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Manipulation tasks often require a high degree of dexterity, typically necessitating grippers with multiple degrees of freedom (DOF). While a robotic hand equipped with multiple fingers can execute precise and intricate manipulation tasks, the inherent redundancy stemming from its high‐DOF often adds complexity that may not be required. In this paper, we introduce the design of a tactile sensor‐equipped gripper with two fingers and five‐DOF. We present a novel design integrating a GelSight tactile sensor, enhancing sensing capabilities and enabling finer control during specific manipulation tasks. To evaluate the gripper's performance, we conduct experiments involving three challenging tasks: 1) retrieving, singularizing, and classification of various objects buried within granular media, 2) executing scooping manipulations of a 3D‐printed credit card in confined environments to achieve precise insertion, and 3) sensing entangled cable states with only tactile perception and executing manipulations to achieve two‐cable untangling. Our results demonstrate the versatility of the proposed gripper across these tasks, with a high success rate of 84% for singulation task, a 100% success rate for scooping and inserting credit cards, and successful cable untangling. Videos are available athttps://yuhochau.github.io/5_dof_gripper/. 

Cameras are deployed at scale with the purpose of searching and tracking objects of interest (e.g., a suspected person) through the camera network on live videos. Such cross-camera analytics is data and compute intensive, whose costs grow with the number of cameras and time. We present Spatula, a cost-efficient system that enables scaling cross-camera analytics on edge compute boxes to large camera networks by leveraging the spatial and temporal cross-camera correlations. While such correlations have been used in computer vision community, Spatula uses them to drastically reduce the communication and computation costs by pruning search space of a query identity (e.g., ignoring frames not correlated with the query identity’s current position). Spatula provides the first system substrate on which cross-camera analytics applications can be built to efficiently harness the cross-camera correlations that are abundant in large camera deployments. Spatula reduces compute load by $$8.3\times$$ on an 8-camera dataset, and by $$23\times-86\times$$ on two datasets with hundreds of cameras (simulated from real vehicle/pedestrian traces). We have also implemented Spatula on a testbed of 5 AWS DeepLens cameras.