Mobile Robot Data Annotation Tools Market Outlook

According to our latest research, the global mobile robot data annotation tools market size reached USD 1.46 billion in 2024, demonstrating robust expansion with a compound annual growth rate (CAGR) of 22.8% from 2025 to 2033. The market is forecasted to attain USD 11.36 billion by 2033, driven by the surging adoption of artificial intelligence (AI) and machine learning (ML) in robotics, the escalating demand for autonomous mobile robots across industries, and the increasing sophistication of annotation tools tailored for complex, multimodal datasets.

The primary growth driver for the mobile robot data annotation tools market is the exponential rise in the deployment of autonomous mobile robots (AMRs) across various sectors, including manufacturing, logistics, healthcare, and agriculture. As organizations strive to automate repetitive and hazardous tasks, the need for precise and high-quality annotated datasets has become paramount. Mobile robots rely on annotated data for training algorithms that enable them to perceive their environment, make real-time decisions, and interact safely with humans and objects. The proliferation of sensors, cameras, and advanced robotics hardware has further increased the volume and complexity of raw data, necessitating sophisticated annotation tools capable of handling image, video, sensor, and text data streams efficiently. This trend is driving vendors to innovate and integrate AI-powered features such as auto-labeling, quality assurance, and workflow automation, thereby boosting the overall market growth.

Another significant growth factor is the integration of cloud-based data annotation platforms, which offer scalability, collaboration, and accessibility advantages over traditional on-premises solutions. Cloud deployment enables distributed teams to annotate large datasets in real time, leverage shared resources, and accelerate project timelines. This is particularly crucial for global enterprises and research institutions working on cutting-edge robotics applications that require rapid iteration and continuous learning. Moreover, the rise of edge computing and the Internet of Things (IoT) has created new opportunities for real-time data annotation and validation at the source, further enhancing the value proposition of advanced annotation tools. As organizations increasingly recognize the strategic importance of high-quality annotated data for achieving competitive differentiation, investment in robust annotation platforms is expected to surge.

The mobile robot data annotation tools market is also benefiting from the growing emphasis on safety, compliance, and ethical AI. Regulatory bodies and industry standards are mandating rigorous validation and documentation of AI models used in safety-critical applications such as autonomous vehicles, medical robots, and defense systems. This has led to a heightened demand for annotation tools that offer audit trails, version control, and compliance features, ensuring transparency and traceability throughout the model development lifecycle. Furthermore, the emergence of synthetic data generation, active learning, and human-in-the-loop annotation workflows is enabling organizations to overcome data scarcity challenges and improve annotation efficiency. These advancements are expected to propel the market forward, as stakeholders seek to balance speed, accuracy, and regulatory requirements in their AI-driven robotics initiatives.

From a regional perspective, Asia Pacific is emerging as a dominant force in the mobile robot data annotation tools market, fueled by rapid industrialization, significant investments in robotics research, and the presence of leading technology hubs in countries such as China, Japan, and South Korea. North America continues to maintain a strong foothold, driven by early adoption of AI and robotics technologies, a robust ecosystem of annotation tool providers, and supportive government initiatives. Europe is also witnessing steady growth, particularly in the manufacturing and automotive sectors, while Latin America and the Middle East & Africa are gradually catching up as awareness and adoption rates increase. The interplay of regional dynamics, regulatory environments, and industry verticals will continue to shape the competitive landscape and growth trajectory of the global market over the forecast period.

We examined the influence of a parent on robot-assisted activities for a child with Autism Spectrum Disorder. We observed the interactions between a robot and the child wearing a wearable device during free play sessions. The child participated in four sessions with the parent and interacted willingly with the robot, therapist, and parent. This study adopted video recording for behavioral observations and specifically observed the situations.

Annotation Tools for Robotics Perception Market Outlook

According to our latest research, the global Annotation Tools for Robotics Perception market size reached USD 1.36 billion in 2024 and is projected to grow at a robust CAGR of 17.4% from 2025 to 2033, achieving a forecasted market size of USD 5.09 billion by 2033. This significant growth is primarily fueled by the rapid expansion of robotics across sectors such as automotive, industrial automation, and healthcare, where precise data annotation is critical for machine learning and perception systems.

The surge in adoption of artificial intelligence and machine learning within robotics is a major growth driver for the Annotation Tools for Robotics Perception market. As robots become more advanced and are required to perform complex tasks in dynamic environments, the need for high-quality annotated datasets increases exponentially. Annotation tools enable the labeling of images, videos, and sensor data, which are essential for training perception algorithms that empower robots to detect objects, understand scenes, and make autonomous decisions. The proliferation of autonomous vehicles, drones, and collaborative robots in manufacturing and logistics has further intensified the demand for robust and scalable annotation solutions, making this segment a cornerstone in the advancement of intelligent robotics.

Another key factor propelling market growth is the evolution and diversification of annotation types, such as 3D point cloud and sensor fusion annotation. These advanced annotation techniques are crucial for next-generation robotics applications, particularly in scenarios requiring spatial awareness and multi-sensor integration. The shift towards multi-modal perception, where robots rely on a combination of visual, LiDAR, radar, and other sensor data, necessitates sophisticated annotation frameworks. This trend is particularly evident in industries like automotive, where autonomous driving systems depend on meticulously labeled datasets to achieve high levels of safety and reliability. Additionally, the growing emphasis on edge computing and real-time data processing is prompting the development of annotation tools that are both efficient and compatible with on-device learning paradigms.

