TeachAnything: A Multimodal Crowdsourcing Platform for Training Embodied AI Agents in Symmetrical Reality
For researchers developing embodied AI agents, this platform provides a practical tool for collecting diverse demonstration data, though it is an incremental contribution as it combines existing concepts (crowdsourcing, multimodal demonstrations, physics simulation) without introducing fundamentally new methods.
The paper introduces TeachAnything, a cloud-based crowdsourcing platform for collecting multimodal demonstration data to train embodied AI agents in Symmetrical Reality, addressing the need for diverse human guidance. The platform unifies virtual and physical interactions through a three-stage demonstration paradigm and physics simulation.
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.