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AI has dramatically transformed from its early days of web-based chatbots to the intelligent agents we see today. FAIR (Facebook AI Research) is at the forefront of this evolution, pushing boundaries and imagining a future where AI isn’t just virtual but physically embodied, socially aware, and interactive.
1. Vision of FAIR : Beyond the Screen
FAIR envisions a world where AI-powered assistants are not confined to screens but are part of our physical reality. They are working towards creating all-day wearable augmented reality (AR) glasses with contextualized AI interfaces. Furthermore, they're advancing technology for socially intelligent robots that adapt and cater to the individual needs and preferences of their human counterparts.
2. The Challenge: Real-world Testing
Training AI models in the real world, especially those intended for physical hardware like robots or AR glasses, poses challenges. It's costly, time-consuming, has safety concerns, and lacks standard benchmarking. To overcome these obstacles, FAIR has introduced tools that simplify and accelerate robotics research by leveraging simulators, datasets, and affordable tech stacks.
3. Breakthroughs to Celebrate
Habitat 3.0: This is the apex of simulation technology, designed to train AI agents for human-robot collaboration in home-like settings. It simulates real-world scenarios like cleaning a house, making it a valuable tool for training robots. A unique feature is its human-in-the-loop evaluation framework, which tests AI agents with real human interactions.
Habitat Synthetic Scenes Dataset (HSSD-200): A revolutionary 3D dataset, HSSD-200 features over 18,000 objects across 466 categories in 211 scenes. It enables training of navigation agents with unparalleled accuracy and efficiency, outperforming previous datasets.
HomeRobot: Bridging the gap between simulation and reality, HomeRobot is a hardware and software platform for home robot assistants. It's designed for open vocabulary tasks in both simulated and real-world settings. The platform promotes reproducibility and scalability in robotics research.
4. Why Simulation Matters
Simulation provides a risk-free environment to train and test AI models. Habitat 3.0, for instance, enables experiments that would take years in the real world to be completed in mere days. It also allows for rapid environment changes, safe testing, and scalable data collection. Importantly, the simulation aids in ensuring that when robots are introduced into the real world, they're well-trained, reducing potential safety concerns.
5. A Glimpse of Tasks and Benchmarks
FAIR introduced two essential tasks to standardize the field of social embodied AI:
Social Rearrangement: Robots and humanoid avatars collaboratively tidy up a space, emphasizing coordination for common goals.
Social Navigation: Robots must locate and follow a human while keeping a safe distance, demonstrating the importance of spatial awareness and human-robot interaction.
6. Towards a Dynamic Future
The next phase of FAIR's research will focus on dynamic environments where humans and robots continuously interact. They aim to advance beyond the current static AI models and embrace a future of collaboration, communication, and real-time adaptability.
FAIR's relentless pursuit of embodied AI research is paving the way for a future where humans and robots coexist and collaborate seamlessly. With tools like Habitat 3.0, HSSD-200, and HomeRobot, we're not just moving towards intelligent robots, but socially adept companions ready to integrate into our daily lives. The road ahead is filled with challenges, but with innovations like these, a harmonious human-robot future seems not just possible, but imminent. Source
This article is based on the insights from "Introducing Habitat 3.0: The next milestone on the path to socially intelligent robots" published on October 20, 2023. For a deeper dive, please refer to the original article.