Furthermore, the increasing integration of annotation tools within cloud-based platforms is streamlining collaboration and scalability for enterprises. Cloud deployment offers advantages such as centralized data management, seamless updates, and the ability to leverage distributed workforces for large-scale annotation projects. This is particularly beneficial for global organizations managing extensive robotics deployments across multiple geographies. The rise of annotation-as-a-service models and the incorporation of AI-driven automation in labeling processes are also reducing manual effort and improving annotation accuracy. As a result, businesses are able to accelerate the training cycles of their robotics perception systems, driving faster innovation and deployment of intelligent robots across diverse applications.

From a regional perspective, North America continues to lead the Annotation Tools for Robotics Perception market, driven by substantial investments in autonomous technologies and a strong ecosystem of AI startups and research institutions. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid industrialization, government initiatives supporting robotics, and increasing adoption of automation in manufacturing and agriculture. Europe also remains a significant market, particularly in automotive and industrial robotics, thanks to stringent safety standards and a strong focus on technological innovation. Collectively, these regional dynamics are shaping the competitive landscape and driving the global expansion of annotation tools tailored for robotics perception.

Component Analysis

The Annotation Tools for Robotics Perception market, when segmented by component, is primarily divided into software and services. Software solutions dominate the market, accounting for the largest revenue share in 2024. This dominance is attributed to the proliferation of robust annotation platforms that offer advanced features such as automated labeling, AI-assisted annotation, and integration with machine learning pipelines. These software tools are designed to handle diverse data types, including images, videos, and 3D point clouds, enabling organizations to efficiently annotate large datasets required for training r

Annotation Tools for Robotics Perception Market Outlook

As per our latest research, the global Annotation Tools for Robotics Perception market size reached USD 1.47 billion in 2024, with a robust growth trajectory driven by the rapid adoption of robotics in various sectors. The market is expected to expand at a CAGR of 18.2% during the forecast period, reaching USD 6.13 billion by 2033. This significant growth is attributed primarily to the increasing demand for sophisticated perception systems in robotics, which rely heavily on high-quality annotated data to enable advanced machine learning and artificial intelligence functionalities.

A key growth factor for the Annotation Tools for Robotics Perception market is the surging deployment of autonomous systems across industries such as automotive, manufacturing, and healthcare. The proliferation of autonomous vehicles and industrial robots has created an unprecedented need for comprehensive datasets that accurately represent real-world environments. These datasets require meticulous annotation, including labeling of images, videos, and sensor data, to train perception algorithms for tasks such as object detection, tracking, and scene understanding. The complexity and diversity of environments in which these robots operate necessitate advanced annotation tools capable of handling multi-modal data, thus fueling the demand for innovative solutions in this market.

Another significant driver is the continuous evolution of machine learning and deep learning algorithms, which require vast quantities of annotated data to achieve high accuracy and reliability. As robotics applications become increasingly sophisticated, the need for precise and context-rich annotations grows. This has led to the emergence of specialized annotation tools that support a variety of data types, including 3D point clouds and multi-sensor fusion data. Moreover, the integration of artificial intelligence within annotation tools themselves is enhancing the efficiency and scalability of the annotation process, enabling organizations to manage large-scale projects with reduced manual intervention and improved quality control.

The growing emphasis on safety, compliance, and operational efficiency in sectors such as healthcare and aerospace & defense further accelerates the adoption of annotation tools for robotics perception. Regulatory requirements and industry standards mandate rigorous validation of robotic perception systems, which can only be achieved through extensive and accurate data annotation. Additionally, the rise of collaborative robotics (cobots) in manufacturing and agriculture is driving the need for annotation tools that can handle diverse and dynamic environments. These factors, combined with the increasing accessibility of cloud-based annotation platforms, are expanding the reach of these tools to organizations of all sizes and across geographies.

In this context, Automated Ultrastructure Annotation Software is gaining traction as a pivotal tool in enhancing the efficiency and precision of data labeling processes. This software leverages advanced algorithms and machine learning techniques to automate the annotation of complex ultrastructural data, which is particularly beneficial in fields requiring high-resolution imaging and detailed analysis, such as biomedical research and materials science. By automating the annotation process, this software not only reduces the time and labor involved but also minimizes human error, leading to more consistent and reliable datasets. As the demand for high-quality annotated data continues to rise across various industries, the integration of such automated solutions is becoming increasingly essential for organizations aiming to maintain competitive advantage and operational efficiency.

From a regional perspective, North America currently holds the largest share of the Annotation Tools for Robotics Perception market, accounting for approximately 38% of global revenue in 2024. This dominance is attributed to the regionÂ’s strong presence of robotics technology developers, advanced research institutions, and early adoption across automotive and manufacturing sectors. Asia Pacific follows closely, fueled by rapid industrialization, government initiatives supporting automation, and the presence of major automotiv

REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly

📋 Introduction

Robotic manipulation remains a core challenge in robotics, particularly for contact-rich tasks such as industrial assembly and disassembly. Existing datasets have significantly advanced learning in manipulation but are primarily focused on simpler tasks like object rearrangement, falling short of capturing the complexity and physical dynamics involved in assembly and disassembly. To bridge this gap, we present REASSEMBLE (Robotic assEmbly disASSEMBLy datasEt), a new dataset designed specifically for contact-rich manipulation tasks. Built around the NIST Assembly Task Board 1 benchmark, REASSEMBLE includes four actions (pick, insert, remove, and place) involving 17 objects. The dataset contains 4,551 demonstrations, of which 4,035 were successful, spanning a total of 781 minutes. Our dataset features multi-modal sensor data including event cameras, force-torque sensors, microphones, and multi-view RGB cameras. This diverse dataset supports research in areas such as learning contact-rich manipulation, task condition identification, action segmentation, and more. We believe REASSEMBLE will be a valuable resource for advancing robotic manipulation in complex, real-world scenarios.

