Here, the robots learned how to complete a task like pushing a block by getting some information that will assist it, like “seeing” where the block is, and what the nearby terrain is like. In certain cases, for robots to learn just like humans, trial and error can lead to the best performance of understanding a task, which is the thought behind reinforcement learning. We hope that our work brings us one step closer to a future with robots as intelligent as you or I.” “By creating a large-scale benchmark that focuses on speed and simplicity, we not only create a common language for exchanging ideas and results within the reinforcement learning and co-design space, but also enable researchers without stat-of-the-art computing resources to contribute to algorithmic development in these areas. “With Evolution Gym we’re aiming to push the boundaries of algorithms for machine learning and artificial intelligence,” says MIT undergraduate Jagdeep Bhatia, a lead researcher on the project. For example, the “catcher” robot would often dive forward to catch a falling block that was falling behind it.Įven though the robot designs evolved autonomously from scratch and without prior knowledge by the co-design algorithms, in a step toward more evolutionary processes, they often grew to resemble existing natural creatures while outperforming hand-designed robots. For instance, sometimes the optimized robots exhibited what the team calls “frustratingly” obvious nonoptimal behavior on many tasks. In over 30 different environments, the bots performed amply on simple tasks, like walking or carrying an item, but in more difficult environments, like catching and lifting, they fell short, showing the limitations of current co-design algorithms. In addition to standard tasks like walking and jumping, the researchers also included some unique tasks, like climbing, flipping, balancing, and stair-climbing. The result looks like a little robot Olympics. The design optimization asks “how well does the design perform?” and the control optimization responds with a score, which could look like a five for “walking.” The co-design algorithm functions somewhat like a power couple, where the design optimization methods evolve the robot’s bodies and the RL algorithms optimize a controller (a computer system that connects to the robot to control the movements) for a proposed design. To test the robot’s aptitude, the team developed their own co-design algorithms by combining standard methods for design optimization and deep reinforcement learning (RL) techniques. The robots in the simulator look a little bit like squishy, moveable Tetris pieces made up of soft, rigid, and actuator “cells” on a grid, put to the tasks of walking, climbing, manipulating objects, shape-shifting, and navigating dense terrain. Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), aimed to fill the gap by designing “Evolution Gym,” a large-scale testing system for co-optimizing the design and control of soft robots, taking inspiration from nature and evolutionary processes. Co-optimizing for both elements is hard - it takes a lot of time to train various robot simulations to do different things, even without the design element. ![]() You’d need to optimize for both the brain and the body, perhaps by giving the bot some high-tech legs and feet, coupled with a powerful algorithm to enable the climb.Īlthough design of the physical body and its brain, the “control,” are key ingredients to letting the robot move, existing benchmark environments favor only the latter. Let’s say you wanted to build the world’s best stair-climbing robot.
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