The use of machine learning to investigate grasp affordances has received extensive attention over the past several decades. The existing literature provides a robust basis to build upon, though a number of aspects may be improved. Results commonly work in terms of grasp configuration, with little consideration for the manner in which the grasp may be (re-)produced, from a reachability and trajectory planning perspective. We propose a different perspective on grasp affordance learning, explicitly accounting for grasp synthesis; that is, the manner in which manipulator kinematics are used to allow materialization of grasps. The approach allows to explicitly map the grasp policy space in terms of generated grasp types and associated grasp quality. Results of application to a range of objects illustrate merit of the method and highlight the manner in which it may promote a greater degree of explainability for otherwise intransparent reinforcement processes.

This paper describes the hardware implementation and characterization of a capacitive sensor designed to support detection and localization of nearby objects. The sensor can be mounted on the exterior of any given robotic system. The technology is particularly well-suited to detection of capacitive material, such as living tissue. As such, it offers perspectives of facilitating human-robot interactions (cobotics). We exploit experimental data to implement a digital model of the sensor and illustrate its accuracy by emulating experimental results in simulation. The sensor is used in a number of interaction scenarii (following and avoidance), providing examples of the manner in which it can be used to support human-robot interactions.

We propose a new approach to investigate and quantify dynamic grasp performance. Oftentimes, existing approaches to grasp analysis assess a grasp’s quality in a static situation. We build upon such considerations to also account for the dynamic nature of most grasp operations. In particular, these typically do not, in practice, occur in a static setting. Robotic grasping is indeed commonly involved in, for instance, pick-and-place operations which involve movement and thus a dynamic aspect. We investigate grasp quality over such movements, affording consideration not only to the gripper’s and grasp configuration, but also to their trajectory. More specifically, we explore the relationship from the gripper’s base acceleration to the stability of the grasped object (assessed using the relative acceleration of the object with respect to that of the gripper), using linear approximations of the corresponding dynamics. From such relations, we construct a grasp’s robustness metric, which accounts for the movements involved in the considered scenario. Numerical simulations are used to compare achieved results with those obtained using alternate existing methods. We illustrate merit of the proposed metric by exploring robustness of a given grasp under different trajectories.