Abstract — As robots enter novel, uncertain home and office environments, they are able to navigate these environments successfully. However, to be practically deployed, robots should be able to manipulate their environment to gain access to new spaces, such as by opening a door and operating an elevator. This, however, remains a challenging problem because a robot will likely encounter doors (and elevators) it has never seen before. Objects such as door handles are very different in appearance, yet similar function implies similar form. These general, shared visual features can be extracted to provide a robot with the necessary information to manipulate the specific object and carry out a task. For example, opening a door requires the robot to identify the following properties: (a) location of the door handle axis of rotation, (b) size of the handle, and (c) type of handle (leftturn or right-turn). Given these keypoints, the robot can plan the sequence of control actions required to successfully open the door. We identify these “visual keypoints ” using vision-based learning algorithms. Our system assumes no prior knowledge of the 3D location or shape of the door handle. By experimentally verifying our algorithms on doors not seen in the training set, we advance our work towards being the first to enable a robot to navigate to more spaces in a new building by opening doors and elevators, even ones it has not seen before. I.

Abstract — Many tasks that would be of benefit to users in domestic environments require that robots manipulate articulated objects such as doors and drawers. In this paper, we present a novel approach that simultaneously estimates the kinematic model of an articulated object based on the trajectory described by the robot’s end effector, and uses this model to predict the future trajectory of the end effector. One advantage of our approach is that the robot can directly use these predictions to generate an equilibrium point control path for operating the mechanism. Additionally, our approach can improve these predictions based on previously learned articulation models. We have implemented and tested our approach on a real mobile manipulator. Through 40 trials, we show that the robot can reliably open various household objects, including cabinet doors, sliding doors, office drawers, and a dishwasher. Furthermore, we demonstrate that using the information from previous interactions as a prior significantly improves the prediction accuracy. I.

Abstract — We present a novel planning algorithm for the problem of placing objects on a cluttered surface such as a table, counter or floor. The planner (1) selects a placement for the target object and (2) constructs a sequence of manipulation actions that create space for the object. When no continuous space is large enough for direct placement, the planner leverages means-end analysis and dynamic simulation to find a sequence of linear pushes that clears the necessary space. Our heuristic for determining candidate placement poses for the target object is used to guide the manipulation search. We show successful results for our algorithm in simulation. I.

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Abstract — We propose a novel method for a robot to separate and segment objects in a cluttered tabletop environment. The method leverages the fact that external object boundaries produce visible edges within an object cluster. We achieve this singulation of objects by using the robot arm to perform pushing actions specifically selected to test whether particular visible edges correspond to object boundaries. We verify the separation of objects after a push by examining the clusters formed by geometric segmentation of regions residing on the table surface. To avoid explicitly representing and tracking edges across push behaviors we aggregate over all edges in a given orientation by representing the push-history as an orientation histogram. By tracking the history of directions pushed for each object cluster we can build evidence that a cluster cannot be further separated. We present quantitative and qualitative experimental results performed in a real home environment by a mobile manipulator using input from an RGB-D camera mounted on the robot’s head. We show that our pushing strategy can more reliably obtain singulation in fewer pushes than an approach, that does not explicitly reason about boundary information. I.