While there has been tremendous progress in autonomous robot manipulation, environments with clutter and unknown objects remain challenging particularly for the perception algorithms that support manipulation. This paper adopts a human-aided perception paradigm and investigates alternative interactive segmentation methods to allow users to segment a target object or object part. Through a first user study (N=24) we compare four interactive segmentation methods and characterize the tradeoff between efficiency and accuracy. Next we develop a hybrid segmentation interface and integrate it into an end-to-end human-in-the-loop manipulation system. In a second user study (N=12) we compare the performance of this system to a direct gripper-control system that allows similar manipulation tasks to be performed in challenging scenes. We find that this system enables more efficient manipulation with a lower mental load on the user, while offering a similar task success rate.

Though teleoperated robots have become common for more extreme tasks such as bomb diffusion, search-and-rescue, and space exploration, they are not commonly used in human-populated environments for more ordinary tasks such as house cleaning or cooking. This presents near-term opportunities for teleoperated robots in the home. However, a teleoperator's remote presence in a consumer's home presents serious security and privacy risks, and the concerns of end-users about these risks may hinder the adoption of such in-home robots. In this paper, we define and explore the privacy-utility tradeoff for remotely teleoperated robots: as we reduce the quantity or fidelity of visual information received by the teleoperator to preserve the end-user's privacy, we must balance this against the teleoperator's need for sufficient information to successfully carry out tasks. We explore this tradeoff with two surveys that provide a framework for understanding the privacy attitudes of end-users, and with a user study that empirically examines the effect of different filters of visual information on the ability of a teleoperator to carry out a task. Our findings include that respondents do desire privacy protective measures from teleoperators, that respondents prefer certain visual filters from a privacy perspective, and that, for the studied task, we can identify a filter that balances privacy with utility. We make recommendations for in-home teleoperation based on these findings.

We introduce an approach for analyzing Wikipedia and other text, together with online photos, to produce annotated 3D models of famous tourist sites. The approach is completely automated, and leverages online text and photo co-occurrences via Google Image Search. It enables a number of new interactions, which we demonstrate in a new 3D visualization tool. Text can be selected to move the camera to the corresponding objects, 3D bounding boxes provide anchors back to the text describing them, and the overall narrative of the text provides a temporal guide for automatically flying through the scene to visualize the world as you read about it. We show compelling results on several major tourist sites.