{"Hama1": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ], 
 "Hama2": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ], 
 "DAVIS346": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ], 
 "NIST_Board1": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ]
}

[x, y, z] represent the position of the object, and [qx, qy, qz, qw] represent its orientation as a quaternion.

The HDF5 (.h5) format organizes data into two main types of structures: datasets, which hold the actual data, and groups, which act like folders that can contain datasets or other groups. In the diagram below, groups are shown as folder icons, and datasets as file icons. The main group of the file directly contains the video, audio, and event data. To save memory, video and audio are stored as encoded byte strings, while event data is stored as arrays. The robot’s proprioceptive information is kept in the robot_state group as arrays. Because different sensors record data at different rates, the arrays vary in length (signified by the N_xxx variable in the data shapes). To align the sensory data, each sensor’s timestamps are stored separately in the timestamps group. Information about action segments is stored in the segments_info group. Each segment is saved as a subgroup, named according to its order in the demonstration, and includes a start timestamp, end timestamp, a success indicator, and a natural language description of the action. Within each segment, low-level skills are organized under a low_level subgroup, following the same structure as the high-level annotations.

📁

The splits folder contains two text files which list the h5 files used for the traning and validation splits.

📌 Important Resources

The project website contains more details about the REASSEMBLE dataset. The Code for loading and visualizing the data is avaibile on our github repository.

📄 Project website: https://tuwien-asl.github.io/REASSEMBLE_page/
💻 Code: https://github.com/TUWIEN-ASL/REASSEMBLE

The AI Data Annotation Solution market is projected for significant expansion, driven by the escalating demand for high-quality, labeled data across various artificial intelligence applications. With an estimated market size of approximately $6.5 billion in 2025, the sector is anticipated to experience a robust Compound Annual Growth Rate (CAGR) of around 18% through 2033. This substantial growth is underpinned by critical drivers such as the rapid advancement and adoption of machine learning and deep learning technologies, the burgeoning need for autonomous systems in sectors like automotive and robotics, and the increasing application of AI for enhanced customer experiences in retail and financial services. The proliferation of data generated from diverse sources, including text, images, video, and audio, further fuels the necessity for accurate and efficient annotation solutions to train and refine AI models. Government initiatives focused on smart city development and healthcare advancements also contribute considerably to this growth trajectory, highlighting the pervasive influence of AI-driven solutions. The market is segmented across various applications, with IT, Automotive, and Healthcare expected to be leading contributors due to their intensive AI development pipelines. The growing reliance on AI for predictive analytics, fraud detection, and personalized services within the Financial Services sector, along with the push for automation and improved customer engagement in Retail, also signifies substantial opportunities. Emerging trends such as the rise of active learning and semi-supervised learning techniques to reduce annotation costs, alongside the increasing adoption of AI-powered annotation tools and platforms that offer enhanced efficiency and scalability, are shaping the competitive landscape. However, challenges like the high cost of annotation, the need for skilled annotators, and concerns regarding data privacy and security can act as restraints. Major players like Google, Amazon Mechanical Turk, Scale AI, Appen, and Labelbox are actively innovating to address these challenges and capture market share, indicating a dynamic and competitive environment focused on delivering precise and scalable data annotation services. This comprehensive report delves deep into the dynamic and rapidly evolving AI Data Annotation Solution market. With a Study Period spanning from 2019 to 2033, a Base Year and Estimated Year of 2025, and a Forecast Period from 2025 to 2033, this analysis provides unparalleled insights into market dynamics, trends, and future projections. The report leverages Historical Period data from 2019-2024 to establish a robust foundation for its forecasts.

Computer Vision Annotation Tool Market Outlook

According to our latest research, the global Computer Vision Annotation Tool market size reached USD 2.16 billion in 2024, and it is expected to grow at a robust CAGR of 16.8% from 2025 to 2033. By 2033, the market is forecasted to achieve a value of USD 9.28 billion, driven by the rising adoption of artificial intelligence and machine learning applications across diverse industries. The proliferation of computer vision technologies in sectors such as automotive, healthcare, retail, and robotics is a key growth factor, as organizations increasingly require high-quality annotated datasets to train and deploy advanced AI models.

The growth of the Computer Vision Annotation Tool market is primarily propelled by the surging demand for data annotation solutions that facilitate the development of accurate and reliable machine learning algorithms. As enterprises accelerate their digital transformation journeys, the need for precise labeling of images, videos, and other multimedia content has intensified. This is especially true for industries like autonomous vehicles, where annotated datasets are crucial for object detection, path planning, and safety assurance. Furthermore, the increasing complexity of visual data and the necessity for scalable annotation workflows are compelling organizations to invest in sophisticated annotation tools that offer automation, collaboration, and integration capabilities, thereby fueling market expansion.

