Author: Kyle Wiggers / Source: VentureBeat
Robots don’t plan ahead as well as humans, but they’re becoming better at it. That’s the gist of a trio of academic papers Google’s robotics research division highlighted in a blog post this afternoon. Taken together, the authors say, they lay the groundwork for robots capable of navigating long distances by themselves.
“In the United States alone, there are three million people with a mobility impairment that prevents them from ever leaving their homes,” senior research scientist Aleksandra Faust and senior robotics software engineer Anthony Francis wrote. “[Machines could] improve the independence of people with limited mobility, for example, by bringing them groceries, medicine, and packages.”
How? In part by using reinforcement learning (RL), an AI training technique that employs rewards to drive agents toward goals. Faust, Francis, and colleagues combined RL with long-range planning to produce planner agents that can traverse short distances (up to 15 meters) safely, without colliding into moving obstacles. They tapped AutoRL, a tool that automates the search for RL rewards and neural network architectures, to train those agents in a simulated environment. They next used the trained agents to build roadmaps, or graphs comprising nodes (locations) and edges that connect to the nodes only if said agents can traverse between them reliably.
It’s easier said than done; as the researchers point out, training agents with traditional RL approaches poses lots of challenges. It requires spending time iterating and hand-tuning rewards and making poorly informed decisions about AI architectures, not to mention mitigating “catastrophic forgetting,” a phenomenon in which AI systems abruptly forget previously learned information upon learning new information.
AutoRL attempts to solve for this in two phases: reward search and neural network architecture search. During the first stage, it trains agents concurrently over several generations, each with slightly different…
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