Wheelchair-mounted robotic arms (and other assistive robots) should help their users perform everyday tasks. One way robots can provide this assistance is shared autonomy. Within shared autonomy, both the human and robot maintain control over the robot's motion: as the robot becomes confident it understands what the human wants, it increasingly intervenes to automate the task. But how does the robot know what tasks the human may want to perform in the first place? Today's shared autonomy approaches often rely on prior knowledge: for example, the robot must know the set of possible human goals a priori. In the long-term, however, this prior knowledge will inevitably break down -- sooner or later the human will reach for a goal that the robot did not expect. In this paper we propose a learning approach to shared autonomy that takes advantage of repeated interactions. Learning to assist humans would be impossible if they performed completely different tasks at every interaction: but our insight is that users living with physical disabilities repeat important tasks on a daily basis (e.g., opening the fridge, making coffee, and having dinner). We introduce an algorithm that exploits these repeated interactions to recognize the human's task, replicate similar demonstrations, and return control when unsure. As the human repeatedly works with this robot, our approach continually learns to assist tasks that were never specified beforehand: these tasks include both discrete goals (e.g., reaching a cup) and continuous skills (e.g., opening a drawer). Across simulations and an in-person user study, we demonstrate that robots leveraging our approach match existing shared autonomy methods for known goals, and outperform imitation learning baselines on new tasks. See videos here: https://youtu.be/Plh4t3wQeIA
We discuss some of the challenges facing shared autonomy. In particular, we explore 1) shared autonomy over unreliable networks, 2) how we can model individual human operators (in contrast to the average of a human operator), and 3) how our approach naturally models and integrates sliding autonomy into the joint human-machine system. We include a Background Section for completeness.
Many tasks, particularly those involving interaction with the environment, are characterized by high variability, making robotic autonomy difficult. One flexible solution is to introduce the input of a human with superior experience and cognitive abilities as part of a shared autonomy policy. However, current methods for shared autonomy are not designed to address the wide range of necessary corrections (e.g., positions, forces, execution rate, etc.) that the user may need to provide to address task variability. In this paper, we present corrective shared autonomy, where users provide correcti…
bits-pilani（皮拉尼比特斯学院（注：此处为常见简称，完整应为Birla Institute of Technology and Science, Pilani，中文通常译为“比特拉理工学院”或简称“皮拉尼BITS”，但根据原文直接提取“bits-pilani”））
In this paper, we aim to achieve a human-robot work balance by implementing shared autonomy through a web interface. Shared autonomy integrates user input with the autonomous capabilities of the robot and therefore increases the overall performance of the robot. Presenting only the relevant information to the user on the web page lowers the cognitive load of the operator. Through our web interface, we provide a mechanism for the operator to directly interact using the displayed information by applying a point-and-click paradigm. Further, we present our idea to employ a human-robot mutual adapt…
Corrective Shared Autonomy is a method where human corrections are layered on top of an otherwise autonomous robot behavior. Specifically, a Corrective Shared Autonomy system leverages an external controller to allow corrections across a range of task variables (e.g., spinning speed of a tool, applied force, path) to address the specific needs of a task. However, this inherent flexibility makes the choice of what corrections to allow at any given instant difficult to determine. This choice of corrections includes determining appropriate robot state variables, scaling for these variables, and a…
University of Stuttgart（斯图加特大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
