In recent years, there has been a proliferation of works on human action classification from
depth sequences. These works generally present methods and/or feature representations …

We present a comprehensive survey of robot Learning from Demonstration (LfD), a
technique that develops policies from example state to action mappings. We introduce the …

This paper provides a comprehensive computational account of hand-centered research,
which is principles, methodologies and practical issues behind human hands, robot hands …

While the grasping capability of robotic grippers has shown significant development, the
ability to manipulate objects within the hand is still limited. One explanation for this limitation …

Learning from demonstration provides an attractive solution to the problem of teaching
autonomous systems how to perform complex tasks. Reward learning from demonstration is …

Unconstrained human-hand motions that consist grasp motions and in-hand manipulations
lead to a fundamental challenge that many algorithms have to face in both theoretical and …

In order to have a robotic system able to effectively learn by imitation, and not merely
reproduce the movements of a human teacher, the system should have the capabilities of …

Fundamental to the successful, autonomous operation of mobile robots are robust motion
control algorithms. Motion control algorithms determine an appropriate action to take based …

This paper describes the development of a six-axis force/moment sensor with rectangular
taper beams for an intelligent robot's wrist and ankle. In order to accurately push and pull an …

The problem of learning a policy, a task representation mapping from world states to actions,
lies at the heart of many robotic applications. One approach to acquiring a task policy is …