Another significant growth driver is the rapid evolution of AI-powered applications in healthcare, retail, and security. In the healthcare sector, computer vision annotation tools are pivotal in training models for medical imaging diagnostics, disease detection, and patient monitoring. Similarly, in retail, these tools enable the development of intelligent systems for inventory management, customer behavior analysis, and automated checkout solutions. The security and surveillance segment is also witnessing heightened adoption, as annotated video data becomes essential for facial recognition, threat detection, and crowd monitoring. The convergence of these trends is accelerating the demand for advanced annotation platforms that can handle diverse data modalities and deliver high annotation accuracy at scale.

The increasing availability of cloud-based annotation solutions is further catalyzing market growth by offering flexibility, scalability, and cost-effectiveness. Cloud deployment models allow organizations to access powerful annotation tools remotely, collaborate with distributed teams, and leverage on-demand computing resources. This is particularly advantageous for large-scale projects that require the annotation of millions of images or videos. Moreover, the integration of automation features such as AI-assisted labeling, quality control, and workflow management is enhancing productivity and reducing time-to-market for AI solutions. As a result, both large enterprises and small-to-medium businesses are embracing cloud-based annotation platforms to streamline their AI development pipelines.

From a regional perspective, North America leads the Computer Vision Annotation Tool market, accounting for the largest revenue share in 2024. The region’s dominance is attributed to the presence of major technology companies, robust AI research ecosystems, and early adoption of computer vision solutions in sectors like automotive, healthcare, and security. Europe follows closely, driven by regulatory support for AI innovation and growing investments in smart manufacturing and healthcare technologies. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by expanding digital infrastructure, government initiatives to promote AI adoption, and the rise of technology startups. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a comparatively moderate pace, as organizations in these regions increasingly recognize the value of annotated data for digital transformation initiatives.

Component Analysis

The Computer Vision Annotation Tool market is segmented by component into software and services, each playing a distinct yet complementary role in the value chain. The software segment encompasses standalone annotation platforms, integrated development environments, and specialized tools designed for labeling images, videos, text, and audio. These solutions are characterized by fe

Robotics Data Labeling Services Market Outlook

As per our latest research, the global Robotics Data Labeling Services market size stood at USD 1.42 billion in 2024. The market is witnessing robust momentum, projected to expand at a CAGR of 20.7% from 2025 to 2033, reaching an estimated USD 9.15 billion by 2033. This surge is primarily driven by the increasing adoption of AI-powered robotics across various industries, where high-quality labeled data is essential for training and deploying advanced machine learning models. The rapid proliferation of automation, coupled with the growing complexity of robotics applications, is fueling demand for precise and scalable data labeling solutions on a global scale.

The primary growth factor for the Robotics Data Labeling Services market is the accelerating integration of artificial intelligence and machine learning algorithms into robotics systems. As robotics technology becomes more sophisticated, the need for accurately labeled data to train these systems is paramount. Companies are increasingly investing in data annotation and labeling services to enhance the performance and reliability of their autonomous robots, whether in manufacturing, healthcare, automotive, or logistics. The complexity of robotics applications, including object detection, environment mapping, and real-time decision-making, mandates high-quality labeled datasets, driving the marketÂ’s expansion.

Another significant factor propelling market growth is the diversification of robotics applications across industries. The rise of autonomous vehicles, industrial robots, service robots, and drones has created an insatiable demand for labeled image, video, and sensor data. As these applications become more mainstream, the volume and variety of data requiring annotation have multiplied. This trend is further amplified by the shift towards Industry 4.0 and the digital transformation of traditional sectors, where robotics plays a central role in operational efficiency and productivity. Data labeling services are thus becoming an integral part of the robotics development lifecycle, supporting innovation and deployment at scale.

Technological advancements in data labeling methodologies, such as the adoption of AI-assisted labeling tools and cloud-based annotation platforms, are also contributing to market growth. These innovations enable faster, more accurate, and cost-effective labeling processes, making it feasible for organizations to handle large-scale data annotation projects. The emergence of specialized labeling services tailored to specific robotics applications, such as sensor fusion for autonomous vehicles or 3D point cloud annotation for industrial robots, is further enhancing the value proposition for end-users. As a result, the market is witnessing increased participation from both established players and new entrants, fostering healthy competition and continuous improvement in service quality.

In the evolving landscape of robotics, Robotics Synthetic Data Services are emerging as a pivotal component in enhancing the capabilities of AI-driven systems. These services provide artificially generated data that mimics real-world scenarios, enabling robotics systems to train and validate their algorithms without the constraints of physical data collection. By leveraging synthetic data, companies can accelerate the development of robotics applications, reduce costs, and improve the robustness of their models. This approach is particularly beneficial in scenarios where real-world data is scarce, expensive, or difficult to obtain, such as in autonomous driving or complex industrial environments. As the demand for more sophisticated and adaptable robotics solutions grows, the role of Robotics Synthetic Data Services is set to expand, offering new opportunities for innovation and efficiency in the market.

