Robot-assisted feeding: A systematic review and future prospects

BackgroundRobot-assisted feeding systems aim to promote independence for individuals with motor impairments. Despite significant technological progress, widespread adoption remains limited due to challenges related to adaptability, safety, and cost.ObjectiveThis review investigates recent advancements in robot-assisted feeding, highlights key technical and usability challenges, and outlines future directions to improve system adaptability, autonomy, and cost-effectiveness.MethodsA systematic literature search was conducted for peer-reviewed articles published in the past decade. The analysis focuses on critical domains including hardware architecture, human-robot interaction (HRI) modalities, and control strategies.ResultsAdvances in artificial intelligence (AI) and HRI have enhanced system autonomy and user adaptability. Nevertheless, unresolved issues persist in handling diverse food types, achieving real-time responsiveness, and minimizing system costs. Emerging solutions-such as adaptive learning, Artificial Intelligence of Things (AIoT) integration, and modular design-offer promising pathways to overcome these barriers and support scalable deployment in real-world care settings.

Human-like Dexterous Grasping Through Reinforcement Learning and Multimodal Perception

Dexterous robotic grasping with multifingered hands remains a critical challenge in non-visual environments, where diverse object geometries and material properties demand adaptive force modulation and tactile-aware manipulation. To address this, we propose the Reinforcement Learning-Based Multimodal Perception (RLMP) framework, which integrates human-like grasping intuition through operator-worn gloves with tactile-guided reinforcement learning. The framework's key innovation lies in its Tactile-Driven DCNN architecture-a lightweight convolutional network achieving 98.5% object recognition accuracy using spatiotemporal pressure patterns-coupled with an RL policy refinement mechanism that dynamically correlates finger kinematics with real-time tactile feedback. Experimental results demonstrate reliable grasping performance across deformable and rigid objects while maintaining force precision critical for fragile targets. By bridging human teleoperation with autonomous tactile adaptation, RLMP eliminates dependency on visual input and predefined object models, establishing a new paradigm for robotic dexterity in occlusion-rich scenarios.

Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges

Artificial intelligence (AI) is an evolving set of technologies used for solving a wide range of applied issues. The core of AI is machine learning (ML)—a complex of algorithms and methods that address the problems of classification, clustering, and forecasting. The practical application of AI&ML holds promising prospects. Therefore, the researches in this area are intensive. However, the industrial applications of AI and its more intensive use in society are not widespread at the present time. The challenges of widespread AI applications need to be considered from both the AI (internal problems) and the societal (external problems) perspective. This consideration will identify the priority steps for more intensive practical application of AI technologies, their introduction, and involvement in industry and society. The article presents the identification and discussion of the challenges of the employment of AI technologies in the economy and society of resource-based countries. The systematization of AI&ML technologies is implemented based on publications in these areas. This systematization allows for the specification of the organizational, personnel, social and technological limitations. This paper outlines the directions of studies in AI and ML, which will allow us to overcome some of the limitations and achieve expansion of the scope of AI&ML applications.

A Low-Cost Assistive Robot for Children with Neurodevelopmental Disorders to Aid in Daily Living Activities

In this paper, we present a new low-cost robotic platform that has been explicitly developed to increase children with neurodevelopmental disorders’ involvement in the environment during everyday living activities. In order to support the children and youth with both the sequencing and learning of everyday living tasks, our robotic platform incorporates a sophisticated online action detection module that is capable of monitoring the acts performed by users. We explain all the technical details that allow many applications to be introduced to support individuals with functional diversity. We present this work as a proof of concept, which will enable an assessment of the impact that the developed technology may have on the collective of children and youth with neurodevelopmental disorders in the near future.

A Survey of Robots in Healthcare

In recent years, with the current advancements in Robotics and Artificial Intelligence (AI), robots have the potential to support the field of healthcare. Robotic systems are often introduced in the care of the elderly, children, and persons with disabilities, in hospitals, in rehabilitation and walking assistance, and other healthcare situations. In this survey paper, the recent advances in robotic technology applied in the healthcare domain are discussed. The paper provides detailed information about state-of-the-art research in care, hospital, assistive, rehabilitation, and walking assisting robots. The paper also discusses the open challenges healthcare robots face to be integrated into our society.

Robot-assisted feeding: A technical application that combines learning from demonstration and visual interaction

BACKGROUND

The traditional meal assistance robots use human-computer interaction such as buttons, voice, and EEG. However, most of them rely on excellent programming technology for development, in parallelism with exhibiting inconvenient interaction or unsatisfactory recognition rates in most cases.

OBJECTIVE

To develop a convenient human-computer interaction mode with a high recognition rate, which allows users to make the robot show excellent adaptability in the new environment without programming ability.

METHODS

A visual interaction method based on deep learning was used to develop the feeding robot: when the camera detects that the user's mouth is open for 2 seconds, the feeding command is turned on, and the feeding is temporarily conducted when the eyes are closed for 2 seconds. A programming method of learning from the demonstration, which is simple and has strong adaptability to different environments, was employed to generate a feeding trajectory.

RESULTS

The user is able to eat independently through convenient visual interaction, and it only requires the caregiver to drag and teach the robotic arm once in the face of a new eating environment.

Adaptive Human–Machine Evaluation Framework Using Stochastic Gradient Descent-Based Reinforcement Learning for Dynamic Competing Network

Complex problems require considerable work, extensive computation, and the development of effective solution methods. Recently, physical hardware- and software-based technologies have been utilized to support problem solving with computers. However, problem solving often involves human expertise and guidance. In these cases, accurate human evaluations and diagnoses must be communicated to the system, which should be done using a series of real numbers. In previous studies, only binary numbers have been used for this purpose. Hence, to achieve this objective, this paper proposes a new method of learning complex network topologies that coexist and compete in the same environment and interfere with the learning objectives of the others. Considering the special problem of reinforcement learning in an environment in which multiple network topologies coexist, we propose a policy that properly computes and updates the rewards derived from quantitative human evaluation and computes together with the rewards of the system. The rewards derived from the quantitative human evaluation are designed to be updated quickly and easily in an adaptive manner. Our new framework was applied to a basketball game for validation and demonstrated greater effectiveness than the existing methods.