We approach the problem of high-DOF reaching-and-grasping via learning joint planning of
grasp and motion with deep reinforcement learning. To resolve the sample efficiency issue …

Being able to detect and recognize human activities is important for making personal
assistant robots useful in performing assistive tasks. The challenge is to develop a system …

We consider the problem of grasping novel objects in cluttered environments. If a full 3-d
model of the scene were available, one could use the model to estimate the stability and …

In recent years, small-scale driving testbeds have been developed as controlled physical
environments for the evaluation of autonomous vehicle controllers. Such controllers are …

The real-time estimation through vision of the physical properties of objects manipulated by
humans is important to inform the control of robots for performing accurate and safe grasps …

Finding an occluded object in a lateral access environment such as a shelf or cabinet is a
problem that arises in many contexts such as warehouses, retail, healthcare, shipping, and …

A key challenge for haptically reaching in dense clutter is the frequent contact that can occur
between the robot's arm and the environment. We have previously used single-time-step …

Adapting to uncertain environments is a key obstacle in the development of robust robotic
object manipulation systems, as there is a trade-off between the computationally expensive …

Manipulation in contrast to grasping is a trajectorial task that needs to use dexterous hands.
Improving the dexterity of robot hands increases the controller complexity and thus requires …

Monitoring a fleet of robots requires stable long-term tracking with re-identification, which is
yet an unsolved challenge in many scenarios. One application of this is the analysis of …