From a regional perspective, North America currently dominates the Robotics Data Labeling Services market, accounting for the largest revenue share in 2024. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid industrialization, expanding robotics manufacturing capabilities, and significant investments in AI research and development. Europe also holds a substantial market share, supported by strong regulatory frameworks and a focus on technological innovation. Meanwhile, Latin

Introduction The UT Campus Object Dataset (CODa) is a mobile robot egocentric perception dataset collected at the University of Texas at Austin campus designed for research and planning for autonomous navigation in urban environments. CODa provides benchmarks for 3D object detection and 3D semantic segmentation. At the moment of publication, CODa contains the largest diversity of ground truth object class annotations in any available 3D LiDAR dataset collected in human-centric urban environments, and over 196 million points annotated with semantic labels to indicate the terrain type of each point in the 3D point cloud. Three of the five modalities available in CODa. RGB image with 3D to 2D projected annotations (bottom left), 3D point cloud with ground truth object annotations (middle), stereo depth image (bottom right). Dataset Contents The dataset contains: 8.5 hours of multimodal sensor data. Synchronized 3D point clouds and stereo RGB video from a 128-channel 3D LiDAR and two 1.25MP RGB cameras at 10 fps. RGB-D videos from an additional 0.5MP sensor at 7 fps A 9-DOF IMU sensor at 40 Hz. 54 minutes of ground-truth annotations containing 1.3 million 3D bounding boxes with instance IDs for 50 semantic classes. 5000 frames of 3D semantic annotations for urban terrain, and pseudo-ground truth localization. Dataset Characteristics Robot operators repeatedly traversed 4 unique pre-defined paths - which we call trajectories - in both the forward and opposite directions to provide viewpoint diversity. Every unique trajectory was traversed at least once during cloudy, sunny, dark lighting and rainy conditions amounting to 23 "sequences". Of these sequences, 7 were collected during cloudy conditions, 4 during evening/dark conditions, 9 during sunny days, and 3 immediately before/after rainfall. We annotated 3D point clouds in 22 of the 23 sequences. Spatial map of geographic locations contained in CODa. Data Collection The data collection team consisted of 7 robot operators. The sequences were traversed in teams of two; one person tele-operated the robot along the predefined trajectory and stopped the robot at designated waypoints - denoted on the map above - on the route. Each time a waypoint was reached, the robot was stopped and the operator noted both time and waypoint reached. The second person managed the crowds' questions and concerns. Before each sequence, the robot operator manually commanded the robot to publish all sensor topics over the Robot Operating System (ROS) middleware and recorded these sensor messages to a rosbag. At the end of each sequence, the operator stopped the data recording manually and post-processed the recorded sensor data into individual files. We used the official CODa development kit to extract the raw images, point clouds, inertial, and GPS information to individual files. The development kit and documentation are publicly available on Github (https://github.com/ut-amrl/coda-devkit). Robot Top-down diagram view of robot used for CODa. For all sequences, the data collection team tele-operated a Clearpath Husky, which is approximately 990mm x 670mm x 820mm (length, width, height) with the sensor suite included. The robot was operated between 0 to 1 meter per second and used 2D, 3D, stereo, inertial, and GPS sensors. More information about the sensors is included in the Data Report. Human Subjects This study was approved by the University of Texas at Austin Institutional Review Board (IRB) under the IRB ID: STUDY00003493. Anyone present in the recorded sensor data and their observed behavior was purely incidental. To protect the privacy of individuals recorded by the robots and present in the dataset, we did not collect any personal information on individuals. Furthermore, the operator managing the crowd was acting as a point of contact for anyone who wished not to be present in the dataset. Anyone who did not wish to participate and expressed so was noted and removed from the sensor data and from the annotations. Included in this data package are the IRB exempt determination and the Research Information Sheet distributed to the incidental participants. Data Annotation Deepen AI annotated the dataset. We instructed their labeling team on how to annotate the 3D bounding boxes and 3D terrain segmentation labels. The annotation document is part of the data report, which is included in this dataset. Data Quality Control The Deepen team conducted a two-stage internal review process during the labeling process. In the first stage, human annotators reviewed every frame and flagged issues for fixing. In the second stage, a separate team reviewed 20% of the annotated frames for missed issues. Their quality assurance (QA) team repeated this process until at least 95% of 3D bounding boxes and 90% of semantic segmentation labels met the labeling standards. The CODa data collection team also manually reviewed each completed frame. While it is possible to convert these...

The files accompany the paper "Enhancing Video-Based Robot Failure Detection Using Task Knowledge" published at ECMR 2025. They include updated labels for the ARMBench Video Detect dataset, and additional annotations for the dataset, including object bounding boxes, temporal segmentation of robot actions, etc.

The FAILURE.zip file contains temporal segmentation annotations for the FAILURE dataset.

Building successful collaboration between humans and robots requires efficient, effective, and natural communication. This dataset supports the study of RGB-based deep learning models for controlling robots through gestures (e.g., “follow me”). To address the challenge of collecting high-quality annotated data from human subjects, synthetic data was considered for this domain. This dataset of gestures includes real videos with human subjects and synthetic videos from our custom simulator. This dataset can be used as a benchmark for studying how ML models for activity perception can be improved with synthetic data.

Reference: de Melo C, Rothrock B, Gurram P, Ulutan O, Manjunath BS (2020) Vision-based gesture recognition in human-robot teams using synthetic data. In Proc. IROS 2020.

Methods For effective human-robot interaction, the gestures need to have clear meaning, be easy to interpret, and have intuitive shape and motion profiles. To accomplish this, we selected standard gestures from the US Army Field Manual, which describes efficient, effective, and tried-and-tested gestures that are appropriate for various types of operating environments. Specifically, we consider seven gestures: Move in reverse, instructs the robot to move back in the opposite direction; Halt, stops the robot; Attention, instructs the robot to halt its current operation and pay attention to the human; Advance, instructs the robot to move towards its target position in the context of the ongoing mission; Follow me, instructs the robot to follow the human; and, Move forward, instructs the robot to move forward.

