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Pivoting gait is an efficient way for robots to manipulate a heavy object. Although we can cope with the contact constraints of the pivoting gait by using the model predictive control (MPC), systems with complex dynamics, including the pivoting gait, usually require long horizons in the MPC and it leads to a heavy computational load. To overcome this problem, we introduce basis functions to parameterize the free variables in the MPC and formulate an optimization problem with new decision variables, which are coefficients of the basis functions. We especially introduce multiple basis functions and compare their performances in generating the robotic pivoting gait. As a result, the most effective reduction in the dimension of the free variables is achieved by using the Laguerre basis function and the computational efficiency of the MPC is greatly improved. The simulation and experiments show that the time cost of the generation of pivoting gaits by the proposed method is remarkably reduced and the generated pivoting gaits are feasible and robust where a dual-arm robot successfully manipulates a toy piano by the pivoting gait. [ABSTRACT FROM AUTHOR]

When robots are used to manipulate objects in various ways, they often have to consider the dynamic constraint. Machine learning is a good candidate for such complex trajectory planning problems. However, it sometimes does not satisfy the task objectives due to a change in the objective or a lack of guarantee that the objective functions will be satisfied. To overcome this issue, we applied a method of trajectory deformation by using sequence-to-sequence (seq2seq) models. We propose a method of adjusting the generated trajectories, by utilizing the architecture of seq2seq models. The proposed method optimizes the latent variables of the seq2seq models instead of the trajectories to minimize the given objective functions. The verification results show that the use of latent variables can obtain the desired trajectories faster than direct optimization of the trajectories. [ABSTRACT FROM AUTHOR]