Open X-Embodiment Dataset (unofficial)

This is an unofficial Dataset Repo. This Repo is set up to make Open X-Embodiment Dataset (55 in 1) more accessible for people who love huggingface🤗. Open X-Embodiment Dataset is the largest open-source real robot dataset to date. It contains 1M+ real robot trajectories spanning 22 robot embodiments, from single robot arms to bi-manual robots and quadrupeds. More information is located on RT-X website… See the full description on the dataset page: https://huggingface.co/datasets/jxu124/OpenX-Embodiment.

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/nhatchung/_gr1_unified.

The embodied intelligent general robot market is experiencing significant growth, projected to reach $2.166 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 22.3% from 2025 to 2033. This robust expansion is driven by several key factors. Firstly, advancements in artificial intelligence (AI), particularly in areas like computer vision, natural language processing, and machine learning, are enabling robots to perform increasingly complex tasks with greater autonomy and adaptability. Secondly, the rising demand for automation across various industries, including manufacturing, logistics, healthcare, and even domestic settings, is fueling the adoption of these versatile robots. Finally, decreasing production costs and improved energy efficiency are making embodied intelligent general robots more accessible and cost-effective for businesses and consumers alike. The market is witnessing a shift towards collaborative robots (cobots) designed to work alongside humans, enhancing productivity and safety. The competitive landscape is marked by a diverse range of established players and innovative startups. Companies like Boston Dynamics, SoftBank Robotics, and others are leading the charge with advanced robotic solutions. However, the market is also characterized by ongoing technological innovations and the emergence of new entrants. The forecast period (2025-2033) anticipates continued growth, driven by the expanding applications of embodied intelligent general robots in emerging sectors such as elder care and personalized assistance. Challenges remain, including concerns about safety regulations, ethical considerations surrounding AI in robotics, and the need for robust cybersecurity measures. Nevertheless, the overall trajectory points towards a future where embodied intelligent general robots play an increasingly prominent role in our daily lives and industrial processes.

Discover the booming Embodied AI Robot market! This analysis reveals key trends, growth drivers, and challenges, featuring leading companies like Boston Dynamics and Sony. Learn about market size projections, regional breakdowns, and future opportunities in this rapidly evolving sector.

IntroductionNowadays, several robots have been developed to provide not only companionship to older adults, but also to cooperate with them during health and lifestyle activities. Despite the undeniable wealth of socially assistive robots (SARs), there is an increasing need to customize the tools used for measuring their acceptance in real-life applications.MethodsWithin the Robot-Era project, a scale was developed to understand the degree of acceptance of the robotic platform. A preliminary test with 21 participants was performed to assess the statistical validity of the Robot-Era Inventory (REI) scales.ResultsBased on the criteria observed in the literature, 41 items were developed and grouped in different scales (perceived robot personality, human–robot interaction, perceived benefit, ease of use, and perceived usefulness). The reliability of the Robot-Era Inventory scale was analyzed with Cronbach's alpha, with a mean value of 0.79 (range = 0.61–0.91). Furthermore, the preliminary validity of this scale has been tested by using the correlation analysis with a gold standard, the Unified Theory of Acceptance and Use of Technology (UTAUT) model.DiscussionThe Robot-Era Inventory represents a useful tool that can be easily personalized and included in the assessment of any SARs that cooperate with older people in real environment applications.

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 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.

Yearly citation counts for the publication titled "Designing Intelligent Robots-On the Implications of Embodiment-".

Human demonstration videos are a widely available data source for robot learning and an intuitive user interface for expressing desired behavior.

Discover the booming market for embodied intelligent robot dexterous hands. Explore a projected $2B+ market by 2033, driven by healthcare, manufacturing, and logistics. Learn about key players, growth drivers, and market restraints in this comprehensive analysis.

The embodied intelligent general robot market is booming, projected to reach $2166 million by 2025 and grow at a CAGR of 22.3% through 2033. Driven by automation needs across industries and AI advancements, this market analysis reveals key trends, regional breakdowns, and leading companies like Boston Dynamics and SoftBank Robotics. Discover the future of robotics.

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.

With the continuous advancement of Artificial intelligence (AI), robots as embodied intelligent systems are increasingly becoming more present in daily life like households or in elderly care. As a result, lay users are required to interact with these systems more frequently and teach them to meet individual needs. Human-in-the-loop reinforcement learning (HIL-RL) offers an effective way to realize this teaching. Studies show that various feedback modalities, such as preference, guidance, or demonstration can significantly enhance learning success, though their suitability varies among users expertise in robotics. Research also indicates that users apply different scaffolding strategies when teaching a robot, such as motivating it to explore actions that promise success. Thus, providing a collection of different feedback modalities allows users to choose the method that best suits their teaching strategy, and allows the system to individually support the user based on their interaction behavior. However, most state-of-the-art approaches provide users with only one feedback modality at a time. Investigating combined feedback modalities in interactive robot learning remains an open challenge. To address this, we conducted a study that combined common feedback modalities. Our research questions focused on whether these combinations improve learning outcomes, reveal user preferences, show differences in perceived effectiveness, and identify which modalities influence learning the most. The results show that combining the feedback modalities improves learning, with users perceiving the effectiveness of the modalities vary ways, and certain modalities directly impacting learning success. The study demonstrates that combining feedback modalities can support learning even in a simplified setting and suggests the potential for broader applicability, especially in robot learning scenarios with a focus on user interaction. Thus, this paper aims to motivate the use of combined feedback modalities in interactive imitation learning.

Dataset A dataset of human manipulation actions recorded with a motion capture system. A Qualisys motion capture system was used to record the data. We tracked individual finger movements as well as the position and orientation of the right hand. Some recordings contain additional markers at the back, shoulder, and elbow. The motion capture setup is explained here. The dataset contains the original recordings of manipulation actions as well as metadata with annotations of relevant parts of the recordings (labels, start, end). Recordings are exported from the Qualisys Track Manager (QTM) as tab-separated value (TSV) files. Metadata is provided in JSON format. Related software is available at github.com/dfki-ric/hand_embodiment, which also contains code to load and use the dataset. Publication This dataset was introduced in the following paper: Alexander Fabisch, Manuela Uliano, Dennis Marschner, Melvin Laux, Johannes Brust, Marco Controzzi: "A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations", Proceedings of IEEE-RAS International Conference on Humanoid Robots 2022. It is available from arxiv.org as a preprint or from IEEE. If you use the dataset, please cite the paper as: @INPROCEEDINGS{Fabisch2022, author={Fabisch, Alexander and Uliano, Manuela and Marschner, Dennis and Laux, Melvin and Brust, Johannes and Controzzi, Marco}, booktitle={2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)}, title={A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations}, year={2022}, pages={801--808}, doi={10.1109/Humanoids53995.2022.10000165}} Ethics Approval Experimental protocols were approved by the ethics committee of the University of Bremen. Written informed consent was obtained from all participants for participation in the study and to publish this dataset. Origin and Funding This dataset is provided by the Robotics Innovation Center, DFKI GmbH. This work was supported by the European Commission under the Horizon 2020 framework program for Research and Innovation (project acronym: APRIL, project number: 870142).

A CommonSense Reasoning Dataset pertaining to Physical Commonsense affordance of objects. https://github.com/Ayush8120/COAT

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.