## 3 Results
### Failure Analysis  
The failure cases for the five-finger grasp show the difficulties with complex shapes and how forces are applied. The system worked well with two- and three-finger grips but struggled with objects like the Banana, Bottle, and Ball, as seen in Table 2. These challenges primarily stem from model limitations and hardware constraints, affecting force distribution and grasp consistency.  
Each model exhibited specific weaknesses when dealing with certain object geometries. The LSTM model struggled with sudden force spikes, particularly in the Index and Middle fingers when grasping the Banana. Figure 14a shows the corresponding force profile. As the Banana is long and prone to bending, stable grasping required continuous fine-tuned force adjustments. However, the sequential nature of LSTM limited its ability to dynamically regulate forces in real-time, resulting in frequent overcompensation. The GP model, while effective in providing uncertainty-aware predictions, tended to underestimate required forces, leading to instabilities when grasping rigid objects such, as the Bottle. Repeated force fluctuations were observed in the Middle and Ring fingers due to the model's conservative force estimates, which led to unintended shifts in grasp stability. Figure 14b shows the corresponding force profile. The Transformer model demonstrated superior adaptability but encountered difficulties with the Ball, where its symmetrical surface required a precise balance of forces across all fingers. Figure 14c shows the corresponding force profile. The model occasionally struggled to maintain uniform force distribution, leading to slow adjustments and inconsistent grasp stability.  
Figure 14: Force profile for the failure cases of five-finger grasp configuration: **(a)** Banana with LSTM, **(b)** Bottle with GP, **(c)** Ball with Transformer model.  
Beyond model-specific limitations, hardware constraints also contributed to grasp failures. The robotic hand used in this study relies on embedded force sensors for force estimation but lacks distributed tactile sensing across the fingers. This limitation was particularly evident in objects such as the Tomato and Strawberry, where minor force inconsistencies caused unexpected shifts in object position. The absence of fine-grained tactile sensing restricted real-time corrective adjustments, impacting grasp stability for deformable and delicate objects. Additionally, actuation delays in the robotic fingers introduced minor inconsistencies in force application. Although the Transformer model effectively adjusted grasp forces, the hardware's response lag influenced grasp precision, especially in five-finger configurations where force needed to be dynamically redistributed.  
Addressing these challenges requires improvements in both model robustness and hardware capabilities. Enhancing the Transformer model with stability-aware grasp predictions could mitigate force inconsistencies for symmetrical objects, such as the Ball. Incorporating additional tactile sensors would improve real-time force adaptation, particularly for fragile and deformable objects, where subtle force variations significantly affect grasp stability. Moreover, optimizing grasp configuration predictions to incorporate real-time adjustments based on force feedback could further enhance grasp performance. These findings emphasize the importance of integrating advanced force prediction models with improved sensing and actuation mechanisms to achieve stable and adaptive robotic grasping.