodied AI -
May 7
10:30 - 11:10
How to Build Your Humanoid
Embodied AIIn 2021, desperate need for human connection led me to the creation of a 16-DOF data glove. Open-sourcing it made the glove find its way to Rob Knight, creator of the 16-DOF DexHand, who had just begun developing a humanoid with Rémi Cadène's LeRobot. -
May 7
11:10 - 11:50
RoboBrain: A Unified Brain Model for Robotic Manipulation & RoboOS: A Hierarchical Collaborative Framework for RoboBrain and Robot Agents
Embodied AIRoboBrain is an MLLM-based model that enhances robotic manipulation by integrating task planning, object affordance, and trajectory prediction, addressing the limitations of MLLMs in robotic scenarios—particularly in long-horizon tasks—while achieving state-of-the-art performance. Building on RoboBrain’s planning capabilities, we propose the RoboOS hierarchical collaborative framework, which integrates the efficient execution of the robotic skills to enable cross-embodiment collaborative control of multiple robots. -
May 7
12:00 - 14:00
Lunch Break
Embodied AI -
May 7
14:00 - 14:40
G1 Open Source Dataset and Humanoid Robot from Unitree Robotics
Embodied AIWith artificial intelligence technology move very fast in the past two years, humanoid robot have been one of the most import form to realized embodied AI and AGI, Unitree have been working for more than 8 years in leg robot and 1 year in humanoid robot area. There are three most important parts, algorithm, data and computing capability for realized AGI. Those three part will finally running on physical robots, we believe build robust physical humanoid robot system is key for this ecosystem, and World Large-Scale Model (most people called foundation model) is the key to bring Embodied AI for for humanoid robot, we will share the most important progressing have been made on industry and research side in the past one year, and expect and excited for new progressing will happening in next few years soon. In order to promote the development of the global embodied AI industry, the Unitree G1 robot operation data set is open sourced, adapted to a variety of open source solutions, and continuously updated. -
May 7
14:40 - 15:20
Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation
Embodied AITo accelerate continuous skill acquisition through reusable motion-aware primitives, we propose Primitive Prompt Learning (PPL). PPL enables lifelong robot manipulation by optimizing new prompts alongside pretrained ones and demonstrates superior performance in both simulated and real-world tasks. -
May 7
15:20 - 15:40
Afternoon Coffee
Embodied AI -
May 7
15:40 - 16:20
TBD
Embodied AI -
May 7
16:20 - 17:00
AI Empowers IoT Devices to Drive the Dual Engines of Industrial Transformation
Embodied AIAmidst the contemporary surge of digital transformation, the symbiotic convergence of artificial intelligence (AI) and IoT devices has emerged as a pivotal catalyst for industrial evolution. AI's infusion of autonomous learning, intelligent decision-making, and seamless interaction capabilities into intelligent hardware has redefined the paradigm, elevating conventional tools to the status of sophisticated, intelligent collaborators. This technological metamorphosis is evident across a spectrum of applications, from the bespoke experiences delivered by smart home ecosystems to the pinpoint precision of operations within industrial automation frameworks. The ramifications of this fusion extend beyond mere enhancement; it has become a driving force propelling the digital reinvention of traditional industries and the emergence of new sectors. In this presentation, we will delve into the intricate dynamics of the integration trends between AI and IoT devices, explore groundbreaking technological innovations, examine a diverse array of application scenarios, and assess the profound and far-reaching impacts on industrial transformation. By doing so, we aim to peer into the future, where the potential for growth and innovation is boundless, and to chart a course that offers novel insights and strategic directions for the continued advancement of our industry. -
May 7
17:00 - 18:00
Spotlight Demos
Embodied AIGOSIM AI Spotlight Finalists will present their projects in a short pitch. -
May 7
18:00 - 21:00
Social Gathering
Embodied AI
Grab your GOSIM AI Paris ticket
Paris, France
May 6-7, 2025
Paris, the City of Light, transforms into the City of Artificial Brilliance this May. GOSIM AI 2025 invites visionaries, disruptors, and pioneers to converge at Station F—a crucible of innovation—to shape the next frontier of AI.