The human dataset consists of recordings for 14 subjects (4 females, 10 males). Subjects performed each gesture twice, once for each of eight camera orientations (0º, 45º, ..., 315º). Some gestures can only be performed with one repetition (halt, advance), whereas others can have multiple repetitions (e.g., move in reverse); in the latter case, we instructed subjects to perform the gestures with as many repetitions as it felt natural to them. The videos were recorded in open environments over four different sessions. The procedure for the data collection was approved by the US Army Research Laboratory IRB, and the subjects gave informed consent to share the data. The average length of each gesture performance varied from 2 to 5 seconds and 1,574 video segments of gestures were collected. The video frames were manually annotated using custom tools we developed. The frames before and after the gesture performance were labelled 'Idle'. Notice that since the duration of the actual gesture - i.e., non-idle motion - varied per subject and gesture type, the dataset includes comparable, but not equal, number of frames for each gesture.

To synthesize the gestures, we built a virtual human simulator using a commercial game engine, namely Unity. The 3D models for the character bodies were retrieved from Mixamo, the 3D models for the face were generated on FaceGen, and the characters were assembled using 3ds Max. The character bodies were already rigged and ready for animation. We created four characters representative of the domains we were interested in: male in civilian and camouflage uniforms, and female in civilian and camouflage uniforms. Each character can be changed to reflect a Caucasian, African-American, and East Indian skin color. The simulator also supports two different body shapes: thin and thick. The seven gestures were animated using standard skeleton-animation techniques. Three animations, using the human data as reference, were created for each gesture. The simulator supports performance of the gestures with an arbitrary number of repetitions and at arbitrary speeds. The characters were also endowed with subtle random motion for the body. The background environments were retrieved from the Ultimate PBR Terrain Collection available at the Unity Asset Store. Finally, the simulator supports arbitrary camera orientations and lighting conditions.

The synthetic dataset was generated by systematically varying the aforementioned parameters. In total, 117,504 videos were synthesized. The average video duration was between 3 to 5 seconds. To generate the dataset, we ran several instances of Unity, across multiple machines, over the course of two days. The labels for these videos were automatically generated, without any need for manual annotation.

OverviewThe HA4M dataset is a collection of multi-modal data relative to actions performed by different subjects in an assembly scenario for manufacturing. It has been collected to provide a good test-bed for developing, validating and testing techniques and methodologies for the recognition of assembly actions. To the best of the authors' knowledge, few vision-based datasets exist in the context of object assembly.The HA4M dataset provides a considerable variety of multi-modal data compared to existing datasets. Six types of simultaneous data are supplied: RGB frames, Depth maps, IR frames, RGB-Depth-Aligned frames, Point Clouds and Skeleton data.These data allow the scientific community to make consistent comparisons among processing approaches or machine learning approaches by using one or more data modalities. Researchers in computer vision, pattern recognition and machine learning can use/reuse the data for different investigations in different application domains such as motion analysis, human-robot cooperation, action recognition, and so on.Dataset detailsThe dataset includes 12 assembly actions performed by 41 subjects for building an Epicyclic Gear Train (EGT).The assembly task involves three phases first, the assembly of Block 1 and Block 2 separately, and then the final setting up of both Blocks to build the EGT. The EGT is made up of a total of 12 components divided into two sets: the first eight components for building Block 1 and the remaining four components for Block 2. Finally, two screws are fixed with an Allen Key to assemble the two blocks and thus obtain the EGT.Acquisition setupThe acquisition experiment took place in two laboratories (one in Italy and one in Spain), where an acquisition area was reserved for the experimental setup. A Microsoft Azure Kinect camera acquires videos during the execution of the assembly task. It is placed in front of the operator and the table where the components are spread over. The camera is place on a tripod at an height h of 1.54 m and a distance of 1.78m. The camera is down-tilted by an angle of 17 degrees.Technical informationThe HA4M dataset contains 217 videos of the assembly task performed by 41 subjects (15 females and 26 males). Their ages ranged from 23 to 60. All the subjects participated voluntarily and were provided with a written description of the experiment. Each subject was asked to execute the task several times and to perform the actions at their own convenience (e.g. with both hands), independently from their dominant hand. The HA4M project is a growing project. So new acquisitions, planned in the next future, will expand the current dataset.ActionsTwelve actions are considered in HA4M. Actions from 1 to 4 are needed to build Block 1, then actions from 5 to 8 for building Block 2 and finally, the actions from 9 to 12 for completing the EGT. Actions are listed below:Pick up/Place CarrierPick up/Place Gear Bearings (x3)Pick up/Place Planet Gears (x3)Pick up/Place Carrier ShaftPick up/Place Sun ShaftPick up/Place Sun GearPick up/Place Sun Gear BearingPick up/Place Ring BearPick up Block 2 and place it on Block 1Pick up/Place CoverPick up/Place Screws (x2)Pick up/Place Allen Key, Turn Screws, Return Allen Key and EGTAnnotationData annotation concerns the labeling of the different actions in the video sequences.The annotation of the actions has been manually done by observing the RGB videos, frame by frame. The start frame of each action is identified as the subject starts to move the arm to the component to be grasped. The end frame, instead, is recorded when the subject releases the component, so the next frame becomes the start frame of the subsequent action.The total number of actions annotated in this study is 4123, including the “don't care” action (ID=0) and the action repetitions in the case of actions 2, 3 and 11.Available codeThe dataset has been acquired using the Multiple Azure Kinect GUI software, available at https://gitlab.com/roberto.marani/multiple-azure-kinect-gui, based on the Azure Kinect Sensor SDK v1.4.1 and Azure Kinect Body Tracking SDK v1.1.2.The software records device data to a Matroska (.mkv) file, containing video tracks, IMU samples, and device calibration. In this work, IMU samples are not considered.The same Multiple Azure Kinect GUI software processes the Matroska file and returns the different types of data provided with our dataset: RGB images, RGB-depth-Aligned (RGB-A) images, Depth images, IR images, Point Cloud and Skeleton data.

