Mobile manipulator robots, which can move around and physically interact with their environments, can empower people with motor limitations to independently carry out many activities of daily living. While many interfaces have been developed for tele-operating complex robots, most of them are not accessible to people with severe motor limitations. Further, most interfaces are rigid with limited configurations and are not readily available to download and use. To address these barriers, we developed AccessTeleopKit: an open-source toolkit for creating custom and accessible robot tele-operation interfaces based on cursor-and-click input for the Stretch 3 mobile-manipulator. With AccessTeleopKit users can add, remove, and rearrange components such as buttons and camera views, and select between a variety of control modes. We describe the participatory and iterative design process that led to the current implementation of AccessTeleopKit, involving three long-term deployments of the robot in the home of a quadriplegic user. We demonstrate how AccessTeleopKit allowed the user to create different interfaces for different tasks and the diversity of tasks it allowed the user to carry out. We also present two studies involving six additional users with severe motor limitations, demonstrating the power of AccessTeleopKit in creating custom interfaces for different user needs and preferences.

Only a small percentage of blind and low-vision people use traditional mobility aids such as a cane or a guide dog. Various assistive technologies have been proposed to address the limitations of traditional mobility aids. These devices often give either the user or the device majority of the control. In this work, we explore how varying levels of control affect the users' sense of agency, trust in the device, confidence, and successful navigation. We present Glide, a novel mobility aid with two modes for control: Glide-directed and User-directed. We employ Glide in a study (N=9) in which blind or low-vision participants used both modes to navigate through an indoor environment. Overall, participants found that Glide was easy to use and learn. Most participants trusted Glide despite its current limitations, and their confidence and performance increased as they continued to use Glide. Users' control mode preferences varied in different situations; no single mode "won" in all situations.

Mobile manipulator platforms, like the Stretch RE1 robot, make the promise of in-home robotic assistance feasible. For people with severe physical limitations, like those with quadriplegia, the ability to tele-operate these robots themselves means that they can perform physical tasks they cannot otherwise do themselves, thereby increasing their level of independence. In order for users with physical limitations to operate these robots, their interfaces must be accessible and cater to the specific needs of all users. As physical limitations vary amongst users, it is difficult to make a single interface that will accommodate all users. Instead, such interfaces should be customizable to each individual user. In this paper we explore the value of customization of a browser-based interface for tele-operating the Stretch RE1 robot. More specifically, we evaluate the usability and effectiveness of a customized interface in comparison to the default interface configurations from prior work. We present a user study involving participants with motor impairments $(\mathrm{N}=10)$ and without motor impairments, who could serve as a caregiver, $(\mathrm{N}=13)$ that use the robot to perform mobile manipulation tasks in a real kitchen environment. Our study demonstrates that no single interface configuration satisfies all users’ needs and preferences. Users perform better when using the customized interface for navigation, but not for manipulation due to higher complexity of learning to manipulate through the robot. All participants are able to use the robot to complete all tasks, and participants with motor impairments believe that having the robot in their home would make them more independent.

In this work, we present an anytime planner for creating open-loop trajectories that solve rearrangement planning problems under uncertainty using nonprehensile manipulation. We first extend the Monte Carlo Tree Search algorithm to the unobservable domain. We then propose two default policies that allow us to quickly determine the potential to achieve the goal while accounting for the contact that is critical to rearrangement planning. The first policy uses a learned model generated from a set of user demonstrations. This model can be quickly queried for a sequence of actions that attempts to create contact with objects and achieve the goal. The second policy uses a heuristically guided planner in a subspace of the full state space. Using these goal informed policies, we are able to find initial solutions to the problem quickly, then continuously refine the solutions as time allows. We demonstrate our algorithm on a 7 degree-of-freedom manipulator moving objects on a table.