In this paper we present a framework for the teleoperation of pick-and-place tasks. We define a shared control policy that allows to blend between direct user control and autonomous control based on user intent inference. One of the main challenges in shared autonomy systems is to define the arbitration function, which decides when to let the autonomous agent take over. In this work, we propose a model and training method to learn the arbitration function. Our model is based on a recurrent neural network that takes as input the state, intent prediction scores and user command to produce an arb…
Toyota Technological Institute at Chicago（丰田工业大学芝加哥分校）
Shared autonomy provides an effective framework for human-robot collaboration that takes advantage of the complementary strengths of humans and robots to achieve common goals. Many existing approaches to shared autonomy make restrictive assumptions that the goal space, environment dynamics, or human policy are known a priori, or are limited to discrete action spaces, preventing those methods from scaling to complicated real world environments. We propose a model-free, residual policy learning algorithm for shared autonomy that alleviates the need for these assumptions. Our agents are trained t…
Trust-Preserved Human-Robot Shared Autonomy enabled by Bayesian Relational Event Modeling
School of Electrical and Computer Engineering, Georgia Institute of Technology（电子与计算机工程学院，佐治亚理工学院）；Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology（电子与计算机工程系，香港科技大学）
Assistive robots enable people with disabilities to conduct everyday tasks on their own. However, these tasks can be complex, containing both coarse reaching motions and fine-grained manipulation. For example, when eating, not only does one need to move to the correct food item, but they must also precisely manipulate the food in different ways (e.g., cutting, stabbing, scooping). Shared autonomy methods make robot teleoperation safer and more precise by arbitrating user inputs with robot controls. However, these works have focused mainly on the high-level task of reaching a goal from a discre…
Carnegie Mellon University（卡内基梅隆大学）；National Research Council of Italy（意大利国家研究委员会）
In shared autonomy, a user and autonomous system work together to achieve shared goals. To collaborate effectively, the autonomous system must know the user's goal. As such, most prior works follow a predict-then-act model, first predicting the user's goal with high confidence, then assisting given that goal. Unfortunately, confidently predicting the user's goal may not be possible until they have nearly achieved it, causing predict-then-act methods to provide little assistance. However, the system can often provide useful assistance even when confidence for any single goal is low (e.g. move t…
The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applications. Exploring diverse scenarios of humans and robots interacting in simulation can improve understanding of the robotic system and avoid potentially costly failures in real-world settings. We formulate this problem as a quality diversity (QD) problem, where the goal is to discover diverse failure scenarios by simultaneously exploring both environments and human actions. We focus on the shared autonomy domain, whe…
Carnegie Mellon University（卡内基梅隆大学）；National University of Singapore（新加坡国立大学）
Shared autonomy integrates user input with robot autonomy in order to control a robot and help the user to complete a task. Our work aims to improve the performance of such a human-robot team: the robot tries to guide the human towards an effective strategy, sometimes against the human's own preference, while still retaining his trust. We achieve this through a principled human-robot mutual adaptation formalism. We integrate a bounded-memory adaptation model of the human into a partially observable stochastic decision model, which enables the robot to adapt to an adaptable human. When the huma…
Paul G. Allen School of Computer Science and Engineering, University of Washington（保罗·G·艾伦计算机科学与工程学院，华盛顿大学）
Robotic agents that share autonomy with a human should leverage human domain knowledge and account for their preferences when completing a task. This extra knowledge can dramatically improve plan efficiency and user-satisfaction, but these gains are lost if communicating with a robot is taxing and unnatural. In this paper, we show how viewing humanrobot language through the lens of shared autonomy explains the efficiency versus cognitive load trade-offs humans make when deciding how cooperative and explicit to make their instructions.
