Mobile Manipulation for the HSR Intelligent Home Service Robot

Yi, Jae-Bong; Yi, Seung‐Joon

doi:10.1109/urai.2019.8768782

Cited by 17 publications

(9 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These issues (i.e., multimodal perception, manipulation planning and reasoning, system-level integration, speed of execution, and whole-body coordination) continue to be the main challenges in mobile manipulation systems, as observed during other recent competitions [10], [11], [13]. A more recent development is the introduction of competitions that focus on learning-based approaches, e.g., the Real Robot Challenge by the MPI-IS in 2020.…”

Section: Further Technical Advances Requiredmentioning

confidence: 99%

“…The task was to assemble a kit formed by six objects obtained from stations around a designated arena, combining navigation and manipulation skills. Similarly, the RoboCup@Home competition (https://athome.robocup.org/), using the Toyota Human Support Robot (HSR) [11] as the official platform, includes a set of tidying-up and service tasks in living room and kitchen setups, requiring mobile manipulation. RoboCup@Home also encourages teams to make "open challenge" demonstrations (i.e., free demonstrations determined by the teams, instead of a fixed set of tasks), although these are not the main focus, as they are performed during off hours and do not necessarily include awards [12].…”

Section: Related Hackathons and Competitionsmentioning

confidence: 99%

See 1 more Smart Citation

Mobile Manipulation Hackathon: Moving into Real World Applications

Roa

Dogar

Pagès³

et al. 2021

IEEE Robot. Automat. Mag.

View full text Add to dashboard Cite

Section: Further Technical Advances Requiredmentioning

confidence: 99%

Section: Related Hackathons and Competitionsmentioning

confidence: 99%

Mobile Manipulation Hackathon: Moving into Real World Applications

Roa

Dogar

Pagès³

et al. 2021

IEEE Robot. Automat. Mag.

View full text Add to dashboard Cite

“…However, these robot architectures could not support table cleaning and maintenance tasks. In this context, HSR can be a viable candidate for this task [9,10]. However, configuring HSR for cleanliness inspection and food litter collection is a challenging task [11].…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by the works mentioned above, this work proposes the deep learning-based food litter detection system and a path planner algorithm for the Toyota Human Support Robot (HSR) [10,32] to accomplish the table cleaning and inspection task for the standard food court setting. The technical contributions of the paper are as follows.…”

Section: Introductionmentioning

confidence: 99%

Table Cleaning Task by Human Support Robot Using Deep Learning Technique

Yin

Apuroop

Tamilselvam

et al. 2020

Sensors

View full text Add to dashboard Cite

This work presents a table cleaning and inspection method using a Human Support Robot (HSR) which can operate in a typical food court setting. The HSR is able to perform a cleanliness inspection and also clean the food litter on the table by implementing a deep learning technique and planner framework. A lightweight Deep Convolutional Neural Network (DCNN) has been proposed to recognize the food litter on top of the table. In addition, the planner framework was proposed to HSR for accomplishing the table cleaning task which generates the cleaning path according to the detection of food litter and then the cleaning action is carried out. The effectiveness of the food litter detection module is verified with the cleanliness inspection task using Toyota HSR, and its detection results are verified with standard quality metrics. The experimental results show that the food litter detection module achieves an average of 96% detection accuracy, which is more suitable for deploying the HSR robots for performing the cleanliness inspection and also helps to select the different cleaning modes. Further, the planner part has been tested through the table cleaning tasks. The experimental results show that the planner generated the cleaning path in real time and its generated path is optimal which reduces the cleaning time by grouping based cleaning action for removing the food litters from the table.Sensors 2020, 20, 1698 2 of 20 vision-based techniques are widely used in cleaning robots for recognizing the litter and compute the cleaning action [14][15][16][17][18][19]. Andersen et al., built up a visual cleaning map for cleaning robots using a vision algorithm and a powerful light-transmitting diode. The sensor recognizes the grimy region and generates the dirt map by examining the surface pictures pixel-by-pixel utilizing the multi-variable statistical method [15]. David et al., proposed high-level manipulation actions for cleaning dirt from table surfaces using REEM a humanoid service robot. The author uses a background subtraction algorithm for recognizing the dirt from the table and Noisy Indeterministic Deictic (NID) rules-based learning algorithm to generate the sequence of cleaning action [16]. Ariyan et al., developed a planning algorithm for the removal of stains from non-planar surfaces where the author uses a depth-first branch-and-bound search to generate cleaning trajectories with the K-means clustering algorithm [17]. Hass et al., demonstrated the use of unsupervised clustering algorithm and Markov Decision Problem (MDP) for performing the cleaning task where unsupervised clustering algorithm is used to distinguish the dirt from surface and MDP algorithm is used to generate the maps, and transition model from clustered image is used to describe the robot cleaning action [18]. Nonetheless, these approaches have some practical issues and disadvantages for using in food court table cleaning; the detection ratio relies heavily on the textured surfaces, which makes it challenging to identify the litter type as solid...

show abstract

“…In a similar line of work, a motion-planning and arm-manipulation technique was proposed for grasping books from any table, and returning them to a predetermined position [ 31 ]. The motion-planning and manipulation for an HSR is discussed in [ 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

A Human Support Robot for the Cleaning and Maintenance of Door Handles Using a Deep-Learning Framework

Ramalingam

Yin

Elara

et al. 2020

Sensors

View full text Add to dashboard Cite

The role of mobile robots for cleaning and sanitation purposes is increasing worldwide. Disinfection and hygiene are two integral parts of any safe indoor environment, and these factors become more critical in COVID-19-like pandemic situations. Door handles are highly sensitive contact points that are prone to be contamination. Automation of the door-handle cleaning task is not only important for ensuring safety, but also to improve efficiency. This work proposes an AI-enabled framework for automating cleaning tasks through a Human Support Robot (HSR). The overall cleaning process involves mobile base motion, door-handle detection, and control of the HSR manipulator for the completion of the cleaning tasks. The detection part exploits a deep-learning technique to classify the image space, and provides a set of coordinates for the robot. The cooperative control between the spraying and wiping is developed in the Robotic Operating System. The control module uses the information obtained from the detection module to generate a task/operational space for the robot, along with evaluating the desired position to actuate the manipulators. The complete strategy is validated through numerical simulations, and experiments on a Toyota HSR platform.

show abstract

Mobile Manipulation for the HSR Intelligent Home Service Robot

Cited by 17 publications

References 6 publications

Mobile Manipulation Hackathon: Moving into Real World Applications

Mobile Manipulation Hackathon: Moving into Real World Applications

Table Cleaning Task by Human Support Robot Using Deep Learning Technique

A Human Support Robot for the Cleaning and Maintenance of Door Handles Using a Deep-Learning Framework

Contact Info

Product

Resources

About