Robocraft Royale utilizes Robocraft‘s unique combat and damage mechanics creating a tense battle royale in the Robocraft universe. Our learned model-based planning framework is comparable to and sometimes better than human subjects on the tested tasks.Robocraft Royale is a free-to-play robot themed battle royale shooter game with 100 Players competing in 100 Crazy Rides including Mechs, tanks, dinosaurs, helicopters, jet fighters, monster trucks, you drive them, you fight with them in an epic fight for survival with only one winner. We also conduct comparisons between RoboCraft and untrained human subjects controlling the gripper to manipulate deformable objects in both simulation and the real world.

We perform systematic evaluations in both simulation and the real world to demonstrate the robot’s manipulation capabilities and ability to generalize to a more complex action space, different tool shapes, and a mixture of motion modes. We show through experiments that with just 10 minutes of real-world robotic interaction data, our robot can learn a dynamics model that can be used to synthesize control signals to deform elasto-plastic objects into various target shapes, including shapes that the robot has never encountered before. The learned model can then be coupled with model-predictive control (MPC) algorithms to plan the robot’s behavior. It transforms the sensing data into particles and learns a particle-based dynamics model using graph neural networks (GNNs) to capture the structure of the underlying system. Our system, RoboCraft, only assumes access to raw RGBD visual observations. We propose to tackle these challenges by employing a particle-based representation for elasto-plastic objects in a model-based planning framework. However, due to the high degree of freedom of elasto-plastic objects, significant challenges exist in virtually every aspect of the robotic manipulation pipeline, e.g., representing the states, modeling the dynamics, and synthesizing the control signals. Modeling and manipulating elasto-plastic objects are essential capabilities for robots to perform complex industrial and household interaction tasks (e.g., stuffing dumplings, rolling sushi, and making pottery). Yunzhu Li (Massachusetts Institute of Technology) , Zhiao Huang (University of California San Diego) , RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks

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