Grasping is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …

The object perception capabilities of humans are impressive, and this becomes even more
evident when trying to develop solutions with a similar proficiency in autonomous robots …

The usefulness and versatility of a robotic end-effector depends on the diversity of grasps it
can accomplish and also on the complexity of the control methods required to achieve them …

We propose a new large-scale database containing grasps that are applied to a large set of
objects from numerous categories. These grasps are generated in simulation and are …

This work described a bioinspired soft robotic finger with variable bending length to conform
objects with different sizes by means of selectively varying the structural stiffness of its …

We investigate the premise that robust grasping performance is enabled by exploiting
constraints present in the environment. These constraints, leveraged through motion in …

The ability to selectively stiffen otherwise compliant soft actuators increases their versatility
and dexterity. We investigate granular jamming and layer jamming as two possible methods …

We address the challenging problem of robotic grasping and manipulation in the presence
of uncertainty. This uncertainty is due to noisy sensing, inaccurate models, and hard-to …

Robotic food handling is a complicated task to perform when the ingredients that are
handled are delicate in nature. It is important to design end-effectors that can be versatile …

In the present work, we propose an active learning framework based on optimal query paths
to efficiently address the problem of tactile object shape exploration. Most previous …