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Chronic illnesses often go unnoticed mainly due to their invisibility and lack of understanding both at home and in the workplace. In this chapter, I use an autoethnographic approach to engage with my “emotionally charged” lived experiences of living and working with a stigmatized chronic illness – irritable bowel syndrome (IBS) – in a highly patriarchal Pashtun society where women are expected to perform various social roles despite of illness and are often silenced to male domination. IBS is a functional gastrointestinal disorder characterized by abdominal pain, abnormal bowel function, and bloating, in the absence of any structural abnormalities, and has a significant impact on one’s life. As I navigate through my experiences of suffering from a chronic illness and the emotional labor involved therein, I shed light on the challenges I face as a woman in managing work and life and as I silence my pain and emotions to fit into the roles of a “professional” academic, a “good” wife, a “good” daughter, a “good” sister-in-law, a “good” daughter-in-law, and so forth. I have used both the lens of stigma to reflect my sufferings and normalization to demonstrate my resilience and (re)adjustment to the new life. In doing so, pain and emotions do leak out during intense situations but silencing chronic illness is mostly strategic as it protects us from being excluded, marginalized, and stigmatzed both at work and home.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

Assistive technology has been developed to assist the visually impaired individuals in their social interactions. Specifically designed to enhance communication skills, facilitate social engagement and improve the overall quality of life, conversational assistive technologies include speech recognition APIs, text-to-speech APIs and various communication tools that are real. Enable real-time interaction. Using natural language processing (NLP) and machine learning algorithms, the technology analyzes spoken language and provides appropriate responses, offering an immersive experience through voice commands, audio feedback and vibration alerts.

These technologies have demonstrated their ability to promote self-confidence and self-reliance in visually impaired individuals during social interactions. Moreover, they promise to improve social competence and foster better relationships. In short, assistive technology in conversation stands as a promising tool that empowers the visually impaired individuals, elevating the quality of their social engagement.

The main benefit of assistive communication technology is that it will help visually impaired people overcome communication barriers in social contexts. This technology helps them communicate effectively with acquaintances, family, co-workers and even strangers in public places. By enabling smoother and more natural communication, it works to reduce feelings of isolation and increase overall quality of life.

Research findings include successful activity recognition, aligning with activities on which the VGG-16 model was trained, such as hugging, shaking hands, talking, walking, waving and more. The originality of this study lies in its approach to address the challenges faced by the visually impaired individuals in their social interactions through modern technology. Research adds to the body of knowledge in the area of assistive technologies, which contribute to the empowerment and social inclusion of the visually impaired individuals.