Towards Safe Human-Robot Collaboration Using Deep Reinforcement Learning

El-Shamouty, Mohamed; Wu, Xinyang; Yang, Shanqi; Albus, Marcel; Huber, Marco F.

doi:10.1109/icra40945.2020.9196924

Cited by 48 publications

(27 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite the current trend in robotics literature (e.g., [1,2,3,4]), we believe that it is implausible to learn the human's behavior by only passively observing his/her states and actions. Indeed, the human's trajectory may not encode sufficient information about the human.…”

Section: Introductionmentioning

confidence: 88%

Toward Safe and Efficient Human-Robot Interaction via Behavior-Driven Danger Signaling

Hosseinzadeh,

Sinopoli,

Bobick

2021

Preprint

View full text Add to dashboard Cite

This paper introduces the notion of danger awareness in the context of Human-Robot Interaction (HRI), which decodes whether a human is aware of the existence of the robot, and illuminates whether the human is willing to engage in enforcing the safety. This paper also proposes a method to quantify this notion as a single binary variable, so-called danger awareness coefficient. By analyzing the effect of this coefficient on the human's actions, an online Bayesian learning method is proposed to update the belief about the value of the coefficient. It is shown that based upon the danger awareness coefficient and the proposed learning method, the robot can build a predictive human model to anticipate the human's future actions. In order to create a communication channel between the human and the robot, to enrich the observations and get informative data about the human, and to improve the efficiency of the robot, the robot is equipped with a danger signaling system. A predictive planning scheme, coupled with the predictive human model, is also proposed to provide an efficient and Probabilistically safe plan for the robot. The effectiveness of the proposed scheme is demonstrated through simulation studies on an interaction between a self-driving car and a pedestrian.

show abstract

Section: Introductionmentioning

confidence: 88%

Toward Safe and Efficient Human-Robot Interaction via Behavior-Driven Danger Signaling

Hosseinzadeh,

Sinopoli,

Bobick

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…El-Shamouty et al ( 2020 ), in trying to minimize the risk of accidents in HRC scenarios, propose a deep RL framework that encodes all the task and safety requirements of the scenario into RL settings, and also takes into account components such as the behavior of the human operator. Liu and Hao ( 2019 ) work on a scenario of multimodal CNNs and use a Leap Motion sensor for hand motion detection, as well as voice and body posture recognition.…”

Section: State Of the Artmentioning

confidence: 99%

Egocentric Gesture Recognition Using 3D Convolutional Neural Networks for the Spatiotemporal Adaptation of Collaborative Robots

2021

View full text Add to dashboard Cite

Collaborative robots are currently deployed in professional environments, in collaboration with professional human operators, helping to strike the right balance between mechanization and manual intervention in manufacturing processes required by Industry 4.0. In this paper, the contribution of gesture recognition and pose estimation to the smooth introduction of cobots into an industrial assembly line is described, with a view to performing actions in parallel with the human operators and enabling interaction between them. The proposed active vision system uses two RGB-D cameras that record different points of view of gestures and poses of the operator, to build an external perception layer for the robot that facilitates spatiotemporal adaptation, in accordance with the human's behavior. The use-case of this work is concerned with LCD TV assembly of an appliance manufacturer, comprising of two parts. The first part of the above-mentioned operation is assigned to a robot, strengthening the assembly line. The second part is assigned to a human operator. Gesture recognition, pose estimation, physical interaction, and sonic notification, create a multimodal human-robot interaction system. Five experiments are performed, to test if gesture recognition and pose estimation can reduce the cycle time and range of motion of the operator, respectively. Physical interaction is achieved using the force sensor of the cobot. Pose estimation through a skeleton-tracking algorithm provides the cobot with human pose information and makes it spatially adjustable. Sonic notification is added for the case of unexpected incidents. A real-time gesture recognition module is implemented through a Deep Learning architecture consisting of Convolutional layers, trained in an egocentric view and reducing the cycle time of the routine by almost 20%. This constitutes an added value in this work, as it affords the potential of recognizing gestures independently of the anthropometric characteristics and the background. Common metrics derived from the literature are used for the evaluation of the proposed system. The percentage of spatial adaptation of the cobot is proposed as a new KPI for a collaborative system and the opinion of the human operator is measured through a questionnaire that concerns the various affective states of the operator during the collaboration.

show abstract

“…Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) methods are a promising approach to solving complex tasks in the real world with physical robots. RL/DRL methods are also used in real-world applications, such as improvements in the gaming industry for the Go game [24], as well as in robotic applications for manipulation [21], goal achievement [5,22], Human-Robot Collaboration [9], and more [17].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal

Parak

Matoušek

2021

mendel

View full text Add to dashboard Cite

Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) methods are a promising approach to solving complex tasks in the real world with physical robots. In this paper, we compare several reinforcement learning (Q-Learning, SARSA) and deep reinforcement learning (Deep Q-Network, Deep Sarsa) methods for a task aimed at achieving a specific goal using robotics arm UR3. The main optimization problem of this experiment is to find the best solution for each RL/DRL scenario and minimize the Euclidean distance accuracy error and smooth the resulting path by the Bézier spline method. The simulation and real word applications are controlled by the Robot Operating System (ROS). The learning environment is implemented using the OpenAI Gym library which uses the RVIZ simulation tool and the Gazebo 3D modeling tool for dynamics and kinematics.

show abstract

Towards Safe Human-Robot Collaboration Using Deep Reinforcement Learning

Cited by 48 publications

References 18 publications

Toward Safe and Efficient Human-Robot Interaction via Behavior-Driven Danger Signaling

Toward Safe and Efficient Human-Robot Interaction via Behavior-Driven Danger Signaling

Egocentric Gesture Recognition Using 3D Convolutional Neural Networks for the Spatiotemporal Adaptation of Collaborative Robots

Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal

Contact Info

Product

Resources

About