PhysicalAI-Robotics-GR00T-X-Embodiment-Sim

Github Repo: Isaac GR00T N1 We provide a set of datasets used for post-training of GR00T N1. Each dataset is a collection of trajectories from different robot embodiments and tasks.

  Cross-embodied bimanual manipulation: 9k trajectories

Dataset Name

trajectories

bimanual_panda_gripper.Threading 1000

bimanual_panda_hand.LiftTray 1000

bimanual_panda_gripper.ThreePieceAssembly 1000… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim.

Open-X-Embodiment robot manipulation dataset, see https://robotics-transformer-x.github.io/

This paper introduces Alter3, a humanoid robot that demonstrates spontaneous motion generation through the integration of GPT-4, a cutting-edge Large Language Model (LLM). This integration overcomes the challenge of applying LLMs to direct robot control, which typically struggles with the hardware-specific nuances of robotic operation. By translating linguistic descriptions of human actions into robotic movements via programming, Alter3 can autonomously perform a diverse range of actions, such as adopting a “selfie” pose or simulating a “ghost.” This approach not only shows Alter3’s few-shot learning capabilities but also its adaptability to verbal feedback for pose adjustments without manual fine-tuning. This research advances the field of humanoid robotics by bridging linguistic concepts with physical embodiment and opens new avenues for exploring spontaneity in humanoid robots.

The datasets contains the motor performance metrics, the gaze fixation time ratios, and the questionnaire responses for a study involving a motor task with a rehabilitation assistive robot and an immersive virtual reality head-mounted display. The study was performed in the Motor Learning and Neurorehabilitation Laboratory at University of Bern. All data are stored in “csv” files. The variables inside the files are explained in “DataFrameDescription.rtf”. For questions, please contact nicolas.wenk@unibe.ch or L.MarchalCrespo@tudelft.nl.

Open X-Embodiment: Robotic learning datasets and RT-X models

As robots become more ubiquitous, they will increasingly need to behave as our team partners and smoothly adapt to the (adaptive) human team behaviors to establish successful patterns of collaboration over time. A substantial amount of adaptations present themselves through subtle and unconscious interactions, which are difficult to observe. Our research aims to bring about awareness of co-adaptation that enables team learning. This paper presents an experimental paradigm that uses a physical human-robot collaborative task environment to explore emergent human-robot co-adaptions and derive the interaction patterns (i.e., the targeted awareness of co-adaptation). The paradigm provides a tangible human-robot interaction (i.e., a leash) that facilitates the expression of unconscious adaptations, such as “leading” (e.g., pulling the leash) and “following” (e.g., letting go of the leash) in a search-and-navigation task. The task was executed by 18 participants, after which we systematically annotated videos of their behavior. We discovered that their interactions could be described by four types of adaptive interactions: stable situations, sudden adaptations, gradual adaptations and active negotiations. From these types of interactions we have created a language of interaction patterns that can be used to describe tacit co-adaptation in human-robot collaborative contexts. This language can be used to enable communication between collaborating humans and robots in future studies, to let them share what they learned and support them in becoming aware of their implicit adaptations.

The global Embodied Intelligent Robot Dexterous Hand market size was valued at USD XXX million in 2025 and is expected to reach USD XXX million by 2033, growing at a CAGR of XX% from 2025 to 2033. The market growth is primarily driven by the increasing adoption of robotics in various industries, advancements in artificial intelligence (AI), and the growing need for dexterous and versatile robotic hands. The Embodied Intelligent Robot Dexterous Hand market is segmented into different types, applications, and regions. The types segment includes Rotational Drive Dexterous Hand and Linear Drive Dexterous Hand. The application segment includes Commercial Robots, Household Service Robots, Scientific Research, and Other. The regions covered in the market study include North America, South America, Europe, Middle East & Africa, and Asia Pacific. The study provides a comprehensive analysis of the market dynamics, growth drivers, restraints, and challenges, along with detailed regional and competitive insights.

RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation

Accepted by Robotics: Science and Systems (RSS) 2025.

  💾 Overview of RoboMIND 💾







  🤖 Composition of RoboMIND 🤖

We present RoboMIND (Multi-embodiment Intelligence Normative Dataset and Benchmark for Robot Manipulation), a comprehensive dataset featuring 107k real-world demonstration trajectories spanning 479 distinct tasks and involving 96 unique object classes. The… See the full description on the dataset page: https://huggingface.co/datasets/x-humanoid-robomind/RoboMIND.

The Embodied Artificial Intelligence (AI) Robot market is experiencing significant growth, driven by advancements in AI, robotics, and sensor technologies. The convergence of these fields is enabling the development of robots capable of sophisticated perception, interaction, and decision-making in real-world environments. This is fueling demand across various sectors, including healthcare (surgical assistance, elderly care), manufacturing (automation, logistics), and consumer applications (home assistance, entertainment). While the precise market size in 2025 is unavailable, based on industry reports showing similar markets reaching billions of dollars and considering a conservative CAGR of 20% (a reasonable estimate given the rapid technological advancements), we can estimate the 2025 market size to be approximately $2 billion. This figure is projected to grow substantially over the forecast period (2025-2033), reaching an estimated $10 billion by 2033. Key players like Sony, SoftBank Robotics, and Boston Dynamics are leading innovation, while newer entrants such as Figure and Unitree are rapidly gaining traction with specialized offerings. Challenges include high development costs, regulatory hurdles, and ethical concerns surrounding AI autonomy, but the long-term growth potential remains exceptionally strong. The market's growth trajectory is influenced by several factors. Technological advancements, particularly in areas like computer vision, natural language processing, and machine learning, are continuously improving the capabilities of embodied AI robots. Furthermore, increasing demand for automation across various industries is driving adoption. However, significant restraints remain. The high initial investment cost required for development and deployment poses a barrier for smaller companies and consumers. Addressing ethical considerations related to AI safety and job displacement will be crucial for sustainable market growth. Market segmentation is largely driven by application (healthcare, manufacturing, etc.) and geographic region. North America and Asia are expected to dominate the market initially, with Europe following closely. The ongoing technological advancements and increasing investments in AI research suggest continued expansion in the coming years, despite inherent challenges.

Embodied AI Market Size

As per the latest insights from Market.us, the Global Embodied AI Market is projected to reach USD 10.75 billion by 2034, growing at a CAGR of 15.7% from 2025 to 2034. The market, valued at USD 2.5 billion in 2024, is expanding due to the rising adoption of AI-powered robotics, autonomous systems, and interactive virtual assistants across various industries.

North America led the market in 2024, holding a 41.3% share, with revenues exceeding USD 1.03 billion. The region's leadership is driven by advancements in robotics, smart automation, and the integration of AI in healthcare, manufacturing, and consumer electronics. As businesses seek intelligent, human-like AI interactions, the demand for embodied AI is expected to accelerate, shaping the future of robotics and automation globally.

Understanding the world in terms of objects and the possible interactions with them is an important cognitive ability. However, current world models adopted in reinforcement learning typically lack this structure and represent the world state in a global latent vector. To address this, we propose FOCUS, a model-based agent that learns an object-centric world model. This novel representation also enables the design of an object-centric exploration mechanism, which encourages the agent to interact with objects and discover useful interactions. We benchmark FOCUS in several robotic manipulation settings, where we found that our method can be used to improve manipulation skills. The object-centric world model leads to more accurate predictions of the objects in the scene and it enables more efficient learning. The object-centric exploration strategy fosters interactions with the objects in the environment, such as reaching, moving, and rotating them, and it allows fast adaptation of the agent to sparse reward reinforcement learning tasks. Using a Franka Emika robot arm, we also showcase how FOCUS proves useful in real-world applications. Website: focus-manipulation.github.io.

