Language-Based Augmentation to Address Shortcut Learning in Object Goal Navigation

Deep Reinforcement Learning (DRL) has shown great potential in enabling robots to find certain objects (e.g., `find a fridge') in environments like homes or schools. This task is known as Object-Goal Navigation (ObjectNav). DRL methods are predominantly trained and evaluated using environment simulators. Although DRL has shown impressive results, the simulators may be biased or limited. This creates a risk of shortcut learning, i.e., learning a policy tailored to specific visual details of training environments. We aim to deepen our understanding of shortcut learning in ObjectNav, its implications and propose a solution. We design an experiment for inserting a shortcut bias in the appearance of training environments. As a proof-of-concept, we associate room types to specific wall colors (e.g., bedrooms with green walls), and observe poor generalization of a state-of-the-art (SOTA) ObjectNav method to environments where this is not the case (e.g., bedrooms with blue walls). We find that shortcut learning is the root cause: the agent learns to navigate to target objects, by simply searching for the associated wall color of the target object's room. To solve this, we propose Language-Based (L-B) augmentation. Our key insight is that we can leverage the multimodal feature space of a Vision-Language Model (VLM) to augment visual representations directly at the feature-level, requiring no changes to the simulator, and only an addition of one layer to the model. Where the SOTA ObjectNav method's success rate drops 69%, our proposal has only a drop of 23%

Language-Based Augmentation to Address Shortcut Learning in Object Goal Navigation

Deep Reinforcement Learning (DRL) has shown great potential in enabling robots to find certain objects (e.g., `find a fridge') in environments like homes or schools. This task is known as Object-Goal Navigation (ObjectNav). DRL methods are predominantly trained and evaluated using environment simulators. Although DRL has shown impressive results, the simulators may be biased or limited. This creates a risk of shortcut learning, i.e., learning a policy tailored to specific visual details of training environments. We aim to deepen our understanding of shortcut learning in ObjectNav, its implications and propose a solution. We design an experiment for inserting a shortcut bias in the appearance of training environments. As a proof-of-concept, we associate room types to specific wall colors (e.g., bedrooms with green walls), and observe poor generalization of a state-of-the-art (SOTA) ObjectNav method to environments where this is not the case (e.g., bedrooms with blue walls). We find that shortcut learning is the root cause: the agent learns to navigate to target objects, by simply searching for the associated wall color of the target object's room. To solve this, we propose Language-Based (L-B) augmentation. Our key insight is that we can leverage the multimodal feature space of a Vision-Language Model (VLM) to augment visual representations directly at the feature-level, requiring no changes to the simulator, and only an addition of one layer to the model. Where the SOTA ObjectNav method's success rate drops 69%, our proposal has only a drop of 23%.Comment: 8 pages, 6 figures, to be published in IEEE IRC 202

Differentiated thyroid carcinoma : treatment and clinical consequences of therapy

The first chapters of this thesis describe the treatment of radioiodine non-avid thyroid carcinoma with the tyrosine kinase inhibitor sorafenib. The remainder of the thesis describes the clinical consequences of the treatment of thyroid carcinoma.Bayer B.V. Novo Nordisk B.V. Servier Nederland Farma B.V. MSD B.V. Genzyme B.V. AstraZeneca B.V. Ipsen Farmaceutica B.V. Novartis Pharma B.V. J.E. Jurriaanse StichtingUBL - phd migration 201

Het geheim van de uitgever

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Tuta sub aegide Pallas. Drukkersmerken door de eeuwen heen

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'Bibliotheca Thysiana. Tot publycque dienst der studie’

Medieval and Early Modern Studie

Rijk van Pallas. Rede uitgesproken bij de aanvaarding van het ambt van bijzonder hoogleraar in de geschiedenis van het boek vanwege de Dr P.A. Tiele-Stichting aan de Universiteit Leiden

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‘I know I'm not invincible’: An interpretative phenomenological analysis of thyroid cancer in young people

Objective. Thyroid cancer is one of the most common cancers affecting young people and carries an excellent prognosis. Little is known about the psychosocial issues that face young people diagnosed with a treatable cancer. This study explored how young people experienced diagnosis, treatment, and how they made sense of an experience which challenged their views on what it means to have cancer. Method. Semi-structured interviews were conducted with eight young people diagnosed with either papillary or follicular thyroid cancer, and analysed with interpre- tative phenomenological analysis (IPA). Results. Two inter-related aspects of their experience are discussed: (1) the range of feelings and emotions experienced including feeling disregarded, vulnerability, shock and isolation; (2) how they made sense of and ascribed meaning to their experience in the light of the unique nature of their cancer. A thread running throughout the findings highlights that this was a disruptive biographical experience. Conclusions. Young people experienced a loss of youthful immunity which contrasted with a sense of growth and shift in life perspective. Having a highly treatable cancer was helpful in aiding them to reframe their situation positively but at the same time left them feeling dismissed over a lack of recognition that they had cancer. The young peoples’ experiences point to a need for increased understanding of this rare cancer, more effective communication from health care professionals and a greater understanding of the experiential impact of this disease on young people. Suggestions to improve the service provision to this patient group are provided

Van vele letters thuis. Niet-westerse typografie in Europe en Leiden tot 1800

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