To tackle the water surface pollution problem, a vision-based water surface garbage capture robot has been developed in our lab. In this article, we present a modified you only look once v3-based garbage detection method, allowing real-time and high-precision object detection in dynamic aquatic environments. More specifically, to improve the real-time detection performance, the detection scales of you only look once v3 are simplified from 3 to 2. Besides, to guarantee the accuracy of detection, the anchor boxes of our training data set are reclustered for replacing some of the original you only look once v3 prior anchor boxes that are not appropriate to our data set. By virtue of the proposed detection method, the capture robot has the capability of cleaning floating garbage in the field. Experimental results demonstrate that both detection speed and accuracy of the modified you only look once v3 are better than those of other object detection algorithms. The obtained results provide valuable insight into the high-speed detection and grasping of dynamic objects in complex aquatic environments autonomously and intelligently.

To tackle the problem of aquatic environment pollution, a vision-based autonomous underwater garbage cleaning robot has been developed in our laboratory. W

Large language models (LLMs) have made remarkable advances in natural language processing, demonstrating great potential in modelling structured sequences. However, adapting these capabilities to machine gaming tasks such as Go remains challenging due to limitations in strategy generalisation and optimisation efficiency. This paper presents multitype game optimisation (MyGO), a two‐stage fine‐tuning framework tailored for two‐player perfect information board games, exploring the applicability of LLMs to nonlinguistic decision‐making domains. In the supervised fine‐tuning stage, we propose a unified structural encoding method, action semantic unit (ASU), which efficiently converts heterogeneous game records into discrete token sequences compatible with LLMs. In the reinforcement learning stage, we design TA‐PPO (token‐level adaptive proximal policy optimisation), an enhanced PPO‐based algorithm to address the issue of sparse feedback commonly encountered in game reinforcement learning. Experimental results demonstrate that the fine‐tuned models achieve superior or comparable performance to traditional game‐playing algorithms in terms of strategy quality, rule generalisation and inference efficiency. This work provides a scalable paradigm for fine‐tuning LLMs in complex decision‐making tasks and lays a foundation for future research in game AI and generalisable strategy optimisation.

In this study, hybrid state-action-reward-state-action (SARSAλ) and Q-learning algorithms are applied to different stages of an upper confidence bound applied to tree search for Tibetan Jiu chess. Q-learning is also used to update all the nodes on the search path when each game ends. A learning strategy that uses SARSAλ and Q-learning algorithms combining domain knowledge for a feedback function for layout and battle stages is proposed. An improved deep neural network based on ResNet18 is used for self-play training. Experimental results show that hybrid online and offline reinforcement learning with a deep neural network can improve the game program’s learning efficiency and understanding ability for Tibetan Jiu chess.

With the development of deep learning technology, speech synthesis based on deep neural networks has gradually become the mainstream method in the field of speech synthesis. In this paper, we explored the Tacotron2 model for Lhasa-Tibetan dialect speech synthesis by constructing a feature prediction network with a seq2seq structure which maps the character vector to Mel spectrum, and combining with the WaveNet model trained in a semi-supervised way to synthesize the Mel spectrum into a time domain waveform. The model avoids processing front-end text analysis that requires extensive prior knowledge in Lhasa-Tibetan dialect and reduces the need of a large amount of transcribed speech data. Experimental results show that the proposed method is effective and has higher clarity and naturalness than other related synthesis models for Lhasa-Tibetan dialect.