Human's interference in the process of skill learning can improve the performance of the robot greatly. However, learning from demonstration to generate a new action with human behavioral characteristics in the varying situation is challenging. Generally, dynamic movement primitives (DMPs) method can generalize the trajectory imitating the demonstration, but cannot integrate the feature of multiple trajectories of different targets. In this paper, the proposed method contains two aspects of learning and generating. The statistical method Gaussian mixture model and Gaussian mixture regression (GMM-GMR) is used to extract the common characteristic and eliminate the uncertainty of the multiple demonstrations. To exert the ability of DMPs to generate a human-like motion to a new goal, and we model the shape parameter with locally weighted regression (LWR) method. To enhance the ability of DMPs in multiple trajectories learning, we propose the multivariate Gaussian process regression (MV-GPR) method to construct the regression model of shape parameters to reflect the human intentions, with respect to the target position. To verify the feasibility of the proposed method, we design a peg-in-hole experiment with proving generalization and obstacle avoidance performance. The results have shown that the strategy integrated the generalization of DMPs and feature regeneration ability of MV-GPR method, and the generated valid trajectory could achieve the peg-in-hole task with 6-DOF whole-arm avoidance.INDEX TERMS Dynamic movement primitives, learning from demonstration, MV-GPR, whole-arm obstacle avoidance.
Daily manipulation tasks are characterized by regular characteristics associated with the task structure, which can be described by multiple geometric primitives related to actions and object shapes. Such geometric descriptors can not be expressed only in Cartesian coordinate systems. In this paper, we propose a learning approach to extract the optimal representation from a dictionary of coordinate systems to represent an observed movement. This is achieved by using an extension of Gaussian distributions on Riemannian manifolds, which is used to analyse a set of user demonstrations statistically, by considering multiple geometries as candidate representations of the task. We formulate the reproduction problem as a general optimal control problem based on an iterative linear quadratic regulator (iLQR), where the Gaussian distribution in the extracted coordinate systems are used to define the cost function. We apply our approach to grasping and box opening tasks in simulation and on a 7-axis Franka Emika robot. The results show that the robot can exploit several geometries to execute the manipulation task and generalize it to new situations, by maintaining the invariant features of the skill in the coordinate system(s) of interest.
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