Robot Vision Dataset Services for Space Market Outlook

According to our latest research, the global Robot Vision Dataset Services for Space market size reached USD 1.43 billion in 2024, with a robust CAGR of 17.2% expected from 2025 to 2033. By the end of the forecast period, the market is projected to achieve a value of USD 5.28 billion by 2033. The primary growth factor fueling this market is the escalating demand for highly accurate and annotated vision datasets, which are critical for autonomous robotics and AI-driven operations in space missions. This surge is underpinned by rapid advancements in satellite imaging, planetary exploration, and the increasing adoption of AI technologies by space agencies and commercial space enterprises.

One of the foremost growth drivers for the Robot Vision Dataset Services for Space market is the increasing complexity and scale of space missions. As space agencies and private companies undertake more ambitious projects, such as lunar bases, Mars exploration, and asteroid mining, the demand for sophisticated vision systems powered by high-quality datasets has soared. These datasets are essential for training AI models that enable robots to navigate, identify objects, and make autonomous decisions in unpredictable extraterrestrial environments. The need for precise data annotation, labeling, and validation is paramount, as even minor errors can lead to mission-critical failures. Consequently, service providers specializing in vision dataset curation are witnessing a surge in demand, especially for custom solutions tailored to specific mission requirements.

Another significant factor propelling market growth is the proliferation of commercial space ventures and the democratization of space technology. As more private entities enter the space sector, there is an increased emphasis on cost-effective and scalable solutions for robotic automation and navigation. The integration of AI and machine learning in satellite imaging, spacecraft navigation, and planetary exploration necessitates vast volumes of annotated image, video, and 3D point cloud data. Companies are investing heavily in dataset services to reduce mission risks, enhance operational efficiency, and accelerate time-to-market for new space technologies. This trend is further amplified by advancements in sensor technologies, multispectral imaging, and real-time data transmission from space assets.

Furthermore, the growing collaboration between international space agencies, research institutes, and commercial players is fostering innovation and driving the adoption of standardized vision datasets. Joint missions and shared infrastructure require interoperable datasets that can support diverse robotic platforms and AI algorithms. This has led to the emergence of specialized dataset service providers offering end-to-end solutions, including data collection, annotation, labeling, and validation across multiple formats and spectral bands. As the space sector becomes increasingly interconnected, the demand for robust, high-fidelity datasets that adhere to global standards is expected to intensify, further fueling market expansion.

Regionally, North America dominates the Robot Vision Dataset Services for Space market, accounting for the largest share in 2024, driven by the presence of major space agencies like NASA and a vibrant commercial space ecosystem. Europe follows closely, benefiting from strong government support and collaborative research initiatives. The Asia Pacific region is emerging as a high-growth market, propelled by significant investments in space technology by countries such as China, India, and Japan. Latin America and the Middle East & Africa are also witnessing increased activity, albeit from a smaller base, as local space programs gain momentum and seek advanced vision dataset services to support their missions.

Service Type Analysis

The Service Type segment in the Robot Vision Dataset Services for Space market encompasses a diverse range of offeri

This datasets contains motion and force measurements of humans performing various manual tasks, as well as annotations of the actions and postures adopted by the participants. 13 participants performed a series of activities mimicking industrial tasks, such as setting screws at different heights and manipulating loads (15 trials per participant, duration of one trial: between 1.5 and 2 min). Participants' whole-body kinematics and hand contact pressure force were recorded. Whole-body kinematics was recorded both with optical (gold standard) and inertial motion capture systems. Hand pressure force was recorded with a prototype glove equipped with pressure sensors. Videos of the participants performing the activities were then annotated by 3 human annotator, to specify the action performed and the posture adopted in each frame of the video. Posture taxonomy follows the Ergonomic Assessment Worksheet (EAWS) postural grid. Action taxonomy defines elementary actions such as reaching, carrying, picking.

All data files are provided in proprietary format (when existing), in standard motion analysis format and in csv format. Annotations are provided in csv format. Videos of a human avatar replaying participants' motion are also provided (annotations were performed on those videos).

A detailed description of how the data were collected is available in the paper associated with the dataset: "Human Movement and Ergonomics: an Industry-Oriented Dataset for Collaborative Robotics" (Maurice et al., IJRR, in press) https://hal.archives-ouvertes.fr/hal-02289107/document

A novel audio-visual dataset capturing human social eating behaviors of groups of three people sharing a meal. It contains multi-view RGBD video and directional audio recordings of 30 sessions, totaling over 18 hours of multistream, multimodal recordings of 90 people, and provides the following data. ROS bags with topics: 4x mic audio, mixed audio, sound direction, per-participant RGBD, and scene RGBD. Raw data (extracted from ROS bags): scene audio, sound direction, per-participant videos, and scene videos. Processed data (extracted from raw data): per-participant speaking status, per-participant face and body keypoints from OpenPose, per-participant gaze, and head pose from RT-GENE, per-participant bite count, and per-participant times since the last bite lifted and since the last bite delivered to mouth. Annotations: per-participant interactions with food, drink, and napkins (all entered, lifted, delivered to mouth, and mouth open events), per-participant food type labels, and observations of interesting behaviors. Please see Section 5 (and the referenced Appendix sections) of our paper for the data collection study setup, data annotation details, pre- and post-study questionnaires, and data statistics.

