Reliable control of upper-limb prostheses remains challenging because muscle signals can change between sessions and wearable EMG sensors may suffer from poor electrode contact. This study presents a real-time prosthetic hand control platform based on an OYMotion gForce Pro armband and a five-finger Dynamixel-actuated prosthetic hand. The system is designed to work without an external dataset, using only a short user-specific recording session and brief calibration before real-time use — specifically, five main training repetitions plus four supplementary calibration repetitions per gesture — intentionally minimising the calibration burden to enable practical single-session deployment. Five hand gestures—power grasp, open hand, OK gesture, index pointing, and thumbs-up—were classified using several machine-learning and ensemble-based methods. All models were evaluated under the same live data stream to ensure a fair comparison. Six recording sessions collected across five able-bodied participants on different recording days were included in the final statistical analysis. For every session, the main and supplementary training samples were combined to train session-specific models before live evaluation. Across six independent recording sessions from one able-bodied participant, Linear Discriminant Analysis achieved the best average live accuracy of 85.9%, followed closely by majority voting and adaptive ensemble methods. In contrast, the LSTM model showed lower performance, reaching 70.2% accuracy. The results indicate that, for low-data real-time prosthetic control, ensemble-based methods preserved near-best average accuracy while reducing reliance on any single classifier assumption, making them a more robust deployment choice than individual classifiers. Statistical analysis confirmed that session-to-session variability was the dominant factor in system performance (Kendall's W = 0.249) rather than classifier choice (W = 0.043), suggesting that practical reliability depends more on robust calibration and signal monitoring than on model selection.

Autonomous semantic indoor navigation requires robust low-level control and high-level understanding of objects and spatial context in cluttered and partially occluded environments. While deep reinforcement learning (DRL) methods such as twin delayed deep deterministic policy gradient (TD3) enable reactive obstacle avoidance, they typically struggle with long-horizon semantic navigation, where object-location memory and language-level reasoning are required. We present a lightweight hierarchical two-stage framework that, to the best of our knowledge, is introduced for the first time to integrate a locally deployed vision–language model (VLM), semantic object coordinate memory, and a TD3-based DRL controller for language-conditioned indoor navigation. In Stage 1, the robot performs semantic exploration using odometry, 2D LiDAR, and VLM-based object recognition to build a geometric map and store detected object categories with their estimated world coordinates in a structured javaScript object notation (JSON) semantic memory. In Stage 2, a natural language query is used to retrieve the target object coordinates from memory and pass them to a TD3 target point navigation policy, which performs mapless navigation using odometry and RealSense RGB-D perception. The proposed framework combines open-vocabulary VLM-based object coordinate estimation, LiDAR mapping, RGB-D perception, and language grounding within a unified semantic memory representation. Experiments in a ROS-integrated realistic simulation demonstrate consistent goal-reaching performance and improved navigation efficiency compared with an Artificial Potential Field baseline using the same VLM and a DRL + GPT-4o mini configuration. We also compare the proposed VLM-based recognition module with YOLO-World v2.6 and Grounding DINO, showing that the VLM-based approach provides more reliable semantic grounding and target-coordinate estimation in the tested indoor navigation scenarios, particularly for flexible natural language object queries.