The dataset supports the following publication: M. Pontin and D. Damian, "Multimodal Soft Valve Enables Physical Responsiveness for Pre-emptive Resilience of Soft Robots". The .zip archive contains data (.csv files) and cad models (.stl files) to replicate the study. Please read the README files for detailed information regarding folder structure and file contents.

This dataset contains all the data and CAD models needed to replicate the study presented in "Multimodal Soft Valve Enables Physical Responsiveness for Pre-emptive Resilience of Soft Robots".

The global market for embodied intelligent robot dexterous hands is experiencing robust growth, driven by advancements in artificial intelligence, robotics, and sensor technologies. These advancements are enabling the development of more sophisticated and versatile robotic hands capable of performing complex tasks with dexterity and precision previously unattainable. The increasing adoption of robots across various sectors, including manufacturing, healthcare, and logistics, is fueling market expansion. Specifically, the demand for dexterous hands in commercial and household service robots is surging, as businesses and consumers seek automated solutions for increased efficiency and improved quality of life. The market is segmented by drive type (rotational and linear) and application (commercial, household, scientific research, and other). While precise market sizing data is unavailable, considering the high CAGR typical in rapidly evolving robotics markets (let's conservatively assume a CAGR of 15% based on industry trends), and assuming a 2025 market size of $500 million, the market is projected to reach approximately $1.5 Billion by 2033. This growth is further fueled by ongoing research and development in areas like tactile sensing and advanced control algorithms, which will enhance the capabilities and applications of dexterous robot hands. Key restraints to market growth include the high cost of development and manufacturing, the complexity of integrating these advanced robotic systems into existing infrastructure, and potential safety concerns. However, ongoing innovation and economies of scale are expected to mitigate these challenges. The competitive landscape comprises established players like Shadow Robot and Schunk alongside emerging companies such as qbrobotics and Agile Robots, fostering innovation and driving down costs. The geographic distribution of the market is expected to be widespread, with North America and Europe holding significant shares initially, followed by rapid growth in the Asia-Pacific region due to increased automation in manufacturing and emerging economies. The robust growth trajectory indicates a promising future for this sector.

The embodied intelligent robot dexterous hand market is projected to experience significant growth in the coming years, with a CAGR of XX% during the forecast period of 2025-2033. The market size is estimated to reach million USD by 2033, valuing million USD in 2025. The growth is driven by increasing adoption of robotics in various industries such as manufacturing, healthcare, and retail. Key drivers of the market include technological advancements, rising demand for automation, and government initiatives supporting robotics development. The market is segmented by application and type. The commercial robots segment holds the largest market share due to the wide adoption of robots in industries such as manufacturing and logistics. The rotational drive dexterous hand segment is expected to maintain its dominance due to its versatility and precision. Key players in the market include Shadow Robot, Schunk, qbrobotics, and Ottobock. The market is expected to witness increased competition and innovation, with companies focusing on developing advanced and cost-effective solutions.

Division of labor is ubiquitous in biological systems, as evidenced by various forms of complex task specialization observed in both animal societies and multicellular organisms. Although clearly adaptive, the way in which division of labor first evolved remains enigmatic, as it requires the simultaneous co-occurrence of several complex traits to achieve the required degree of coordination. Recently, evolutionary swarm robotics has emerged as an excellent test bed to study the evolution of coordinated group-level behavior. Here we use this framework for the first time to study the evolutionary origin of behavioral task specialization among groups of identical robots. The scenario we study involves an advanced form of division of labor, common in insect societies and known as “task partitioning†, whereby two sets of tasks have to be carried out in sequence by different individuals. Our results show that task partitioning is favored whenever the environment has features that, when exploit...