ROSMAT24 is a subset of a subset of ROSMA that includes instrument annotations for 24 videos, 22 videos of pea on a peg instances, and 2 videos of post and sleeve. Unlike most of the previous work on instrument detection, we provide separate labeled bounding boxes for the tip of PSM1 and PMS2. This data has a total of 48.919 manually annotated images.

"Nephrec9" dataset contains images of 14 steps of the Robot-Assisted Partial Nephrectomy (RAPN) procedure. “Nephrec9” dataset is developed from 9 full video annotations of RAPN which was annotated by an expert renal surgeon. Extracted videos were divided into small videos of 30 seconds or 720 frames, processed at 24 FPS. We extracted a total of 1262 videos, out of which we used 769 (approx. 60%) for training, 372 (approx. 30%) for the validation and 121 (approx. 10%) for testing. We have provided images of the following RAPN steps: Mobilization Dissection Identification Ultrasound Marking Clamping Resection Midollar suturing Cortical suturing Unclamping Inspection Removal Reconstruction Drainage If you use this dataset in your research or to know more about the dataset, kindly please contact to Hirenkumar Nakawala at hirenkmar.nakawala@polimi.it. Kindly please inform Dr. Hiren Nakawala if you download this dataset - thank you. We will upload some tools to process this dataset easily. If you want to know more about the dataset or have any confusions, please let us know. To know more about this research, please see the manuscript: “Deep-Onto” network for surgical workflow and context recognition, IJCARS, Nov 2018. Link: https://link.springer.com/article/10.1007/s11548-018-1882-8

Dataset accompanying our HRI 2023 paper:Leimin Tian, Kerry He, Shiyu Xu, Akansel Cosgun, and Dana Kulić. 2023. Crafting with a Robot Assistant: Use Social Cues to Inform Adaptive Handovers in Human-Robot Collaboration. In Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction (HRI '23), March 13–16, 2023, Stockholm, Sweden. ACM, New York, NY, USA, 9 pages. (ACM copy, arXiv copy)This record contains two out of three segments of the FACT dataset, namely:FACT-processed:Non-identifiable data as csv files, including the robot's status, the controls of the teleoperation framework, the estimates of facial and upper body keypoints from the OAK-D camera feed, and the emotion estimates from both camera feeds, extracted from the synchronised raw data at 0.1s intervals. In addition, the de-identified questionnaire responses are provided.FACT-support:Supporting materials, including the instruction sheet, operator's cheat-sheet for teleoperation framework controls, CAD file for the laser-cutting design of the birdhouse; implementation of the teleoperation framework, data processing, and feature extraction provided as fetch-teleop-main.zip. More up to date implementation can also be found at https://github.com/tianleimin/fetch-teleopFACT-raw:Please see https://doi.org/10.26180/21671789.v1Related datasets:Extracted csv data from FACT-HRC stage 1 (teleoperated handovers): https://doi.org/10.26180/21671768Raw ROS bag data of FACT-HRC stage 1 (teleoperated handovers): https://doi.org/10.26180/21671789Extracted csv data files of FACT-HRC stage 2 (auto handovers): https://doi.org/10.26180/25449640Raw ROS bag data of FACT-HRC stage 2 (auto handovers): https://doi.org/10.26180/25449652Questionnaire and annotation from FACT-HRC stage 3 (online video comparison): https://doi.org/10.26180/28492796

IntroductionIn Industry 4.0, collaborative tasks often involve operators working with collaborative robots (cobots) in shared workspaces. Many aspects of the operator's well-being within this environment still need in-depth research. Moreover, these aspects are expected to differ between neurotypical (NT) and Autism Spectrum Disorder (ASD) operators.MethodsThis study examines behavioral patterns in 16 participants (eight neurotypical, eight with high-functioning ASD) during an assembly task in an industry-like lab-based robotic collaborative cell, enabling the detection of potential risks to their well-being during industrial human-robot collaboration. Each participant worked on the task for five consecutive days, 3.5 h per day. During these sessions, six video clips of 10 min each were recorded for each participant. The videos were used to extract quantitative behavioral data using the NOVA annotation tool and analyzed qualitatively using an ad-hoc observational grid. Also, during the work sessions, the researchers took unstructured notes of the observed behaviors that were analyzed qualitatively.ResultsThe two groups differ mainly regarding behavior (e.g., prioritizing the robot partner, gaze patterns, facial expressions, multi-tasking, and personal space), adaptation to the task over time, and the resulting overall performance.DiscussionThis result confirms that NT and ASD participants in a collaborative shared workspace have different needs and that the working experience should be tailored depending on the end-user's characteristics. The findings of this study represent a starting point for further efforts to promote well-being in the workplace. To the best of our knowledge, this is the first work comparing NT and ASD participants in a collaborative industrial scenario.