In nature, fish are excellent swimmers due to their flexible and precise control of tail, which allows them to freely transform between the smooth flapping and the motion of rapid response so that they can move with dexterity. Here, inspired by the versatile motion abilities of fish, a novel robotic fish has been developed, featuring the capability of adaptable bistability. Through tuning the bistability, the robot can acquire two locomotion modes, namely monostable and bistable modes, and it can also swim at different energy barrier that needs to be overcome to realize the bistable motion. The theoretical models are derived to facilitate the control of the robot and the understanding of its nonlinear behavior. The impact of the tunable bistability on the swimming and turning performance is investigated through extensive experiments. The study effectively demonstrates the robotic fish’s capability to swiftly and efficiently navigate through mode switches, enabled by its tunable bistability. This feature is essential for underwater robots to perform tasks in intricate environments.

Named Entity Recognition (NER) is an indispensable component of Natural Language Processing (NLP), which aims to identify and classify entities within text data. While Deep Learning (DL) models have excelled in NER for well-resourced languages such as English, Spanish, and Chinese, they face significant hurdles when dealing with low-resource languages such as Urdu. These challenges stem from the intricate linguistic characteristics of Urdu, including morphological diversity, a context-dependent lexicon, and the scarcity of training data. This study addresses these issues by focusing on Urdu Named Entity Recognition (U-NER) and introducing three key contributions. First, various pre-trained embedding methods are employed, encompassing Word2vec (W2V), GloVe, FastText, Bidirectional Encoder Representations from Transformers (BERT), and Embeddings from language models (ELMo). In particular, fine-tuning is performed on BERT BASE and ELMo using Urdu Wikipedia and news articles. Second, a novel generative Data Augmentation (DA) technique replaces Named Entities (NEs) with mask tokens, employing pre-trained masked language models to predict masked tokens, effectively expanding the training dataset. Finally, the study introduces a novel hybrid model combining a Transformer Encoder with a Convolutional Neural Network (CNN) to capture the intricate morphology of Urdu. These modules enable the model to handle polysemy, extract short- and long-range dependencies, and enhance learning capacity. Empirical experiments demonstrate that the proposed model, incorporating BERT embeddings and an innovative DA approach, attains the highest F1-score of 93.99%, highlighting its efficacy for the U-NER task.

Developing a good control strategy for biomimetic robots is challenging. Robust control methods require an accurate model of the robot. Nowadays, model-free methods are being extensively explored for the control and navigation of terrestrial robots. In this paper, we consider a novel deep reinforcement learning-based model-free swimming control for our bio-inspired robotic tadpole. To realize this, we utilize dynamic motion primitives, which can represent a large range of motion behaviors, and combine them with a decoupled reinforcement learning framework. The proposed architecture optimizes the motion primitives first to develop a travelling wave undulation pattern in the tail and then to navigate the robot along different predefined paths. Through this framework, effective swimming gait emerges, and the robot is able to navigate well on the surface of water. This framework combines the optimization potential of deep reinforcement learning with stability and generalization properties of dynamic motion primitives. We train and test our method on a simulated model of the robot to demonstrate the effectiveness of the method and also conduct experimental testing on the real robot to verify the results.