In this work we study the DMP spatial scaling in the Cartesian space. The DMP framework is claimed to have the ability to generalize learnt trajectories to new initial and goal positions, maintaining the desired kinematic pattern. However we show that the existing formulations present problems in trajectory spatial scaling when used in the Cartesian space for a wide variety of tasks and examine their cause. We then propose a novel formulation alleviating these problems. Trajectory generalization analysis, is performed by deriving the trajectory tracking dynamics. The proposed formulation is compared with the existing ones through simulations and experiments on a KUKA LWR 4+ robot.
In this work, we propose an augmentation to the Dynamic Movement Primitives (DMP) framework which allows the system to generalize to moving goals without the use of any known or approximation model for estimating the goal's motion. We aim to maintain the demonstrated velocity levels during the execution to the moving goal, generating motion profiles appropriate for human robot collaboration. The proposed method employs a modified version of a DMP, learned by a demonstration to a static goal, with adaptive temporal scaling in order to achieve reaching of the moving goal with the learned kinematic pattern. Only the current position and velocity of the goal are required. The goal's reaching error and its derivative is proved to converge to zero via contraction analysis. The theoretical results are verified by simulations and experiments on a KUKA LWR4+ robot.
Robotic grasping in highly cluttered environments remains a challenging task due to the lack of collision free grasp affordances. In such conditions, non-prehensile actions could help to increase such affordances. We propose a multi-fingered push-grasping policy that creates enough space for the fingers to wrap around an object to perform a stable power grasp, using a single primitive action. Our approach learns a direct mapping from visual observations to actions and is trained in a fully end-to-end manner. To achieve a more efficient learning, we decouple the action space by learning separately the robot hand pose and finger configuration. Experiments in simulation demonstrate that the proposed push-grasping policy achieves higher grasp success rate over baselines and it can generalize to unseen objects. Furthermore, although training is performed in simulation, the learned policy is robustly transferred to a real environment without a significant drop in success rate. Qualitative results, code, pre-trained models and simulation environments are available at https://robot-clutter.github.io/ppg.
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