Cartoon robot11/7/2022 But discretization brings its own limitations - for robots that operate in the spatially continuous real world, there are at least two downsides to discretization: (i) it limits precision, and (ii) it triggers the curse of dimensionality, since considering discretizations along many different dimensions can dramatically increase memory and compute requirements. For example, discretization was a key element of our recent Transporter Networks architecture, and is also inherent in many notable achievements by game-playing agents, such as AlphaGo, AlphaStar, and OpenAI’s Dota bot. To encourage robots to be more decisive, researchers often utilize a discretized action space, which forces the robot to choose option A or option B, without oscillating between options. Although one might expect such a task to be easy, that is often not the case for modern learning-based robots, which often learn behavior that expert observers describe as indecisive or imprecise.Įxample of a baseline explicit behavior cloning model struggling on a task where the robot needs to slide a block across a table and then precisely insert it into a fixture. The robot must commit to just one of these options, but must also be capable of changing plans each time the block ends up sliding farther than expected. There are many possible ways to solve this task, each requiring precise movements and corrections. Consider a task in which a robot tries to slide a block across a table to precisely position it into a slot. Posted by Pete Florence, Research Scientist and Corey Lynch, Research Engineer, Robotics at Googleĭespite considerable progress in robot learning over the past several years, some policies for robotic agents can still struggle to decisively choose actions when trying to imitate precise or complex behaviors.
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