TeachAnything: A Multimodal Crowdsourcing Platform for Training Embodied AI Agents in Symmetrical Reality

2026-05-14

AI Summary

This work addresses the scarcity of high-quality human demonstration data—a key bottleneck in developing human-like embodied intelligence within Symmetrical Reality. To overcome this challenge, the authors propose a novel three-stage multimodal demonstration paradigm that integrates a cloud-based crowdsourcing platform, TeachAnything, with physics-based simulation into a unified virtual-physical training framework. This approach enables efficient collection of diverse, high-fidelity demonstrations across varied environments, tasks, and embodiment morphologies. By transcending the scale and diversity limitations of conventional data collection methods, the framework establishes a scalable demonstration infrastructure, providing both a systematic empirical foundation and critical technical support for training embodied agents in Symmetrical Reality.

Abstract

Symmetrical Reality (SR) is emerging as a future trend for human-agent coexistence, placing higher demands on agents to acquire human-like intelligence. It calls for richer and more diverse human guidance. We introduce a three-stage demonstration paradigm integrating multimodal demonstration signals. Building on this paradigm, we developed TeachAnything, a cloud-based, crowdsourcing-oriented demonstration platform with physics simulation capable of collecting diverse demonstration data across varied scenes, tasks, and embodiments. By unifying virtual and physical interactions through both methodological design and physics simulation, the system serves as a practical foundation for developing embodied agents aligned with Symmetrical Reality.

Problem

• Symmetrical Reality
• Embodied AI
• Human Guidance
• Multimodal Demonstration
• Crowdsourcing

Innovation

• Symmetrical Reality
• Embodied AI
• Multimodal Demonstration
• Crowdsourcing Platform
• Physics Simulation