Deformable Object Manipulation in Caregiving Scenarios

Deformable Object Manipulation in Caregiving Scenarios: History

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Subjects: Robotics

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The growing importance of deformable object manipulation (DOM) in caregiving settings can be attributed to various factors. One significant driving force is the aging population, which has led to increasing demand for caregiving services. In addition to supporting the elderly, deformable object manipulation technologies can assist individuals with disabilities and special needs, improving their quality of life and promoting independence. Another essential aspect of deformable object manipulation in caregiving scenarios is the potential to enhance precision and safety in medical procedures. These technologies can reduce the physical strain experienced by human caregivers, preventing injuries and enabling them to provide better patient care.

deformable object manipulation
robotic caregiving
assistive robots
machine learning
simulation environments

1. An Outlook by Timeline

As shown in Figure 6, since the 1980s, deformable object manipulation in caregiving has progressed from early simulation work [16,17] to real-world assistive robot systems, enabled by advances in analytical modeling, computer vision [18], and AI techniques. Key applications that emerged over the decades include robotic dressing [19], bedding [20,21,22], feeding [23,24,25,26], personal hygiene [27], and medical care assistance [28]. While early research in the 2000s relied on model-based methods [29,30,31], recent breakthroughs leveraged data-driven deep learning [32,33] for more flexible policies, like robotic knot-tying in 2021 [34,35]. The timeline illustrates milestones from early vision sensing in the 2000s [18] to robotic meal assistance in 2020 [24] and sway adaptation for bandaging in 2023 [28]. The overarching trend is the translation of deformable object manipulation from simulation environments to real-world caregiving tasks, with increasing autonomy and intelligence. This promises continued improvement in robotic assistance for the elderly and disabled through handling real-world deformable objects.

Figure 6. Key applications of deformable object manipulation in caregiving over time.

2. Classification of Applications

Over the past decade, several cases of DOM in caregiving scenarios have been documented, showcasing advancements in robotic capabilities, methodologies, and applications in the caregiving domain. User perspectives have also been studied, such as factors influencing the adoption of caregiving robots by the elderly and attitudes towards assistive systems [36].

Dressing assistance is an essential application of DOM in caregiving scenarios, particularly for users with limited mobility or dexterity. Researchers have developed robots capable of assisting users in putting on various clothing items, including T-shirts [19], pants [37], and footwear [38]. These studies have demonstrated the potential of DOM in addressing the challenges faced by individuals with disabilities and the elderly in performing daily dressing tasks.
Bedding and Cloth Management is another crucial application of DOM in caregiving scenarios, as it involves handling large, deformable objects and ensuring user comfort and hygiene. Researchers have developed robotic bed-making systems to grasp tension and smooth fitted sheets [20]. Robots capable of managing blankets [21] and pillows [22] have also been developed. The success of these works highlights the potential of DOM in addressing the challenges associated with bedsheet management and the need for continued research and development in this area. Cloth folding is also essential in caregiving settings, particularly for maintaining order and cleanliness. Researchers have developed robots capable of folding clothes, such as towels, shirts, and ropes [19,34,39,40,41,42,43].
Personal Hygiene Support is another critical application of DOM in caregiving scenarios. Researchers have developed robots that handle soft materials such as gauze [27] and diapers [44]. These studies have highlighted the importance of integrating various sensory modalities and control techniques for effective soft material handling in caregiving scenarios.
Meal Assistance is another important application of DOM in caregiving scenarios, particularly for users with limited mobility or dexterity. Researchers have developed robots capable of manipulating deformable objects such as food items and utensils [23,24,25,26]. These studies have demonstrated the potential of DOM in addressing the challenges faced by individuals with disabilities and the elderly in performing daily meal assistance tasks.
Daily Medical Care. In the context of bandaging, deformable object manipulation systems offer improved precision and control, enabling more effective and efficient wound-dressing procedures. These systems can adapt to the varying shapes and contours of the human body, as well as the patient’s involuntary swaying, ensuring proper bandage placement and tension for optimal healing [28]. Deformable object manipulation technologies assist patients during therapeutic exercises and activities in rehabilitation. They provide real-time feedback, support, and guidance, enhancing repair and promoting faster recovery [45,46,47].
Some other Applications. DOM has also been explored in other caregiving applications, such as housekeeping tasks like laundry management [48,49], window cleaning [50], and dishwashing [51].

The past decade has witnessed significant advancements in deformable object manipulation in caregiving scenarios. The increasing demand for caregiving robots highlights the importance of continued research and development to advance their capabilities in DOM and ensure their responsible deployment in various care settings.

3. A New Method to Classify and Analyse

In past research, the classification of deformable objects primarily adopted methodologies such as Sanchez’s [27], which emphasised the physical properties and morphology of the objects. The main objective of these techniques was to facilitate precise modelling of the objects in question. However, a paradigm shift in recent research trends, particularly in Artificial Intelligence, has changed this approach considerably.