The global market for embodied intelligent general robots is experiencing rapid expansion, projected to reach $2.166 billion in 2025 and exhibiting a robust Compound Annual Growth Rate (CAGR) of 22.3% from 2025 to 2033. This significant growth is fueled by several key factors. The increasing adoption of automation across diverse sectors—including medical, industrial manufacturing, agriculture, and education—is a primary driver. The versatility of these robots, encompassing both autonomous and semi-autonomous functionalities, caters to a wide range of applications. Furthermore, advancements in artificial intelligence (AI), machine learning (ML), and sensor technologies are continuously enhancing the capabilities and efficiency of these robots, making them more cost-effective and appealing to businesses seeking to optimize operations and improve productivity. The emergence of sophisticated robots capable of complex tasks, combined with decreasing production costs, is further accelerating market penetration. While challenges such as high initial investment costs and regulatory hurdles exist, the overall market outlook remains overwhelmingly positive. The leading companies in this space—including Boston Dynamics, SoftBank Robotics, and ABB—are actively investing in research and development, driving innovation and expanding the functionalities of embodied intelligent general robots. Regional variations in market growth are expected, with North America and Asia-Pacific projected to dominate due to robust technological infrastructure, higher adoption rates in various industries, and substantial government support for automation initiatives. The competitive landscape is dynamic, with both established players and emerging startups vying for market share. This competition fosters innovation and drives down prices, benefiting consumers and facilitating wider market adoption. The long-term forecast indicates a sustained period of strong growth, driven by the ongoing technological advancements and expanding applications of embodied intelligent general robots across various industry verticals.

Huawei and UBTech Robotics announce a strategic partnership to develop humanoid robots for factories and homes, leveraging AI and innovation to transform laboratory breakthroughs into practical solutions.

Social robots are an active research area, particularly for their use in education. Previous meta-analyses have shown that social robots have a positive impact on learning, with a Cohen’s d compared to control conditions of around 0.6. However, these analyses were often limited in scope or had problematic inclusion criteria, such as different control conditions (e.g., different learning methods or pre-post comparisons). In this meta-analysis, we examined learning outcomes with more studies and a focus on the type of control conditions. Results from 79 studies shown that social robots produced larger learning gains when compared to no intervention. Robots were also more effective than human teachers, though there was large variability in the effect sizes. This variability was partly explained by co-teaching, where robots paired with humans were more effective than robots alone. We also show, based on 370 effects, that pre-post effects are mostly greater than 0, which can be explained because learning inevitably occurs with prolonged practice over time. A sentiment analysis using a large language model revealed that papers from outside Europe used more positive language when describing the robots. The conclusion drawn from the current meta-analysis is that the effect size does not stand on its own but is influenced by the way the robot is used and the control condition chosen. For future research, we recommend fair comparisons where only one parameter (such as embodiment) is varied at a time.

The global tutoring robot market is poised for significant growth, driven by increasing demand for personalized education, technological advancements in AI and robotics, and a growing awareness of the benefits of interactive learning. The market, estimated at $500 million in 2025, is projected to experience a Compound Annual Growth Rate (CAGR) of 20% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the integration of artificial intelligence enables robots to adapt to individual learning styles and provide customized feedback, surpassing traditional tutoring methods in effectiveness and efficiency. Secondly, the rising adoption of educational technology, particularly in K-12 and higher education sectors, creates a receptive market for innovative learning solutions like tutoring robots. Furthermore, advancements in natural language processing (NLP) and computer vision allow for more engaging and interactive learning experiences, leading to higher student engagement and improved learning outcomes. Companies such as UBTECH Robotics, SoftBank Robotics, and Embodied are at the forefront of this innovation, continuously developing and improving their tutoring robot offerings. However, several challenges may hinder the market's growth. High initial investment costs for both consumers and educational institutions could restrict widespread adoption. Concerns about data privacy and security related to student information collected by these robots also need to be addressed. Additionally, the need for robust infrastructure (reliable internet access, technical support) and teacher training to effectively integrate these robots into the educational system presents a barrier. Despite these restraints, the market's trajectory remains positive, fueled by ongoing technological improvements, decreasing production costs, and the increasing acceptance of educational robots as valuable learning tools. The market is expected to witness a shift towards more sophisticated robots with advanced AI capabilities and personalized learning features in the coming years.