Q-Rock framework enables robots to autonomously discover and catalog motion capabilities via simulation

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d, stored and matched to tasks by software. The work, published as an open access research article in the journal AI Perspectives on 5 July 2021, describes both the formal framework and a working demonstration on simulated robotic hardware. The core idea is deceptively simple. The authors argue that the set of all possible behaviors of a robotic system is inherently defined by its hardware and software, and that this

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set can be found by letting the system explore itself. Rather than programming a robot to perform a specific task and hoping the skills generalize, Q-Rock first asks what the machine can physically do, before anyone decides what it should do. The exploration is deliberately goal-agnostic, meaning the robot is not steered toward any particular objective during this phase. The point is to generate a maximal variety of

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n open challenges, and the datasets from the use case are not publicly available. Still, the demonstration marks a concrete step toward robots that can, in a meaningful sense, discover what they are capable of before anyone tells them what to do. Subject of Research: An automated development cycle for exploring, classifying and reasoning about robot behaviors Article Title: A development cycle for automated self-expl

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mbination of the two. For the arm, 106 trajectories of four seconds each were generated, with five polynomial parameters per joint, giving fifteen parameters in total, sampled uniformly from a defined range. A validation model, a fully connected neural network with four hidden layers, was trained to predict whether a given parameter set corresponds to a motion the robot can actually execute, reaching an accuracy of 9

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6.6 plus or minus 0.4 percent on unseen data. The mobile base used twelve parameters applied to wheel velocities over twenty-second trajectories, and the combined system had twenty-seven parameters in total. Clustering results varied across the three systems and feature spaces. For the arm, generative model accuracies were 91 plus or minus 2.1 percent for the start-state feature, 87 plus or minus 3.3 percent for dire

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ctness, and 59 plus or minus 4.1 percent for the end effector end state. The mobile base yielded 89, 73 and 47 percent respectively, while the combined system proved hardest, with accuracies of 82, 55 and 32 percent. These figures, determined by sampling parameters, simulating the resulting trajectories and checking cluster assignment over ten training runs, show that the approach works but also that performance degr

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