Adapting Like Humans: Embodied AI Beyond Datasets and Domains

The traditional approaches of training artificial intelligence on fixed datasets within defined domains have significantly advanced the field. However, the dynamic and evolving nature of real-world scenarios requires AI systems to break free from these boundaries, mirroring human adaptability.

This talk unveils the journey towards embodied AI systems, capable of continuously learning across diverse contexts, much like humans. Inspired by recent works on real-time adaptation, we outline how these methodologies serve as stepping stones towards embodied AI.

Embodiment in AI is important because it captures the agent’s ability to perceive, interact, and symbiotically evolve with the environment. We will show how transitioning from static datasets and domain-dependent AI to a more dynamic, embodied AI, can enhance the robustness and applicability of AI systems in the real world. This requires AI to have the ability to adapt in an online learning fashion, responding to the ever-changing conditions it encounters.

Our research is now going beyond academia becoming foundational tools for AI innovators. One example is Rain AI, startup backed by Sam Altman and closely tied to OpenAI, working on neuromorphic computing. Rain is using our work on real-time adaptation as a key component of their technology stack.