Soft Fingertips with Tactile Sensing and Active Deformation for Robust Grasping of Delicate Objects

Soft fingertips have shown significant adaptability for grasping a wide range of object shapes thanks to elasticity. This ability can be enhanced to grasp soft, delicate objects by adding touch sensing. However, in these cases, the complete restraint and robustness of the grasps have proved to be challenging, as the exertion of additional forces on the fragile object can result in damage. This paper presents a novel soft fingertip design for delicate objects based on the concept of embedded air cavities, which allow the dual ability of adaptive sensing and active shape changing. The pressurized air cavities act as soft tactile sensors to control gripper position from internal pressure variation; and active fingertip deformation is achieved by applying positive pressure to these cavities, which then enable a delicate object to be kept securely in position, despite externally applied forces, by form closure. We demonstrate this improved grasping capability by comparing the displacement of grasped delicate objects exposed to high-speed motions. Results show that passive soft fingertips fail to restrain fragile objects at accelerations as low as 0.1m/s2 , in contrast, with the proposed fingertips, delicate objects are completely secure even at accelerations of more than 5m/s2

Innovative robot hand designs of reduced complexity for dexterous manipulation

This thesis investigates the mechanical design of robot hands to sensibly reduce the system complexity in terms of the number of actuators and sensors, and control needs for performing grasping and in-hand manipulations of unknown objects. Human hands are known to be the most complex, versatile, dexterous manipulators in nature, from being able to operate sophisticated surgery to carry out a wide variety of daily activity tasks (e.g. preparing food, changing cloths, playing instruments, to name some). However, the understanding of why human hands can perform such fascinating tasks still eludes complete comprehension. Since at least the end of the sixteenth century, scientists and engineers have tried to match the sensory and motor functions of the human hand. As a result, many contemporary humanoid and anthropomorphic robot hands have been developed to closely replicate the appearance and dexterity of human hands, in many cases using sophisticated designs that integrate multiple sensors and actuators---which make them prone to error and difficult to operate and control, particularly under uncertainty. In recent years, several simplification approaches and solutions have been proposed to develop more effective and reliable dexterous robot hands. These techniques, which have been based on using underactuated mechanical designs, kinematic synergies, or compliant materials, to name some, have opened up new ways to integrate hardware enhancements to facilitate grasping and dexterous manipulation control and improve reliability and robustness. Following this line of thought, this thesis studies four robot hand hardware aspects for enhancing grasping and manipulation, with a particular focus on dexterous in-hand manipulation. Namely: i) the use of passive soft fingertips; ii) the use of rigid and soft active surfaces in robot fingers; iii) the use of robot hand topologies to create particular in-hand manipulation trajectories; and iv) the decoupling of grasping and in-hand manipulation by introducing a reconfigurable palm. In summary, the findings from this thesis provide important notions for understanding the significance of mechanical and hardware elements in the performance and control of human manipulation. These findings show great potential in developing robust, easily programmable, and economically viable robot hands capable of performing dexterous manipulations under uncertainty, while exhibiting a valuable subset of functions of the human hand.Open Acces

Rapid manufacturing of color-based hemispherical soft tactile fingertips

Tactile sensing can provide access to information about the contact (i.e. slippage, surface feature, friction), which is out of reach of vision but crucial for manipulation. To access this information, a dense measurement of the deformation of soft fingertips is necessary. Recently, tactile sensors that rely on a camera looking at a deformable membrane have demonstrated that a dense measurement of the contact is possible. However, their manufacturing can be time-consuming and labor-intensive. Here, we show a new design method that uses multi-color additive manufacturing and silicone casting to efficiently manufacture soft marker-based tactile sensors that are able to capture with high-resolution the three-dimensional deformation field at the interface. Each marker is composed of two superimposed color filters. The subtractive color mixing encodes the normal deformation of the membrane, and the lateral deformation is found by centroid detection. With this manufacturing method, we can reach a density of 400 markers on a 21 mm radius hemisphere, allowing for regular and dense measurement of the deformation. We calibrated and validated the approach by finding the curvature of objects with a threefold increase in accuracy as compared to previous implementations. The results demonstrate a simple yet effective approach to manufacturing artificial fingertips for capturing a rich image of the tactile interaction at the location of contact

Pose-Based Tactile Servoing: Controlled Soft Touch using Deep Learning

This article describes a new way of controlling robots using soft tactile sensors: pose-based tactile servo (PBTS) control. The basic idea is to embed a tactile perception model for estimating the sensor pose within a servo control loop that is applied to local object features such as edges and surfaces. PBTS control is implemented with a soft curved optical tactile sensor (the BRL TacTip) using a convolutional neural network trained to be insensitive to shear. In consequence, robust and accurate controlled motion over various complex 3D objects is attained. First, we review tactile servoing and its relation to visual servoing, before formalising PBTS control. Then, we assess tactile servoing over a range of regular and irregular objects. Finally, we reflect on the relation to visual servo control and discuss how controlled soft touch gives a route towards human-like dexterity in robots.Comment: A summary video is available here https://youtu.be/12-DJeRcfn0 *NL and JL contributed equally to this wor