A policy-blending formalism for shared control

By Anca Dragan, Siddhartha S. Srinivasa. 2013

429 citations recorded by the source corpus.

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In Reinforcement Learning

Citations in the corpus

LILA: Language-Informed Latent Actions (2021, methodology): “Shared autonomy casts robot control as a collaborative process between humans and robots [2, 4, 16, 22].”
Learning Robot Objectives from Physical Human Interaction (2017, background): “While IRL can learn the robot’s objective function after one or more demonstrations of a task, online inference is possible when the objective is simply to reach a goal state, and the robot moves through free space [12, 13, 14].”
Correcting Robot Plans with Natural Language Feedback (2022, background): “corrections from kinesthetic or joystick data to novel tasks requires inferring the user’s intent given few underspecified demonstrations, which is itself a challenging problem [14].”
Shared Autonomy with Learned Latent Actions (2020, background): “Similar to [8] and [16], let the robot provide assistance towards each discrete goal g ∈ G in proportion to the robot’s confidence in that goal1:”
A Review of Intent Detection, Arbitration, and Communication Aspects of Shared Control for Physical Human-Robot Interaction (2018, background): “[67] and Dragan and Srinivasa [66] combine aspects of co-activity with collaborative arbitration.”
Controlling Assistive Robots with Learned Latent Actions (2019, background): “Here the robot maintains a belief over possible goals, and increasingly assists the human as it becomes confident about their intended goal [7], [8].”
Learning with Humans in the Loop (2020, background): “Previous work in this area [30, 19, 48, 55, 9] has focused on some combination of inferring user intent and acting to achieve it.”
Learning latent actions to control assistive robots (2021, positive): “Shared autonomy approaches like [16,37,25] are not suitable within this setting because: i) the goals lie in continuous regions and ii) the user needs to control both the high-level goal that the robot reaches for and the trajectory the robot follows to reach that goal (e.”
Assistive teleoperation of robot arms via automatic time-optimal mode switching (2016, background): “In these situations, the system must simultaneously predict the user’s goals and assist them to complete the task [26], [27].”
Shared Autonomy via Deep Reinforcement Learning (2018, positive): “We build on shared autonomy work in which the system is initially unaware of the user’s goal [9, 7, 21, 13, 24, 16, 10] and explore problem statements with unknown dynamics, unknown user policy, and unknown goal representation.”
Shared Autonomy via Hindsight Optimization (2015, methodology): “Many of these methods can be thought of as an arbitration between the user’s policy and a fully autonomous policy for the most likely goal [7].”
Shared autonomy via hindsight optimization for teleoperation and teaming (2017, methodology): “Blending methods (Dragan and Srinivasa, 2013b) attempt to bridge the gap between highly assistive methods with little user control, and minimal assistance with higher user burden.”
Probabilistic Human Intent Recognition for Shared Autonomy in Assistive Robotics (2019, methodology): “1 (RBII-1 and RBII-2), for comparative purposes we also implement two approaches to intent inference utilized in previous shared autonomy works—amnesic inference [2, 9, 13] and memory-based inference [8, 9, 20, 30].”