How to Build a Robotic Tactile Dataset: A Step-by-Step Guide Inspired by DAIMON Robotics' Daimon-Infinity

Introduction

Imagine a robot that can fold a delicate shirt without wrinkling it or assemble a tiny screw on a production line with the precision of a human hand. This level of dexterity requires more than just vision – it demands a sense of touch. In April 2025, DAIMON Robotics released Daimon-Infinity, the world's largest omni-modal robotic dataset for Physical AI, integrating high-resolution tactile sensing with vision and language. This guide distills the key steps behind their approach, providing a practical roadmap for researchers and engineers aiming to give robot hands a true sense of touch. Whether you're building a dataset for household chores or industrial automation, these principles will help you create a robust tactile foundation for your robots.

What You Need

• High-Resolution Tactile Sensor: A vision-based sensor with at least 110,000 effective sensing units per fingertip (like DAIMON's monochromatic sensor).
• Distributed Data Collection Network: A system capable of generating millions of hours of tactile data annually, ideally across multiple locations.
• Multimodal Data Integration Platform: Tools to combine vision, language, action, and tactile data into a unified dataset.
• VTLA Architecture Framework: A Vision-Tactile-Language-Action model that treats touch as a primary modality.
• Collaboration Partners: Access to academic and industry partners (e.g., Google DeepMind, universities) for data validation and algorithm development.
• Open-Source Infrastructure: A system to release public portions of the dataset to accelerate community research.

Step-by-Step Guide

Step 1: Develop High-Resolution Tactile Sensors

The foundation of any tactile dataset is the sensor itself. Your sensor must capture fine-grained pressure, texture, and slip information. DAIMON's approach uses a vision-based design with over 110,000 sensing units in a fingertip-sized module. To replicate this, focus on miniaturizing camera-based tactile sensors that convert physical contact into high-resolution images. Test sensor durability for thousands of manipulation cycles to ensure consistent data quality. Calibrate sensitivity to detect forces from a few millinewtons to several newtons.

Step 2: Establish a Distributed Data Collection Network

To amass large-scale datasets, you cannot rely on a single lab. Build a network of data collection stations in diverse environments – homes, factories, hotels, and convenience stores. Each station should be equipped with identical sensor-hardware setups and standardized data logging software. Use cloud-based aggregation to merge data streams. DAIMON's network can generate millions of hours annually, so plan for scalable storage and processing. Ensure each station records metadata such as object type, task, and environmental conditions.

Step 3: Design and Aggregate Omni-Modal Data

A dataset for physical AI must include multiple modalities: vision (RGB cameras), language (task descriptions), action (joint angles, forces), and tactile (sensor readings). For each manipulation task – like folding laundry or assembly – synchronize these streams with timestamps. Cover at least 80 different real-world scenarios and 2,000 human-performed skills, as in Daimon-Infinity. Include varying lighting, object materials, and failure cases to enrich the dataset.

Step 4: Implement Vision-Tactile-Language-Action (VTLA) Architecture

The VTLA architecture elevates tactile data to the same importance as vision, moving beyond the common Vision-Language-Action (VLA) model. Your neural network should process tactile images as a separate input channel, not just a supplement. Design attention mechanisms that fuse tactile features with visual and linguistic cues. Train the system on your collected data to predict actions or classify contact states. Consider using a transformer-based model that handles all four modalities simultaneously.

Step 5: Scale Data Generation to Millions of Hours

Quantity matters for deep learning. Using your distributed network, aim to collect data continuously. Automate repetition of common tasks to generate millions of hours of manipulation data. Employ active learning to focus collection on rare or difficult scenarios. Monitor data diversity and balance sensor distribution across tasks. DAIMON's dataset includes million-hour-scale multimodal data – but you can start with 10,000 hours and scale gradually.

Step 6: Open-Source a Portion of the Dataset

To accelerate the field, release a curated subset of your data under an open-source license. This fosters collaboration, attracts expert feedback, and validates your approach. DAIMON open-sourced 10,000 hours of data. Choose a representative sample covering a range of tasks and environments. Provide clear documentation, benchmarks, and code for loading and preprocessing. Maintain a versioning system to track updates.

Step 7: Validate in Real-World Scenarios

Your dataset is only useful if it improves robot performance in actual applications. Deploy robots equipped with your tactile sensors and VTLA model in settings like hotel room service or convenience store stocking. Measure success rates, speed, and adaptability to new objects. Use the collected real-world data to refine your training dataset. DAIMON's robots are already being tested in Chinese hotels and convenience stores, demonstrating the practical benefits of touch-enabled manipulation.

Tips for Success

• Collaborate widely: Follow DAIMON's example by partnering with leading institutions like Google DeepMind, Northwestern University, and the National University of Singapore. Cross-disciplinary expertise sharpens sensor design and algorithm development.
• Prioritize tactile resolution: A sensor with 110,000 sensing units outperforms lower-resolution alternatives. Invest in custom fabrication if needed.
• Include failure modes: Real-world manipulation includes slips, drops, and misalignments. Your dataset must capture these to train robust policies.
• Standardize data format: Use common file formats (e.g., HDF5) and metadata schemas to ease sharing and reproducibility.
• Iterate on architecture: VTLA is still evolving; experiment with different fusion strategies and loss functions.
• Think beyond grippers: As Prof. Michael Yu Wang emphasizes, touch enables dexterous manipulation for tasks like folding laundry – aim for whole-hand sensing in future iterations.

Building a tactile dataset at the scale of Daimon-Infinity is ambitious, but by following these steps – sensor innovation, distributed collection, multimodal integration, open release, and real-world validation – you can give robot hands the sense of touch they need to transform industries and homes alike.