Facebook AI Research (FAIR) is dedicated to advancing the field of socially intelligent robotics. The primary objective is to develop robots capable of assisting with everyday tasks while adapting to the unique preferences of their human partners. The work involves delving deep into embedded systems to establish the foundation for the next generation of AR and VR experiences. The goal is to make robotics an integral part of our lives, reducing the burden of routine chores and improving the quality of life for individuals. FAIR’s multifaceted approach emphasizes the importance of merging AI, AR, VR, and robotics to create a future where technology seamlessly augments our daily experiences and empowers us in previously unimagined ways.
FAIR has made three significant advancements to address scalability and safety challenges in training and testing AI agents in physical environments:
Habitat 3.0 is a high-quality simulator for robots and avatars, facilitating human-robot collaboration in a home-like setting.
The Habitat Synthetic Scenes Dataset (HSSD-200) is a 3D dataset designed by artists to provide exceptional generalization when training navigation agents.
The HomeRobot platform offers an affordable home robot assistant for open vocabulary tasks in simulated and physical-world environments, thereby accelerating the development of AI agents that can assist humans.
Habitat 3.0 is a simulator designed to facilitate robotics research by enabling quick and safe testing of algorithms in virtual environments before deploying them on physical robots. It allows for collaboration between humans and robots while performing daily tasks and includes realistic humanoid avatars to enable AI training in diverse home-like settings. Habitat 3.0 offers benchmark tasks that promote collaborative robot-human behaviors in real indoor scenarios, such as cleaning and navigation, thereby introducing new avenues to explore socially embodied AI.
HSSD-200 is a synthetic 3D scene dataset that provides a more realistic and compact option for training robots in simulated environments. It comprises 211 high-quality 3D sets replicating physical interiors and contains 18,656 models from 466 semantic categories. Although it has a smaller scale, ObjectGoal navigation agents trained on HSSD-200 perform comparably to those introduced on much larger datasets. In some cases, training on just 122 HSSD-200 scenes outperforms agents trained on 10,000 scenes from prior datasets, demonstrating its efficiency in generalization to physical-world scenarios.
In the field of robotics research, having a shared platform is crucial. HomeRobot seeks to address this need by defining motivating tasks, providing versatile software interfaces, and fostering community engagement. Open-vocabulary mobile manipulation serves as the motivating task, challenging robots to manipulate objects in diverse environments. The HomeRobot library supports navigation and manipulation for Hello Robot’s Stretch and Boston Dynamics’ Spot, both in simulated and physical-world settings, thus promoting replication of experiments. The platform emphasizes transferability, modularity, and baseline agents, with a benchmark showcasing a 20% success rate in physical-world tests.
The field of Embodied AI research is constantly evolving to cater to dynamic environments that involve human-robot interactions. Facebook AI’s vision for developing socially intelligent robots is not limited to static scenarios. Instead, their focus is on collaboration, communication, and predicting future states in dynamic settings. To achieve this, Researchers are using Habitat 3.0 and HSSD-200 as tools to train AI models in simulation. Their aim is to assist and adapt to human preferences while deploying these trained models in the physical world to assess their real-world performance and capabilities.
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The post Meta AI Introduces Habitat 3.0, Habitat Synthetic Scenes Dataset, and HomeRobot: 3 Major Advancements in the Development of Social Embodied AI Agents appeared first on MarkTechPost.