10 accepted papers
The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation tasks. An important line of research in this regard is grasp force control, which aims to manipulate objects safely by lim…
We present a holistic grasping controller, combining free-space position control and in-contact force-control for reliable grasping given uncertain object pose estimates. Employing tactile fingertip sensors, undesired object displacement during grasping is minimized by pausing the finger closing mot…
We present an approach based on conditional generative adversarial networks (GANs) to generate grasps directly and in a feed-forward manner from a raw depth image input. Building on the recently introduced StyleGAN architecture we extend results from an earlier proof-of-concept paper [1] and demonst…
We propose an autonomous grasping pipeline that relies on geometric information extracted from segmented point cloud data. This is in contrast to many recent approaches leveraging deep learning and thus relying on a rather large amount of training samples. We argue that the proposed geometric approa…
We present our second generation tactile sensor for the Shadow Dexterous Hand's palm. We were able to significantly improve the tactile sensor characteristics by utilizing our latest barometer-based tactile sensing technology with linear (R2 ≥ 0.9996) sensor output and no noticeable hysteresis. The…
A lot of motion planning research in robotics focuses on efficient means to find trajectories between individual start and goal regions, but it remains challenging to specify and plan robotic manipulation actions which consist of multiple interdependent subtasks. The Task Constructor framework we pr…
The usage of articulated tools for autonomous robots is still a challenging task. One of the difficulties is to automatically estimate the tool's kinematics model. This model cannot be obtained from a single passive observation, because some information, such as a rotation axis (hinge), can only be…
The dynamics of deformable objects, especially that of highly flexible articles of clothing, is difficult to model. This is due to their vast number of degrees of freedom in addition to the noisy and incomplete measurements robots have to cope with. Therefore, we suggest focusing on the structures a…
A necessary skill when using liquids in the preparation of food is to be able to estimate viscosity, e.g. in order to control the pouring velocity or to determine the thickness of a sauce. We introduce a method to allow a robotic kitchen assistant discriminate between different but visually similar…
Identifying the relevant functional degrees of freedom is a key prerequisite for the proper handling of everyday objects. Recognizing and exploiting these degrees of freedom in the context of non-rigid objects poses challenges that are significantly different from the rigid case. As a major generic…