Abstract--Humans naturally grasp objects with minimal level required force for stability, whereas robots often rely on rigid, over-squeezing control. T o narrow this gap, we propose a human-inspired physics-conditioned tactile method (Phy-T ac) for force-optimal stable grasping (FOSG) that unifies pose selection, tactile prediction, and force regulation. A physics-based pose selector first identifies feasible contact regions with optimal force distribution based on surface geometry. Then, a physics-conditioned latent diffusion model (Phy-LDM) predicts the tactile imprint under FOSG target. Last, a latent-space LQR controller drives the gripper toward this tactile imprint with minimal actuation, preventing unnecessary compression. Trained on a physics-conditioned tactile dataset covering diverse objects and contact conditions, the proposed Phy-LDM achieves superior tactile prediction accuracy, while the Phy-T ac outperforms fixed-force and GraspNet-based baselines in grasp stability and force efficiency. Experiments on classical robotic platforms demonstrate force-efficient and adaptive manipulation that bridges the gap between robotic and human grasping.

Robotic manipulation demands precise control over both contact forces and motion trajectories. While force control is essential for achieving compliant interaction and high-frequency adaptation, it is limited to operations in close proximity to the manipulated object and often fails to maintain stable orientation during extended motion sequences. Conversely, optimization-based motion planning excels in generating collision-free trajectories over the robot's configuration space but struggles with dynamic interactions where contact forces play a crucial role. To address these limitations, we propose a multi-modal control framework that combines force control and optimization-augmented motion planning to tackle complex robotic manipulation tasks in a sequential manner, enabling seamless switching between control modes based on task requirements. Our approach decomposes complex tasks into subtasks, each dynamically assigned to one of three control modes: Pure optimization for global motion planning, pure force control for precise interaction, or hybrid control for tasks requiring simultaneous trajectory tracking and force regulation. This framework is particularly advantageous for bimanual and multi-arm manipulation, where synchronous motion and coordination among arms are essential while considering both the manipulated object and environmental constraints. We demonstrate the versatility of our method through a range of long-horizon manipulation tasks, including single-arm, bimanual, and multi-arm applications, highlighting its ability to handle both free-space motion and contact-rich manipulation with robustness and precision.

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.