Modular robots offer reconfigurability and fault tolerance essential for lunar missions, but require controllers that adapt safely to real-world disturbances. We build on our previous hardware-agnostic actuator synchronization in Motion Stack to develop a new controller enforcing adaptive velocity bounds via a dynamic hypersphere clamp. Using only real-time end-effector and target pose measurements, the controller adjusts its translational and rotational speed limits to ensure smooth, stable alignment without abrupt motions. We implemented two variants, a discrete, step-based version and a continuous, velocity-based version, and tested them on two MoonBot limbs in JAXA's lunar environment simulator. Field trials demonstrate that the step-based variant produces highly predictable, low-wobble motions, while the continuous variant converges more quickly and maintains millimeter-level positional accuracy, and both remain robust across limbs with differing mechanical imperfections and sensing noise (e.g., backlash and flex). These results highlight the flexibility and robustness of our robot-agnostic framework for autonomous self-assembly and reconfiguration under harsh conditions.

With the rapid growth of smart home IoT devices, users are increasingly exposed to various security risks, as evident from recent studies. While seeking answers to know more on those security concerns, users are mostly left with their own discretion while going through various sources, such as online blogs and technical manuals, which may render higher complexity to regular users trying to extract the necessary information. This requirement does not go along with the common mindsets of smart home users and hence threatens the security of smart homes furthermore. In this paper, we aim to identify and address the major user-level security concerns in smart homes. Specifically, we develop a novel dataset of Q&A from public forums, capturing practical security challenges faced by smart home users. We extract major security concerns in smart homes from our dataset by leveraging the Latent Dirichlet Allocation (LDA). We fine-tune relatively "smaller" transformer models, such as T5 and Flan-T5, on this dataset to build a QA system tailored for smart home security. Unlike larger models like GPT and Gemini, which are powerful but often resource hungry and require data sharing, smaller models are more feasible for deployment in resource-constrained or privacy-sensitive environments like smart homes. The dataset is manually curated and supplemented with synthetic data to explore its potential impact on model performance. This approach significantly improves the system's ability to deliver accurate and relevant answers, helping users address common security concerns with smart home IoT devices. Our experiments on real-world user concerns show that our work improves the performance of the base models.

The problem of video frame prediction has received much interest due to its relevance to many computer vision applications such as autonomous vehicles or robotics. Supervised methods for video frame prediction rely on labeled data, which may not always be available. In this paper, we provide a novel unsupervised deep-learning method called Inception-based LSTM for video frame prediction. The general idea of inception networks is to implement wider networks instead of deeper networks. This network design was shown to improve the performance of image classification. The proposed method is evaluated on both Inception-v1 and Inception-v2 structures. The proposed Inception LSTM methods are compared with convolutional LSTM when applied using PredNet predictive coding framework for both the KITTI and KTH data sets. We observed that the Inception based LSTM outperforms the convolutional LSTM. Also, Inception LSTM has better prediction performance compared to Inception v2 LSTM. However, Inception v2 LSTM has a lower computational cost compared to Inception LSTM.