Millions of people cannot eat independently due to a disability, and caregiver-assisted meals can make them feel self-conscious, pressured, or burdensome. Robot-assisted feeding promises to empower people with motor impairments to feed themselves. However, current research typically examines specific robotic system subcomponents and evaluates them in controlled lab settings. This leaves a gap in developing and evaluating an end-to-end system that can feed entire meals in out-of-lab settings. We present one such system, which we developed collaboratively with two community researchers (CRs) with motor-impairments. The key challenge of developing a robot feeding system for out-of-lab use is the varied off-nominal scenarios that inevitably arise. Our key insight is that users can overcome many off-nominals, provided customizability and control over the system. Our system improves upon the state-of-the-art with: (1) a user interface that provides substantial user customizability and control, (2) a bite selection implementation that incorporates users-in-the-loop to generalize across food items, and (3) portable hardware that facilitates system use in diverse environments without inhibiting user mobility. We conduct two studies to evaluate the system. In Study 1, five users with motor impairments and one CR use the system to feed themselves meals of their choice in a cafeteria, office, or conference room. In Study 2, one CR uses the system in his home for five days, feeding himself 10 meals across diverse contexts. We present 3 key lesson learned: (1) spatial contexts are numerous, customizability lets users adapt to them; (2) off-nominals will arise, variable autonomy lets users overcome them; and (3) assistive robots' benefits depend on context. We provide video footage and code on our website.

Autonomous assistive feeding systems need to acquire deformable food items of varying physical characteristics to be able to feed users. However, bite acquisition of these deformable food items is challenging without force feedback of appropriate range and sensitivity. We developed custom solutions using two widely-used shear sensing fingertip tactile sensors and calibrated them to the range of forces needed for manipulating food items. We compared their performance with traditional force/torque sensors and showed the trade-off between the range and the sensitivity of the fingertip tactile sensors in detecting potential bite acquisition successes for food items with widely varying weights and compliance. We then developed a control policy, using which a robotic gripper equipped with the fingertip tactile sensors can autonomously regulate the sensing range and the sensitivity to be able to skewer food items of different compliance and detect their bite acquisition success attempts.

Unmanned aerial vehicles (UAVs) are now very popular for many applications such as surveillance and transport and they are typically constructed starting from the design process. However, it is very time consuming and require all new airworthiness process. In this paper, we aim to provide a novel framework to automate an existing aircraft for unmanned operations using a humanoid robot, which is capable of manipulating the yoke, levers, switches and pedals to fly the airplane while operating various components such as lights and landing gears just as a human pilot would do. In this manner, the airplane can be converted for automated mode in a very short time without losing airworthiness. For testing, due to the complication of operating the robot in a real airplane, it is validated using a flight simulator, which provides the flight data over the network. The simulator is installed on a motion platform, which makes the robot move around due to the airplane's motion for realism. The proposed method is successfully validated in a series of flight simulations with various scenarios to show the feasibility of humanoid robot operation of an aircraft under various conditions.