We propose a novel pipeline for unknown object grasping in shared robotic autonomy scenarios. State-of-the-art methods for fully autonomous scenarios are typically learning-based approaches optimised for a specific end-effector, that generate grasp poses directly from sensor input. In the domain of assistive robotics, we seek instead to utilise the user’s cognitive abilities for enhanced satisfaction, grasping performance, and alignment with their high level task-specific goals. Given a pair of stereo images, we perform unknown object instance segmentation and generate a 3D reconstruction of the object of interest. In shared control, the user then guides the robot end-effector across a virtual hemisphere centered around the object to their desired approach direction. A physics-based grasp planner finds the most stable local grasp on the reconstruction, and finally the user is guided by shared control to this grasp. In experiments on the DLR EDAN platform, we report a grasp success rate of 87% for 10 unknown objects, and demonstrate the method’s capability to grasp objects in structured clutter and from shelves.

The integration of variable stiffness actuators (VSA) in robotic systems endows them with intrinsic flexibility and therefore robustness to unknown disturbances. However, this characteristic presents a challenge: choosing the best intrinsic stiffness setting guaranteeing the required force ap-plication capability while keeping the system as adaptable to uncertainties as possible. This paper proposes a method to set the optimal stiffness for a multi-finger VSA hand to perform a desired manipulation task. The task is generically represented as a force (with unknown magnitude) applied along a reference direction. According to the force application's direction and the hand's kinematic state, the fingers assume a certain role to split the collective force application. We employ the endpoint stiffness ellipsoid to analyze the required finger stiffness to fulfill the task. We evaluate the optimized stiffness settings in a door opening application with an iterative adaption of the stiffness behavior to handle the unknown force requirement. The results show a successful collective behavior of the fingers, where the stiffness setting considers a task-oriented force-adaptability trade-off and effective use of independent VSA fingers.

This paper deals with the problem of planning grasp strategies on constrained and cluttered scenarios. The planner sequences the objects for grasping by considering multiple factors: (i) possible environmental constraints that can be exploited to grasp an object, (ii) object neighborhood, (iii) capability of the arm, and (iv) confidence score of the vision algorithm. To successfully exploit the environmental constraints, this work uses the CLASH hand, a compliant hand that can vary its passive stiffness. The hand can be softened such that it can comply with the object shape, or it can be stiffened to pierce between the objects in clutter. A stiffness decision tree is introduced to choose the best stiffness setting for each particular scenario. In highly cluttered scenarios, a finger position planner is used to find a suitable orientation for the hand such that the fingers can slide in the free regions around the object. Thus, the grasp strategy planner predicts not only the sequence in which the objects can be grasped, but also the required stiffness of the end effector, and the appropriate positions for the fingers around the object. Different experiments are carried out in the context of grocery handling to test the performance of the planner in scenarios that require different grasping strategies.

Underactuated and synergy-driven hands are gaining attention in the grasping community mainly due to their simple kinematics, intrinsic compliance and versatility for grasping objects even in non structured scenarios. The evaluation of the grasping capabilities of such hands is a challenging task. This paper revisits some traditional quality measures developed for multi-fingered, fully actuated hands, and applies them to the case of underactuated hands. The extension of quality metrics for synergy-driven hands for the case of underactuated grasping is also presented. The performance of both types of measures is evaluated with simulated examples, concluding with a comparative discussion of their main features.