Suction cups are an important gripper type in industrial robot applications, and the prior literature focuses on using vision-based planners to improve grasping success in these tasks. Vision-based planners can fail due to adversarial objects or lose generalizability for unseen scenarios, without retraining learned algorithms. In this article, we propose haptic exploration to improve suction cup grasping when visual grasp planners fail. We present the smart suction cup, an end effector that utilizes internal flow measurements for tactile sensing. We show that model-based haptic search methods, guided by these flow measurements, improve grasping success by up to 2.5× as compared with using only a vision planner during a bin-picking task. In characterizing the smart suction cup on both geometric edges and curves, we find that flow rate can accurately predict the ideal motion direction even with large postural errors. The smart suction cup includes no electronics on the cup itself, such that the design is easy to fabricate and haptic exploration does not damage the sensor. This work motivates the use of suction cups with autonomous haptic search capabilities in especially adversarial scenarios.

We present a novel robot end-effector for gripping and haptic exploration. Tactile sensing through suction flow monitoring is achieved with a new suction cup design that contains multiple chambers for air flow. Each chamber connects with its own remote pressure transducer, which enables both absolute and differential pressure measures between chambers. By changing the overall vacuum applied to this smart suction cup, it can perform different functions such as gentle haptic exploration (low pressure) and monitoring breaks in the seal during strong astrictive gripping (high pressure). Haptic exploration of surfaces through sliding and palpation can guide the selection of suction grasp locations and help to identify the local surface geometry. During suction gripping, a trained LSTM network can localize breaks in the suction seal between four quadrants with up to 97% accuracy and detects breaks in the suction seal early enough to avoid total grasp failure.

For small legged robots, ground contact interactions significantly affect the dynamics and locomotion performance. In this article, we designed thin, robust capacitive tactile sensors and applied them to the feet of a small hexapod with C-shaped rotating legs. The sensors measure contact forces as the robot traverses different types of terrain including hard surfaces with high or low friction, sand, and grass. Different gaits perform best on different types of terrain. Useful measured parameters include the magnitude and timing of the peak normal forces, in combination with the leg rotational velocity. The measured parameters were used in a support vector machine classifier to identify terrain types with 82.5% accuracy. Based on gait performance studies, we implemented a terrain-based gait control using real-time terrain classifications. A surface transitioning test shows 17.1% increase in body speed and 13.2% improvement in efficiency as the robot adjusts its gait.