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Autonomous assembly has been a desired functionality of many intelligentrobot systems. We study a new challenging assembly task, designing andconstructing a bridge without a blueprint. In this task, the robot needs tofirst design a feasible bridge architecture for arbitrarily wide cliffs andthen manipulate the blocks reliably to construct a stable bridge according tothe proposed design. In this paper, we propose a bi-level approach to tacklethis task. At the high level, the system learns a bridge blueprint policy in aphysical simulator using deep reinforcement learning and curriculum learning. Apolicy is represented as an attention-based neural network with object-centricinput, which enables generalization to different numbers of blocks and cliffwidths. For low-level control, we implement a motion-planning-based policy forreal-robot motion control, which can be directly combined with a trainedblueprint policy for real-world bridge construction without tuning. In ourfield study, our bi-level robot system demonstrates the capability ofmanipulating blocks to construct a diverse set of bridges with differentarchitectures.

Learning to Design and Construct Bridge without Blueprint