Learning musical structures and composition patterns is necessary for both music generation and understanding, but current methods do not make uniform use of learned features to generate and comprehend music simultaneously. In this paper, we propose PianoBART, a pre-trained model that uses BART for both symbolic piano music generation and understanding. We devise a multi-level object selection strategy for different pre-training tasks of PianoBART, which can prevent information leakage or loss and enhance learning ability. The musical semantics captured in pre-training are fine-tuned for music generation and understanding tasks. Experiments demonstrate that PianoBART efficiently learns musical patterns and achieves outstanding performance in generating high-quality coherent pieces and comprehending music.

Small robots encounter considerable difficulties in learning effective motions on complex terrains owing to their underactuated nature and nonlinear dynamics. In this paper, we present a novel framework for robot motion generation that implements reinforcement learning, based on simplified exploration of the robot’s action and time slice conduction. Our framework controls the robot’s actions using normalized signals and hierarchical mappings on mathematical space, which facilitates the learning process. We execute action in the timeslice to make efficient interaction with the environment. We evaluate the efficacy of our approach on a varied set of simulated terrain scenarios, which include various obstacles and terrain undulations. Our results show that our approach effectively achieves efficient motions on complex terrains designed for small-sized robots.