--- updatedAt: 2026-09-16T22:23:50.000Z agentTools: projectIndex: https://docs.thousandbrains.org/llms.txt --- # Project Showcase A list of projects that utilize Monty or the TBT. This page showcases some projects that were realized using the Monty code-base. If you have a project that you would like to see featured here, simply create a PR adding it to this page. Please make sure your project is well documented, including a README on how to run it and ideally some images or video showcasing it. Feel free to also include a video or image here. Please also keep your description on this page short and concise. # Monty for Object Detection With the iPad Camera [block:embed] { "html": "", "url": "https://www.youtube.com/watch?v=KcE004QbuSw", "title": "2023/03 - Monty's First Live Demo in the Real World", "favicon": "https://www.youtube.com/favicon.ico", "image": "https://i.ytimg.com/vi/KcE004QbuSw/hqdefault.jpg", "provider": "https://www.youtube.com/", "href": "https://www.youtube.com/watch?v=KcE004QbuSw", "typeOfEmbed": "youtube" } [/block] This is the first real-world demo of Monty the TBP team came up with. We used the iPad camera to take an image of an object. Monty then moves a small patch over this image and tries to recognize the object. See the [monty\_lab project folder](https://github.com/thousandbrainsproject/monty_lab/tree/main/monty_meets_world) for more details. # LEGO Robot
The first example of Monty moving its sensors in the real-world. Follow the [LEGO tutorial](/docs/using-monty-for-robotics#example-3-lego-based-robot) to try this out yourself. See the [everything\_is\_awesome repository](https://github.com/thousandbrainsproject/everything_is_awesome) for more information. Watch the video: [block:embed] { "html": "", "url": "https://www.youtube.com/watch?v=_u7STtACQ50", "title": "2025/05 - Robot Hackathon Presentations", "favicon": "https://www.youtube.com/favicon.ico", "image": "https://i.ytimg.com/vi/_u7STtACQ50/hqdefault.jpg", "provider": "https://www.youtube.com/", "href": "https://www.youtube.com/watch?v=_u7STtACQ50", "typeOfEmbed": "youtube" } [/block] # Ultrasound Perception [block:embed] { "html": "", "url": "https://www.youtube.com/watch?v=qhrHyTYVPgo", "title": "Monty for Ultrasound - A Real World Challenge", "favicon": "https://www.youtube.com/favicon.ico", "image": "https://i.ytimg.com/vi/qhrHyTYVPgo/hqdefault.jpg", "provider": "https://www.youtube.com/", "href": "https://www.youtube.com/watch?v=qhrHyTYVPgo", "typeOfEmbed": "youtube" } [/block] Using sensorimotor AI to guide ultrasound. Follow the [ultrasound tutorial](/docs/using-monty-for-robotics#example-2-ultrasound) for more details. See the [ultrasound\_perception repository](https://github.com/thousandbrainsproject/ultrasound_perception) for more information. The repository also includes a downloadable dataset and benchmarks. Watch the video of the first demo: [block:embed] { "html": "", "url": "https://www.youtube.com/watch?v=-zrq0oTJudo", "title": "2025-05 Ultrasound Presentation and Demo", "favicon": "https://www.youtube.com/favicon.ico", "image": "https://i.ytimg.com/vi/-zrq0oTJudo/hqdefault.jpg", "provider": "https://www.youtube.com/", "href": "https://www.youtube.com/watch?v=-zrq0oTJudo", "typeOfEmbed": "youtube" } [/block] # Undergraduate Dissertation Community Project ## Robotic Object Recognition for Thousand-Brains Systems **Author:** Zachary Danzig from Loughborough University\ **Links:** [tbp.monty Fork](https://github.com/Zinxiee/tbp.monty) | [Maixsense A010 Python Package](https://github.com/Zinxiee/Maixsense-A010-Python-Package) | [3D Model Dataset](https://github.com/Zinxiee/tbp.monty/releases/tag/dataset_scans_\(shining_3d\)) | [Robotic Object Recognition For Thousand Brain Systems.pdf](https://github.com/user-attachments/files/30555412/Robotic.Object.Recognition.For.Thousand.Brain.Systems.pdf) This community project expands upon the Thousand Brains Project by designing and constructing a system capable of multimodal sensing and movement. The core aim is to enable the learning and recognition of real-world objects in a human-like way (sensorimotor learning), helping to bridge the framework's sim-to-real gap. While Monty has primarily been validated in simulated environments, this dual-agent sensorimotor system tests how Monty performs on physical hardware in the real world.\ Robotic Object Recognition For Thousand Brain Systems **Dual-agent Sensorimotor Setup**\ 20260424_2050061\ *The dual-agent sensorimotor setup featuring the Ufactory Lite 6 with mounted A010 ToF sensor and Zed 2i stereo camera, with the mc\_fox object in view.* ### Key Features * **Distant Agent Implementation:** Developed around the Zed 2i stereo camera to extend the existing [Monty Meets World](https://docs.thousandbrains.org/docs/project-showcase#monty-for-object-detection-with-the-ipad-camera) pipeline. * **RGBD Scene Indexing:** Supports continual capture and indexing of RGBD scenes. * **Surface Agent Implementation:** Built using a Maixsense A010 ToF sensor mounted to a Ufactory Lite 6 robot arm. * **Custom Integration:** Integrated with Monty through a custom environment, environment interface, and motor-policy. * **Hardware Tooling:** Includes a reverse-engineered Python package that exposes programmatic control of the Maixsense A010 sensor. * **Object Dataset:** Evaluated the system against a structured eight-object dataset with defined orientations suitable for evaluating recognition accuracy, continual learning, and rotation invariance. As an extension to the project, a 3D Model Dataset was scanned in and made available to the community. Note that this project does not integrate the different modalities at the same time. Each agent is run independently. ### Results and Findings * The Distant Agent demonstrated reliable supervised recognition with sub-degree rotation errors. * The Surface Agent exposed critical hardware limitations regarding Time-of-Flight (ToF) scattering on non-flat geometries which heavily restricted Surface Agent experiments. * This work provides a real-world starting point for testing the framework's physical deployment and constraints. [Surface Agent Demo with TBP Mug](https://youtube.com/shorts/E8tcEsyo7-U) ### Monty Meets World Proof of Concept At the start of this project a proof of concept was demonstrated by reproducing the Monty Meets World Experiment above. For information on how this was done, see the following links: [Explanation](https://forum.thousandbrains.org/t/design-of-final-year-undergraduate-project-with-the-inclusion-of-the-tbp/417/30?u=zachary_danzig) | [Experiment YAML](https://github.com/Zinxiee/tbp.monty/blob/main/src/tbp/monty/conf/experiment/zachs_examples/zachs_monty_meets_world.yaml) | [Demo](https://youtu.be/a9u1Y3Amlxc). For more about this project, visit the links above or see the [Discourse community topic](https://forum.thousandbrains.org/t/design-of-final-year-undergraduate-project-with-the-inclusion-of-the-tbp/417). Watch the presentation: [block:embed] { "html": "", "url": "https://www.youtube.com/watch?v=3wgHNqkRekM", "title": "07/2026 - Robotic Object Recognition for Thousand Brains Systems", "favicon": "https://www.youtube.com/favicon.ico", "image": "https://i.ytimg.com/vi/3wgHNqkRekM/hqdefault.jpg", "provider": "https://www.youtube.com/", "href": "https://www.youtube.com/watch?v=3wgHNqkRekM", "typeOfEmbed": "youtube" } [/block]

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