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.

By manipulation of electric fields, it is possible to detect the presence of foreign objects underwater. The presented work builds upon a previous result, in which was developed a neural network-based methodology allowing to address this detection problem for spherical objects. Hereafter, we show that the approach generalizes to the case of continuous walls. The technique relies on a neural model of the forward map (from scene configuration to electric measures). Exploiting this model, together with collected electric measures, it becomes possible to detect and infer the relative distance and orientation of a planar wall. In addition, we show that relying on a single forward model, only descriptive of the presence of a single wall, it is possible to address the same problem in presence of a combination of walls forming a corner or a corridor. Closing the motion control loop with information obtained using the proposed approach, it becomes possible to regulate position of a system at a fixed distance and orientation from a wall, with applications to the exploration and monitoring of flooded pipelines, or to surface quality monitoring of ships' hulls (in relation to biofouling). Data collected experimentally are used together with analytical models and numerical simulations to illustrate efficacy of the approach.

Navigation of cluttered underwater environments remains to this day a challenging task in mobile robotics. Applying an electric field to a mobile robot's direct environment and measuring perturbations of this field, one is able to detect the presence of foreign objects in close proximity of the system. In addition, one is also able to infer a range of information relative to the detected objects, such as their position or electrical characteristics. Extracting such information from available measures typically requires a model (analytical, numerical or heuristic) descriptive of the relationship from geometry of the scene to measures performed (typically referred to as forward model), or of the inverse relationship (inverse model). In the following, we directly extract one such model from experimental data, and capture a forward model using a neural formalism. Then, using an iterative procedure, we are able to estimate the position of a detected object and assess the degree of confidence one can place on this estimate. Merit of the approach is illustrated using experimental data for a spherical object.

In the context of underwater robotics, positioning and coordination of mobile agents can prove a challenging problem. To address this issue, we propose the use of electric sensing, with a technique inspired by weakly electric fishes. In particular, the approach relies on one or several of the agents applying an electric field to their environment. Using electric measures, others agents are able to reconstruct their relative position with respect to the emitter, over a range that is function of the geometry of the emitting agent and of the power applied to the environment. Efficacy of the technique is illustrated using a number of numerical examples. The approach is shown to allow coordination of unmanned underwater vehicles, including that of bio-inspired swimming robotic platforms.