5 accepted papers
We introduce a Learning from Demonstration (LID) approach for contact-rich manipulation tasks, i.e., tasks in which the manipulandum's motion is constrained by contact with the environment. Our approach is motivated by the insight that even a large number of demonstrations will often not contain suf…
We present a robust method to visually segment scenes into objects based on motion and appearance. Both these cues provide complementary information that we fuse using two interconnected recursive estimators: One estimates object segmentation from motion as a probabilistic clustering of tracked 3D p…
Robots need to understand articulated objects, such as drawers. The state of articulated structures is commonly estimated using vision, but visual perception is limited when objects are occluded, have few salient features, or are not in the camera's field of view. Audio sensing does not face these c…
Robotic manipulation behavior should be robust to disturbances that violate high-level task-structure. Such robustness can be achieved by constantly monitoring the environment to observe the discrete high-level state of the task. This is possible because different phases of a task are characterized…
The most prominent criterion for learning of manipulation skills is the optimization of task success, modeled as expected reward or probability of success. This is sensible if we only want to optimize a single controller. But if learned manipulation primitives are used as modules in a larger system,…