6 accepted papers
High resolution tactile sensors are often bulky and have shape profiles that make them awkward for use in manipulation. This becomes important when using such sensors as fingertips for dexterous multi-fingered hands, where boxy or planar fingertips limit the available set of smooth manipulation stra…
Several robot manipulation tasks are extremely sensitive to variations of the physical properties of the manipulated objects. One such task is manipulating objects by using gravity or arm accelerations, increasing the importance of mass, center of mass, and friction information. We present SwingBot,…
Movement Primitives are a well studied and widely applied concept in modern robotics. Composing primitives out of an existing library, however, has shown to be a challenging problem. We propose the use of probabilistic context-free grammars to sequence a series of primitives to generate complex robo…
State abstraction in a discrete reinforcement learning setting clusters states sharing a similar optimal action to yield an easier to solve decision process. In this paper, we generalize the concept of state abstraction to continuous action reinforcement learning by defining an abstract state as a s…
Accurate object shape knowledge provides important information for performing stable grasping and dexterous manipulation. When modeling an object using tactile sensors, touching the object surface at a fixed grid of points can be sample inefficient. In this paper, we present an active touch strategy…
During grasping and other in-hand manipulation tasks maintaining a stable grip on the object is crucial for the task's outcome. Inherently connected to grip stability is the concept of slip. Slip occurs when the contact between the fingertip and the object is partially lost, resulting in sudden unde…