Summary Points
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Reinforcement learning, which involves computers learning through experimentation and feedback, gained prominence with Google DeepMind’s development of superhuman algorithms, and has recently been applied to enhance the behavior of large language models.
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Advances in simulation technology now allow robots to practice movements virtually, significantly reducing the need for extensive physical trials, and improving performance outcomes.
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Academic research has demonstrated successful applications of reinforcement learning in enhancing legged locomotion, with teams at UC Berkeley and ETH Zurich training robots to walk and navigate complex terrains.
- The ongoing efforts at the newly founded Robotics and AI (RAI) Institute, led by Marc Raibert, aim to boost the autonomy and intelligence of legged robots, potentially reducing accidents and increasing their functionality in everyday tasks.
Boston Dynamics Leads Robot Revolution with Self-Taught Machines
Boston Dynamics, renowned for its cutting-edge robots, has taken a significant step. The company’s machines are now learning new skills on their own. This leap forward relies heavily on a technique known as reinforcement learning. It allows robots to experiment and receive feedback, improving their abilities with each trial.
Recently, AI engineers have advanced this method. They created highly accurate simulations that speed up the learning process. According to Marc Raibert, founder of the Robotics and AI Institute, these simulations allow robots to practice without physical constraints. “You don’t have to get as much physical behavior from the robot [to generate] good performance,” Raibert explains.
Academic teams from institutions like UC Berkeley and ETH Zurich have also explored this approach. For instance, UC Berkeley’s researchers trained a humanoid to walk around their campus. Similarly, ETH Zurich guided quadrupeds across challenging terrain. Their successes hint at the potential for more complex tasks in the future.
Boston Dynamics has a long history of building legged robots. The company’s work is grounded in Raibert’s insights into how animals maintain balance. Until now, most advanced behaviors, such as dancing or navigating rooms, required extensive programming or human control. However, the latest advancements suggest a change is coming.
In 2022, Raibert founded the RAI Institute. The goal is to enhance machine intelligence, helping robots become more autonomous. Al Rizzi, the institute’s chief technology officer, emphasizes the potential safety benefits. “You break fewer robots when you actually come to run the thing on the physical machine,” he notes.
As these technologies evolve, many wonder about their practical applications. Humanoid robots are becoming increasingly common. What tasks should they undertake? Readers can share their thoughts by writing to hello@wired.com or commenting below. The future of robotics holds promise.
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