Task-centric selection of robot and environment initial configurations for assistive tasks

Kapusta, Ariel; Park, Daehyung; Kemp, Charles C.

doi:10.1109/iros.2015.7353563

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…We present a task-centric, optimization-based method to select one or more configurations for assistive robots, that we call task-centric optimization of robot configurations (TOC). TOC extends two previous works from Kapusta et al (2015); Fig. 2: TOC can select a single configuration for the PR2 to shave a person in a wheelchair, shown here.…”

Section: Introductionmentioning

confidence: 68%

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Task-centric optimization of configurations for assistive robots

Kapusta

Kemp

2019

Auton Robot

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Robots can provide assistance to a human by moving objects to locations around the person's body. With a well chosen initial configuration, a robot can better reach locations important to an assistive task despite model error, pose uncertainty and other sources of variation. However, finding effective configurations can be challenging due to complex geometry, a large number of degrees of freedom, task complexity and other factors. We present task-centric optimization of robot configurations (TOC), which is an algorithm that finds configurations from which the robot can better reach task-relevant locations and handle task variation. Notably, TOC can return more than one configuration that when used sequentially enable a simulated assistive robot to reach more task-relevant locations. TOC performs substantial offline computation to generate a function that can be applied rapidly online to select robot configurations based on current observations. TOC explicitly models the task, environment, and user, and implicitly handles error using representations of robot dexterity. We evaluated TOC in simulation with a PR2 assisting a user with 9 assistive tasks in both a wheelchair and a robotic bed. TOC had an overall average success rate of 90.6% compared to 50.4%, 43.5%, and 58.9% for threeWe thank Henry and Jane Evans for providing input on assistive tasks and actuated beds. We also thank Daehyung Park and Zackory Erickson for their valuable feedback. Fig. 1: TOC can select a configuration for the PR2 and the robotic bed so the PR2 can better reach task-relevant locations. This figure shows the configuration for the task of a PR2 cleaning the legs of a person in a robotic bed. baseline methods from literature. We additionally demonstrate how TOC can find configurations for more than one robot and can be used to assist in designing or optimizing environments.

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Section: Introductionmentioning

confidence: 68%

“…Task-centric Manipulability (TC-manipulability), P M , is related to the average kinematic dexterity of the arm when reaching the goal poses. It is defined here differently from our previous works, such as from Kapusta et al (2015).…”

Section: Task-centric Manipulabilitymentioning

confidence: 99%

Task-centric optimization of configurations for assistive robots

Kapusta

Kemp

2019

Auton Robot

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“…In feeding, the fixed-base robot often requires the relocation of the robot or user by caregivers in the beginning or during the task. A fixed base restricts the scope of assistive tasks [40]. Without mobility, robots are restricted to a narrow set of tasks and are unable to leave the immediate vicinity of the human to provide assistance elsewhere.…”

Section: Assistive Manipulatorsmentioning

confidence: 99%

“…A fixed base restricts the scope of assistive tasks [40]. Without mobility, robots are restricted to a narrow set of tasks and are unable to leave the immediate vicinity of the human to provide assistance elsewhere.…”

Section: Assistive Manipulatorsmentioning

confidence: 99%

Active robot-assisted feeding with a general-purpose mobile manipulator: Design, evaluation, and lessons learned

Park

Hoshi

Mahajan

et al. 2020

Robotics and Autonomous Systems

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Eating is an essential activity of daily living (ADL) for staying healthy and living at home independently. Although numerous assistive devices have been introduced, many people with disabilities are still restricted from independent eating due to the devices' physical or perceptual limitations. In this work, we present a new meal-assistance system and evaluations of this system with people with motor impairments. We also discuss learned lessons and design insights based on the evaluations. The meal-assistance system uses a general-purpose mobile manipulator, a Willow Garage PR2, which has the potential to serve as a versatile form of assistive technology. Our active feeding framework enables the robot to autonomously deliver food to the user's mouth, reducing the need for head movement by the user. The user interface, visually-guided behaviors, and safety tools allow people with severe motor impairments to successfully use the system. We evaluated our system with a total of 10 able-bodied participants and 9 participants with motor impairments. Both groups of participants successfully ate various foods using the system and reported high rates of success for the system's autonomous behaviors. In general, participants who operated the system reported that it was comfortable, safe, and easy-to-use.

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“…As the population ages, the demand for caregivers to assist with direct and indirect daily living activities is increasing. A potential solution to this challenge is the deployment of robotic caregivers, which can provide increased independence for the elderly and complement human caregivers [1], [2], [3]. However, the command interfaces of robots typically require professional knowledge to operate effectively.…”

Section: Introductionmentioning

confidence: 99%

Natural Language-Guided Semantic Navigation Using Scene Graph

Kim

Cho

et al. 2023

Lecture Notes in Networks and Systems

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The spoken language serves as an accessible and efficient interface, enabling non-experts and disabled users to interact with complex assistant robots. However, accurately grounding language utterances gives a significant challenge due to the acoustic variability in speakers' voices and environmental noise. In this work, we propose a novel speech-scene graph grounding network (SGGNet 2 ) that robustly grounds spoken utterances by leveraging the acoustic similarity between correctly recognized and misrecognized words obtained from automatic speech recognition (ASR) systems. To incorporate the acoustic similarity, we extend our previous grounding model, the scene-graph-based grounding network (SGGNet), with the ASR model from NVIDIA NeMo. We accomplish this by feeding the latent vector of speech pronunciations into the BERTbased grounding network within SGGNet. We evaluate the effectiveness of using latent vectors of speech commands in grounding through qualitative and quantitative studies. We also demonstrate the capability of SGGNet 2 in a speech-based navigation task using a real quadruped robot, RBQ-3, from Rainbow Robotics.

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Task-centric selection of robot and environment initial configurations for assistive tasks

Cited by 10 publications

References 23 publications

Task-centric optimization of configurations for assistive robots

Task-centric optimization of configurations for assistive robots

Active robot-assisted feeding with a general-purpose mobile manipulator: Design, evaluation, and lessons learned

Natural Language-Guided Semantic Navigation Using Scene Graph

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