A friction-model-based framework for Reinforcement Learning of robotic tasks in non-rigid environments

Colomé, Adrià; Planells, Antoni; Torras, Carme

doi:10.1109/icra.2015.7139990

Cited by 50 publications

(44 citation statements)

References 11 publications

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“…For example, reinforcement learning has been successfully used to enable a robot to wrap a scarf [2] and to dress a Tshirt on human mannequins with different head and shoulder inclinations [3,4,8]. The topological relationships between the mannequin and the item of clothing have been studied to optimize the trajectory of the robot through reinforcement learning [5,6].…”

Section: Related Workmentioning

confidence: 99%

“…A common approach to solve this problem is to recognize the postures of the user in real time and to adjust the robot's trajectory accordingly [2][3][4][5][6]. However, severe occlusions occur when the robot arms, the clothes and the human body are in close contact [7], which lead to real-time human pose recognition failures during the dressing process.…”

Section: Introductionmentioning

confidence: 99%

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Personalized robot-assisted dressing using user modeling in latent spaces

Zhang¹,

Cully²,

Demiris³

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Robots have the potential to provide tremendous support to disabled and elderly people in their everyday tasks, such as dressing. Many recent studies on robotic dressing assistance usually view dressing as a trajectory planning problem. However, the user movements during the dressing process are rarely taken into account, which often leads to the failures of the planned trajectory and may put the user at risk. The main difficulty of taking user movements into account is caused by severe occlusions created by the robot, the user, and the clothes during the dressing process, which prevent vision sensors from accurately detecting the postures of the user in real time. In this paper, we address this problem by introducing an approach that allows the robot to automatically adapt its motion according to the force applied on the robot's gripper caused by user movements. There are two main contributions introduced in this paper: 1) the use of a hierarchical multi-task control strategy to automatically adapt the robot motion and minimize the force applied between the user and the robot caused by user movements; 2) the online update of the dressing trajectory based on the user movement limitations modeled with the Gaussian Process Latent Variable Model in a latent space, and the density information extracted from such latent space. The combination of these two contributions leads to a personalized dressing assistance that can cope with unpredicted user movements during the dressing while constantly minimizing the force that the robot may apply on the user. The experimental results demonstrate that the proposed method allows the Baxter humanoid robot to provide personalized dressing assistance for human users with simulated upper-body impairments.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Personalized robot-assisted dressing using user modeling in latent spaces

Zhang¹,

Cully²,

Demiris³

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Assistive robots can use the vision information of a human body when dressing a user [4][5][6]. However, occlusions could occur when the robot's arms, the clothes and the human body are in close contact, which leads to human pose recognition failures.…”

Section: Introductionmentioning

confidence: 99%

Iterative path optimisation for personalised dressing assistance using vision and force information

Gao¹,

Chang²,

Demiris³

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

104

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Abstract-We propose an online iterative path optimisation method to enable a Baxter humanoid robot to assist human users to dress. The robot searches for the optimal personalised dressing path using vision and force sensor information: vision information is used to recognise the human pose and model the movement space of upper-body joints; force sensor information is used for the robot to detect external force resistance and to locally adjust its motion. We propose a new stochastic path optimisation method based on adaptive moment estimation. We first compare the proposed method with other path optimisation algorithms on synthetic data. Experimental results show that the performance of the method achieves the smallest error with fewer iterations and less computation time. We also evaluate real-world data by enabling the Baxter robot to assist real human users with their dressing.

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“…Researchers have used reinforcement learning to teach robots to put on a t-shirt and wrap a scarf around a mannequin's neck [10,5]. In the latter work, the robot learns tasks through dynamic movement primitives, allowing them to modify the trajectory speed or goal location.…”

Section: Related Workmentioning

confidence: 99%

Personalized Assistance for Dressing Users

Klee

Ferreira

Silva

et al. 2015

Lecture Notes in Computer Science

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Abstract. In this paper, we present an approach for a robot to provide personalized assistance for dressing a user. In particular, given a dressing task, our approach finds a solution involving manipulator motions and also user repositioning requests. Specifically, the solution allows the robot and user to take turns moving in the same space and is cognizant of the user's limitations. To accomplish this, a vision module monitors the human's motion, determines if he is following the repositioning requests, and infers mobility limitations when he cannot. The learned constraints are used during future dressing episodes to personalize the repositioning requests. Our contributions include a turn-taking approach to humanrobot coordination for the dressing problem and a vision module capable of learning user limitations. After presenting the technical details of our approach, we provide an evaluation with a Baxter manipulator.

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A friction-model-based framework for Reinforcement Learning of robotic tasks in non-rigid environments

Cited by 50 publications

References 11 publications

Personalized robot-assisted dressing using user modeling in latent spaces

Personalized robot-assisted dressing using user modeling in latent spaces

Iterative path optimisation for personalised dressing assistance using vision and force information

Personalized Assistance for Dressing Users

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