Robotic Arm Data Collection Box AI robotics
The Robotic-Arm Data Collection Box is a portable calibration experiment platform designed specifically for AI robotics, embodied intelligence, visual grasping, and action imitation learning.
The light source, camera mount, and acquisition positions remain fixed in both folded and unfolded configurations. Whether used in a laboratory, classroom, trade show, or customer site, you can reproduce a consistent acquisition environment, ensuring stable, comparable, and transferable model outputs.
Integrated Fixed Calibration, No More Repeated Data Collection
Environmental conditions remain consistent at all times: LED light panel with fixed brightness and angle, camera mount with fixed height and tilt angle, and fixed positions for the test object and light acquisition points.
Out-of-the-Box Ready, Zero Training Required
Environment and pose calibration are completed at the factory. No need to run data collection workflows—simply connect and perform direct inference with models such as ACT, SmolVLA, and groot.
Effortless Folding, Truly Portable
Six-sided folding design for easy opening and closing. One box for classroom demos, traveling presentations, and on-site debugging.
Stable Light Source, Consistent Day and Night
Built-in high-CRI LED lights eliminate interference from ambient light fluctuations, ensuring stable image brightness, color temperature, and shadows.
Customizable Adjustments
The camera mount height and placement can be adjusted as needed to build your own custom model setup.
Shared Datasets
Directly download datasets collected by others and skip the tedious data collection phase.
| Dimensions |
80.5 × 64 × 70 cm ± 0.5 cm |
| Main Material |
420D Silver-coated Reflective Fabric + Iron |
| Operating Voltage |
Input: AC 100-245V 50/60Hz; Output: 12V |
| Rated Power |
20W |
| Luminous Flux (Lm) |
≥ 2000 Lm |
| Color Temperature |
5600K |
Application
Fixed-Environment Visual Acquisition and Dataset Reuse
The Robotic-Arm Data Collection Box provides a stable, consistent, and reproducible enclosed environment for visual acquisition tasks. Built-in fixed LED lighting, a fixed camera mount, and fixed acquisition positions ensure that lighting conditions, viewing angles, and object-distance relationships remain identical in every experiment, effectively eliminating data noise caused by ambient light fluctuations, camera shake, and positional deviations. The acquired image data can be directly reused for training and inference with models such as ACT, SmolVLA, and groot—no need to recollect datasets when changing locations. This significantly reduces redundant data collection and annotation costs, accelerating the iteration cycle of embodied intelligence algorithms.
Portable Model Inference and On-Site Demonstration
With its compact folded size and six-sided flat-unfolding structure, the Robotic Arm Data Acquisition Box easily fits into a backpack or equipment case, allowing you to carry it to laboratories, classrooms, trade shows, or customer sites. In its folded state, it serves as a self-contained enclosed chamber for model inference; when unfolded, it acts as a display panel that intuitively presents the internal calibration structure and algorithm workflow. Whether for technical roadshows, product launches, or customer POCs, users can quickly set up a unified demonstration environment, ensuring stable and reproducible inference results from models like ACT, SmolVLA, and groot, while enhancing the credibility and presentation efficiency of technical solutions.
Teaching Experiments and Rapid Algorithm Validation
The Robotic Arm Data Acquisition Box is an ideal experiment platform for universities, training institutions, and developer communities conducting robotics and AI education. Its no-setup, pre-calibrated, out-of-the-box design lowers the barrier to experimentation. Instructors can unfold the chamber to explain the calibration relationships among the light source, camera position, and acquisition points, while students can directly conduct visual grasping, action imitation, and model validation experiments in the folded state. Algorithm engineers can also leverage this standardized environment to quickly test the performance of new models and strategies across different control tasks—no need to spend extensive time building test benches—allowing them to focus more on algorithm optimization and innovation.
