Today’s AI-powered robots are learning to perform tasks such as folding laundry, cooking food, cleaning surfaces and unloading shopping baskets—tasks that would have been extremely difficult for their predecessors. (Source: Image by RR)

New AI Techniques Enable Robots to Navigate and Adapt to Home Environments

Researchers are using generative AI and other advanced techniques to teach robots new skills, potentially bringing them into homes to perform various tasks. Henry Evans, who became quadriplegic after a stroke, and his wife Jane have hosted numerous robots in their home, seeking to enhance Henry’s autonomy. Early robots like the PR2, although groundbreaking, were cumbersome and expensive. More recent robots, like Stretch, are smaller and more affordable, allowing Henry to perform tasks such as brushing his hair and playing with his granddaughter.

Despite these advances, home environments remain challenging for robots due to their unpredictability. Recent progress in AI, however, is enabling robots to learn new skills and adapt more quickly to these varied settings. As noted in, researchers are focusing on developing general-purpose robot brains using neural networks that can be applied to different robots and scenarios, a shift from the traditional method of pre-programming specific tasks. This new approach has shown promising results, with robots learning to navigate complex terrains and perform a variety of household chores.

Generative AI and reinforcement learning are key techniques driving these advancements. Researchers at institutions like Carnegie Mellon and the Toyota Research Institute have used these methods to teach robots tasks such as cooking, cleaning, and even extreme parkour. By training robots through both simulation and real-world interactions, they are able to learn and adapt in real time. This has led to significant improvements in robot autonomy and versatility.

However, challenges remain, including the need for extensive data to train AI models and the high cost of hardware. Initiatives like Google’s Open X-Embodiment Collaboration aim to create large, diverse datasets to support further development. While fully functional home robots are not yet a reality, the integration of AI is rapidly advancing the field, bringing the dream of useful, adaptable home robots closer to fruition. For individuals like Henry Evans, these developments offer the potential for greater independence and improved quality of life.