S automation advances, robots are increasingly utilized for complex tasks, reducing manual labor in hazardous environments while improving efficiency, precision, and cost-effectiveness [1]. However, real-world robotic applications require seamless interaction with deformable objects, which presents significant challenges due to material flexibility and unpredictable shape changes [2]. Unlike rigid object manipulation, deformable object manipulation (DOM) requires real-time adaptive control to compensate for continuous state variations and external forces. Traditional physics-based control models, such as mass-spring systems and finite element methods [3], [4], [5], attempt to model deformable object behavior but often fall short in real-world applications due to the sensitvity of control parameters and the difficulty of modeling complex contact dynamics. To address these limitations, recent research has shifted toward machine learning and data-driven approaches, where robots learn from sensor feedback or demonstrations rather than relying on hard-coded models [6]. Predictive learning models [7], [8], [9] have proven effective for latent space learning and object behavior forecasting, improving adaptability across applications such as fabric repositioning [10], crop harvesting [11], [12], medical robotics [13], and deformable linear object manipulation [14], [15]. While significant progress has been made in DOM, little research has focused on deformable tool manipulation (DTM), which introduces additional complexities such as bending dynamics, force regulation, and stability issues.

The transition from traditional to self-driving cars is likely to take years if not decades, and Saskatchewan's least favourite form of precipitation could make that process even longer, according to one expert. Autonomous vehicles use a variety of sensors to detect objects in the environment, and the province's climate is likely to prove more challenging than that of the southern United States, said Jonathan Cliffen, an engineer with 3M. "I think the value proposition is going to be a challenge at first," said Cliffen, who leads the Maplewood, Minn.-based materials company's efforts to build signs and road markings optimized for self-driving cars in Canada. "Much like owning a motorcycle, it's going to be a two-thirds-of-the-year solution, right? Autonomous vehicles, you're going to be able to use them year-round in manual mode and hopefully six to eight months of the year you're going to be able to use it in autonomous mode."