## 1 Introduction  
The manipulation of objects with varying shapes, sizes, and fragilities presents significant challenges in robotic automation [1, 2]. Unlike rigid industrial objects, delicate or deformable items are prone to damage from excessive force or poor grasping techniques [3, 4]. Traditional grippers often lack adaptability and compliance, resulting in deformation or damage [5]. As a result, industries still depend on manual labour, which is costly and error prone. Achieving human-like dexterity and sensitivity in robotic systems is critical for automating such tasks [6, 7]. Humans excel at object manipulation by integrating tactile feedback, vision, and cognitive strategies to adapt to grasp forces based on object properties [8]. Reproducingthese abilities in robotics requires dexterous hands, tactile and visual sensors, and adaptive control algorithms [9]. Advances in robotic vision, such as YOLOv8, enable real-time detection, segmentation, and pose estimation, supporting precise grasping [1]. However, tactile sensing and predictive models remain essential for addressing grasp force modulation and stability challenges [6,7] Gaussian Processes (GPs) have become a powerful tool in learning from demonstration (LfD), offering a non-parametric Bayesian framework to capture predictive accuracy and uncertainty [10, 11, 12]. GPs address variability in demonstrations by modelling precise trajectories in low-variability regions and compliance in high-variability ones [13], enabling smooth trajectory planning under uncertainty [14]. Recent advancements include integrating expert and novice demonstrations for robust policy learning, even with noisy data [15]. Frameworks like PILCO utilize GPs for system dynamics modelling and uncertainty-aware optimization, achieving data efficiency in reinforcement learning (RL) tasks [16]. Combining GPs with RL allows self-exploration to refine motion skills, enhancing robustness and safety through dynamic model updates based on uncertainty estimates [17,18]. In vision-based systems, GPs bridge perception and action for cost-effective, adaptable manipulation [10,11]. Transformers have recently addressed challenges in LfD and time-series analysis, particularly for generalization and long-term dependencies. For multi-task manipulation, parameter sharing and auxiliary objectives enhance generalization [19], while hierarchical Transformers tackle sparse rewards and long-horizon tasks through sub-goal planning [20]. Large-scale Task and Motion Planning (TAMP) datasets train visuomotor Transformer policies for extended manipulation tasks [21]. Techniques like action chunking and data augmentation enable robust single-demonstration cloning [22]. For multi-modal inputs, Transformers outperform LSTMs and CNNs in managing extended sequences for decision-making tasks [23]. They also improve dynamic prediction in industrial robots by capturing temporal and attribute dependencies more effectively [24]. In time-series forecasting, Transformers leverage convolutional self-attention and memory-efficient architectures to handle long sequences with reduced computational costs [25], as seen in the Informer model [26]. In robotic grasping, Transformers integrate tactile and visual data to develop robust strategies for deformable objects, surpassing traditional CNN+LSTM approaches [27]. In summary, Gaussian Processes and Transformers address key challenges in robotic manipulation and LfD. GPs enable uncertainty-aware, data-efficient learning, while Transformers excel in handling long-term dependencies and multi-modal inputs. Combining these tools can advance adaptive, robust robotic systems capable of human-like manipulation and decision-making. In addition, a Random Forest-based classifier enables the system to dynamically select optimal grasp configurations (e.g., two-, three-, or five-finger grasps) based on object-specific features like geometry and texture.