Shared autonomy teleoperation can guarantee safety, but does so by reducing the human operator's control authority, which can lead to reduced levels of human-robot agreement and user satisfaction. This paper presents a novel haptic shared autonomy teleoperation paradigm that uses haptic feedback to inform the user about the inner state of a shared autonomy paradigm, while still guaranteeing safety. This differs from haptic shared control, which uses haptic feedback to inform the user's actions, but gives the human operator full control over the robot's actions. We conducted a user study in whi…
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；National Institute of Standards and Technology（国家标准技术研究院）；Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Sciences, University of Pittsburgh（匹兹堡大学健康与康复科学学院康复科学与技术系）
When humans control robot arms these robots often need to infer the human's desired task. Prior research on assistive teleoperation and shared autonomy explores how robots can determine the desired task based on the human's joystick inputs. In order to perform this inference the robot relies on an internal mapping between joystick inputs and discrete tasks: e.g., pressing the joystick left indicates that the human wants a plate, while pressing the joystick right indicates a cup. This approach works well after the human understands how the robot interprets their inputs -- but inexperienced user…
Shared autonomy enables robots to infer user intent and assist in accomplishing it. But when the user wants to do a new task that the robot does not know about, shared autonomy will hinder their performance by attempting to assist them with something that is not their intent. Our key idea is that the robot can detect when its repertoire of intents is insufficient to explain the user's input, and give them back control. This then enables the robot to observe unhindered task execution, learn the new intent behind it, and add it to this repertoire. We demonstrate with both a case study and a user…
Legible Shared Autonomy: Implicit Communication of Robot Belief through Motion
Department of Automation, University of Science and Technology of China（自动化系，中国科学技术大学）；Department of Automation, Hefei University of Technology（自动化系，合肥工业大学）；National Key Laboratory of Autonomous Intelligent Unmanned Systems（自主智能无人系统国家重点实验室）
Human-Centered Shared Autonomy for Motor Planning, Learning, and Control Applications
Translational Neurorobotics Laboratory（转化神经机器人实验室）；Department of Electrical, Computer, and Biomedical Engineering（电气、计算机与生物医学工程系）；University of Rhode Island（罗德岛大学）；Human-Centered AI (HCAI) Labs（以人为本的人工智能实验室）
Northwestern University（西北大学）；Shirley Ryan AbilityLab（雪莉·瑞安能力实验室）；DePaul University（德保罗大学）
A dynamic autonomy allocation framework automatically shifts how much control lies with the human versus the robotics autonomy, for example based on factors such as environmental safety or user preference. To investigate the question of which factors should drive dynamic autonomy allocation, we perform a human subject study to collect ground truth data that shifts between levels of autonomy during shared-control robot operation. Information streams from the human, the interaction between the human and the robot, and the environment are analyzed. Machine learning methods -- both classical and d…
Shared autonomy provides a framework where a human and an automated system, such as a robot, jointly control the system's behavior, enabling an effective solution for various applications, including human-robot interaction. However, a challenging problem in shared autonomy is safety because the human input may be unknown and unpredictable, which affects the robot's safety constraints. If the human input is a force applied through physical contact with the robot, it also alters the robot's behavior to maintain safety. We address the safety issue of shared autonomy in real-time applications by p…
From Novice to Skilled: RL-based Shared Autonomy Communicating with Pilots in UAV Multi-Task Missions
Institute for Clarity in Documentation（文献清晰度研究所）；The Thørväld Group（索瓦尔德集团）；Inria Paris-Rocquencourt（法国国家信息与自动化研究所巴黎-罗康校区）；Rajiv Gandhi University（拉吉夫·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕尔默研究实验室）；The Kumquat Consortium（金桔联盟）
Mobile telepresence robots (MTRs) have become increasingly popular in the expanding world of remote work, providing new avenues for people to actively participate in activities at a distance. However, humans operating MTRs often have difficulty navigating in densely populated environments due to limited situation awareness and narrow field-of-view, which reduces user acceptance and satisfaction. Shared autonomy in navigation has been studied primarily in static environments or in situations where only one pedestrian interacts with the robot. We present a multimodal shared autonomy approach, le…
Istituto Italiano di Tecnologia（意大利理工学院）；Universita di Pisa（比萨大学）
Teleoperation is a widely adopted strategy to control robotic manipulators executing complex tasks that require highly dexterous movements and critical high-level intelligence. Classical teleoperation schemes are based on either joystick control, or on more intuitive interfaces which map directly the user arm motions into one robot arm's motions. These approaches have limits when the execution of a given task requires reconfigurable multiple robotic arm systems. Indeed, the simultaneous teleoperation of two or more robot arms could extend the workspace of the manipulation cell, or increase its…
