Humans have the remarkable ability to pick up unfamiliar objects and quickly understand their surface properties, such as friction, and dynamics. This knowledge enables us not only to reorient objects using our arms but also to manipulate them within our hands, extending our capabilities beyond what is typically seen in traditional robotics. In this paper, we introduce a custom parallel gripper equipped with commercial 6-degree-of-freedom (DoF) force-torque (F/T) sensors and custom relative velocity sensors (see Figure 1), for in-hand slip-aware control that relies solely on in-hand sensing. The ability to independently measure force and planar velocity introduces new opportunities for intricate robotic manipulation. This hardware combination enables rapid estimation of friction and contact surface properties without the need for external sensors, thus facilitating for precise in-hand manipulation of objects in both rotational and translational movements. Slip-aware control significantly enhances the functionality of robotic manipulators by enabling the object-end-effector relative pose to adapt during grasping, thereby extending the operational workspace. This adaptability is particularly valuable in constrained environments, where the manipulator's movement is limited, or for intelligent human-robot interaction, enabling for instance more intuitive and safe handovers. Furthermore, in-hand slippage control opens up new opportunities for multi-arm manipulation of single objects, allowing for the repositioning of grasps without releasing the object, thereby enabling more efficient and flexible handling of larger items. Our system has been rigorously tested across a wide range of experiments, demonstrating its effectiveness and versatility.

To exploit their properties at a system level, we developed parallel image processing architectures for applications that rely mostly on the qualitative properties of the optical flow, rather than on the precise values of the velocity vectors. Specifically, we designed two parallel architectures that employ arrays of elementary motion sensors for the computation of heading direction and time-to-contact. The application domain that we took into consideration for the implementation of such architectures, is the promising one of vehicle navigation. Having defined the types of images to be analyzed and the types of processing to perform, we were able to use a priori infor- VLSI Architectures for Computation of Heading Direction and Time-to-contact 721 mation to integrate selectively the sparse data obtained from the velocity sensors and determine the qualitative properties of the optical flow field of interest.

To exploit their properties at a system level, we developed parallel image processing architectures for applications that rely mostly on the qualitative properties of the optical flow, rather than on the precise values of the velocity vectors. Specifically, we designed two parallel architectures that employ arrays of elementary motion sensors for the computation of heading direction and time-to-contact. The application domain that we took into consideration for the implementation of such architectures, is the promising one of vehicle navigation. Having defined the types of images to be analyzed and the types of processing to perform, we were able to use a priori infor- VLSI Architectures for Computation of Heading Direction and Time-to-contact 721 mation to integrate selectively the sparse data obtained from the velocity sensors and determine the qualitative properties of the optical flow field of interest.

To exploit their properties at a system level, we developed parallel image processing architectures forapplications that rely mostly on the qualitative properties of the optical flow, rather than on the precise values of the velocity vectors. Specifically, we designed twoparallel architectures that employ arrays of elementary motion sensors for the computation of heading direction and time-to-contact. The application domain thatwe took into consideration for the implementation of such architectures, is the promising one of vehicle navigation. Having defined the types of images to be analyzed and the types of processing to perform, we were able to use a priori infor- VLSI Architectures for Computation of Heading Direction and Time-to-contact 721 mation to integrate selectively the sparse data obtained from the velocity sensors and determine the qualitative properties of the optical flow field of interest.