The introduction of innovative learning methodologies like imitation learning and reinforcement learning has accelerated the capabilities of robotic arm operations. Consequently, the necessity to model different deformable objects separately has become less prominent. The study presents a unique classification framework focusing on distinct material behaviors and properties that influence manipulation strategies, as shown in Figure 8. This classification takes into account both the functionality of deformable items in caregiving scenarios and the physical characteristics of the items, which facilitates further analysis of the operating objects faced by the caregiving robot/robotic arm.

Figure 8. Classification of deformable objects in caregiving environments.

3.1. Common Types of Deformable Objects in Caregiving Scenarios

Textiles: This category would cover all cloth and fabric objects like clothing, sheets, towels, etc. Key properties are flexibility, drape, and shear.
Elastomers: Includes stretchable/elastic materials like bandages, tubing, and exercise bands. Key properties are elongation and elasticity.
Fluids: Encompasses materials like water, shampoo, and creams that flow and conform to containers. Key behaviors are pourability and viscosity.
Aggregates/Granular: Covers aggregated materials like rice, beans, and tablets. Flows but maintains loose particulate structure.
Gels: Highly viscous/elastic fluids like food gels, slime, and putty. Resist flow due to cross-linked molecular structure.
Cellular/Porous: Materials with internal voids like sponges and soft foams. Compressible and exhibit springback.
Composite/Hybrid: Combinations of the above categories, like stuffed animals and packaged goods. Display complex interactions of properties.

3.2. A Multi-Factor Analysis Method

While assigning arbitrary subjective scores lacks rigor, a multi-factor analysis can systematically prioritise deformable object manipulation research needs for caregiving. Several key factors should be considered:

Application Utility (U)—Potential to reduce caregiver burden by automating tasks
Object Frequency (F)—How often the object occurs in caregiving activities
Task Complexity (C)—Technical challenges posed by physical properties and handling difficulties
Safety Criticality (S)—Risks of injury or harm during object manipulation
Research Maturity (M)—Existing state of manipulation methods for the object

To apply this analysis, quantitative metrics for each factor are gathered via:

Surveys, interviews, and activity logging (for Application Utility)
Workflow observations and activity logging (for Object Frequency)
Material testing, caregiver surveys, and interviews (for Task Complexity)
Incident data and healthcare professional feedback (for Safety Criticality)
Literature review (for Research Maturity)

The metrics are then combined as follows:

Each metric is normalised on a 0–1 scale based on the maximum value observed. This transforms metrics to a common scale.
Criteria weights are assigned to each factor based on the caregiving context. For example, Safety Criticality may be weighted higher for hospital settings compared to home care.
Weighted sums are calculated by multiplying each normalised metric by the criteria weight.
The weighted sums are aggregated to derive an overall priority score P for each deformable object, where U, F, C, S, and M are the normalised metrics and w1 to w5 are the criteria weights.:

Objects are ranked by priority score P, identifying high-impact areas needing research innovations.

Tuning the criteria weights for specific caregiving applications allows us to systematically prioritise the most promising research directions via this data-driven analysis. Multi-factor analysis indicates general priorities, while subjective scoring adds nuanced use-case customisation. Using both techniques together provides rigor and versatility in determining the key deformable object manipulation research needs for a maximal real-world impact.

4. Challenges of DOM in Caregiving

Accurately modelling deformable objects is challenging due to their high-dimensional, non-linear, and time-varying behaviour [16]. While mathematical models, such as the Finite Element Method (FEM) and Mass-Spring models, can capture some of these properties [17], they may not always be sufficient for real-world caregiving scenarios. This necessitates exploring data-driven and hybrid modelling approaches, such as deep learning-based models [41,43,52,53,54,55] and Gaussian process regression [56,57,58,59,60].

Deformable object manipulation often requires real-time, robust, and precise perception of the object’s shape, deformation, and environmental interactions [61]. This can be challenging due to occlusions, sensor noise, and varying environmental conditions. Multi-modal sensing techniques, such as vision and tactile sensing, have enhanced perception capabilities [62,63,64]. Recent advancements in soft tactile sensors and RGB-D cameras have further improved the accuracy and reliability of deformable object perception [65].

Developing effective control and planning algorithms for deformable object manipulation is challenging due to the non-linear dynamics, uncertainties, and constraints associated with these objects [66]. Traditional robotic control strategies may not be suitable, leading to the investigation of novel control frameworks, including learning-based, adaptive control methods [67] and impedance control [68].

In caregiving scenarios, ensuring the safety of patients and human caregivers during deformable object manipulation is of paramount importance. This requires the development of control strategies that can adapt to uncertainties and unforeseen events, as well as methods for effective communication and collaboration between robots and humans. Recent research on shared autonomy, where a human operator and a robotic system jointly control the manipulation process, has shown promise in achieving safe and efficient deformable object manipulation in caregiving scenarios [69,70,71].

This entry is adapted from the peer-reviewed paper 10.3390/machines11111013

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