Robotic grasping of a target object in cluttered environments poses considerable challenges, often due to limited collision-free grasp affordances caused by the close proximity of other objects. To overcome this limitation, non-prehensile actions like pushing can be strategically employed to manipulate the environment and improve the chances of successful grasps. In this paper, we introduce a novel pre-grasp manipulation policy designed to efficiently retrieve a target object from dense clutter by leveraging pushing actions and considering the gripper’s kinematic capabilities to strategically position the target object within the gripper’s closing region for a secure grasp. Unlike conventional approaches, our policy incorporates sequential pushing, allowing the robot to make decisions while within the camera’s field of view without retracting to a home position, leading to significantly reduced execution time per action. Our policy, trained in simulation, seamlessly transfers to real-world scenarios. Extensive experimental evaluation demonstrates superior performance, faster completion times, and robust generalization to unseen objects compared to existing baselines.

In this work a control scheme is proposed to enforce dynamic obstacle avoidance constraints to the full body of actively compliant robots. We argue that both compliance and accuracy are necessary to build safe collaborative robotic systems; obstacle avoidance is usually not enough, due to the reliance on perception systems which exhibit delays and errors. Our scheme is able to successfully avoid obstacles, while remaining compliant in the entirety of the executed task. Therefore, in case of unexpected collisions due to perception system errors, the robot remains safe for humans and its environment. Our approach is validated through experiments with simulated and real obstacles utilizing a 7-dof KUKA LBR iiwa robotic manipulator.

Trajectory tracking in the orientation space utilizing unit quaternions yields non linear error dynamics as opposed to Cartesian position. In this work, we study trajectory tracking in the orientation space utilizing the most popular quaternion error representations and angular velocity errors. By selecting error functions carefully we show exponential convergence in a region of attraction containing large initial errors. We further show that under certain conditions frequently en-countered in practice, the formulation respecting the geometric characteristics of the quaternion manifold and its tangent space yields linear tracking dynamics allowing us to guarantee a desired tracking performance by gain selection without tuning. Simulation and experimental results are provided.

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.

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.