47,353 research outputs found
Online Update of Safety Assurances Using Confidence-Based Predictions
Robots such as autonomous vehicles and assistive manipulators are
increasingly operating in dynamic environments and close physical proximity to
people. In such scenarios, the robot can leverage a human motion predictor to
predict their future states and plan safe and efficient trajectories. However,
no model is ever perfect -- when the observed human behavior deviates from the
model predictions, the robot might plan unsafe maneuvers. Recent works have
explored maintaining a confidence parameter in the human model to overcome this
challenge, wherein the predicted human actions are tempered online based on the
likelihood of the observed human action under the prediction model. This has
opened up a new research challenge, i.e., \textit{how to compute the future
human states online as the confidence parameter changes?} In this work, we
propose a Hamilton-Jacobi (HJ) reachability-based approach to overcome this
challenge. Treating the confidence parameter as a virtual state in the system,
we compute a parameter-conditioned forward reachable tube (FRT) that provides
the future human states as a function of the confidence parameter. Online, as
the confidence parameter changes, we can simply query the corresponding FRT,
and use it to update the robot plan. Computing parameter-conditioned FRT
corresponds to an (offline) high-dimensional reachability problem, which we
solve by leveraging recent advances in data-driven reachability analysis.
Overall, our framework enables online maintenance and updates of safety
assurances in human-robot interaction scenarios, even when the human prediction
model is incorrect. We demonstrate our approach in several safety-critical
autonomous driving scenarios, involving a state-of-the-art deep learning-based
prediction model.Comment: 7 pages, 3 figure
Healthcare Robotics
Robots have the potential to be a game changer in healthcare: improving
health and well-being, filling care gaps, supporting care givers, and aiding
health care workers. However, before robots are able to be widely deployed, it
is crucial that both the research and industrial communities work together to
establish a strong evidence-base for healthcare robotics, and surmount likely
adoption barriers. This article presents a broad contextualization of robots in
healthcare by identifying key stakeholders, care settings, and tasks; reviewing
recent advances in healthcare robotics; and outlining major challenges and
opportunities to their adoption.Comment: 8 pages, Communications of the ACM, 201
Design and Implementation of a Modular Human-Robot Interaction Framework
With the increasing longevity that accompanies advances in medical technology comes a host of other age-related disabilities. Among these are neuro-degenerative diseases such as Alzheimer\u27s disease, Parkinson\u27s disease, and stroke, which significantly reduce the motor and cognitive ability of affected individuals. As these diseases become more prevalent, there is a need for further research and innovation in the field of motor rehabilitation therapy to accommodate these individuals in a cost-effective manner. In recent years, the implementation of social agents has been proposed to alleviate the burden on in-home human caregivers. Socially assistive robotics (SAR) is a new subfield of research derived from human-robot interaction that aims to provide hands-off interventions for patients with an emphasis on social rather than physical interaction. As these SAR systems are very new within the medical field, there is no standardized approach to developing such systems for different populations and therapeutic outcomes. The primary aim of this project is to provide a standardized method for developing such systems by introducing a modular human-robot interaction software framework upon which future implementations can be built.
The framework is modular in nature, allowing for a variety of hardware and software additions and modifications, and is designed to provide a task-oriented training structure with augmented feedback given to the user in a closed-loop format. The framework utilizes the ROS (Robot Operating System) middleware suite which supports multiple hardware interfaces and runs primarily on Linux operating systems. These design requirements are validated through testing and analysis of two unique implementations of the framework: a keyboard input reaction task and a reaching-to-grasp task. These implementations serve as example use cases for the framework and provide a template for future designs. This framework will provide a means to streamline the development of future SAR systems for research and rehabilitation therapy
Learning Dynamic Robot-to-Human Object Handover from Human Feedback
Object handover is a basic, but essential capability for robots interacting
with humans in many applications, e.g., caring for the elderly and assisting
workers in manufacturing workshops. It appears deceptively simple, as humans
perform object handover almost flawlessly. The success of humans, however,
belies the complexity of object handover as collaborative physical interaction
between two agents with limited communication. This paper presents a learning
algorithm for dynamic object handover, for example, when a robot hands over
water bottles to marathon runners passing by the water station. We formulate
the problem as contextual policy search, in which the robot learns object
handover by interacting with the human. A key challenge here is to learn the
latent reward of the handover task under noisy human feedback. Preliminary
experiments show that the robot learns to hand over a water bottle naturally
and that it adapts to the dynamics of human motion. One challenge for the
future is to combine the model-free learning algorithm with a model-based
planning approach and enable the robot to adapt over human preferences and
object characteristics, such as shape, weight, and surface texture.Comment: Appears in the Proceedings of the International Symposium on Robotics
Research (ISRR) 201
Challenging the Computational Metaphor: Implications for How We Think
This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor--a sequence of steps--with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think
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