Accurate and efficient modeling of soft-tissue interactions is fundamental for advancing surgical simulation, surgical robotics, and model-based surgical automation. To achieve real-time latency, classical Finite Element Method (FEM) solvers are often replaced with neural approximations; however, naively training such models in a fully data-driven manner without incorporating physical priors frequently leads to poor generalization and physically implausible predictions. We present a novel physics-informed neural simulation framework that enables real-time prediction of soft-tissue deformations under complex single- and multi-grasper interactions. Our approach integrates Kelvinlet-based analytical priors with large-scale FEM data, capturing both linear and nonlinear tissue responses. This hybrid design improves predictive accuracy and physical plausibility across diverse neural architectures while maintaining the low-latency performance required for interactive applications. We validate our method on challenging surgical manipulation tasks involving standard laparoscopic grasping tools, demonstrating substantial improvements in deformation fidelity and temporal stability over existing baselines. These results establish Kelvinlet-augmented learning as a principled and computationally efficient paradigm for real-time, physics-aware soft-tissue simulation in surgical AI.

Haptic devices have shown to be valuable in supplementing surgical training, especially when providing haptic feedback based on user performance metrics such as wrench applied by the user on the tool. However, current 6-axis force/torque sensors are prohibitively expensive. This paper presents the design and calibration of a low-cost, six-axis force/torque sensor specially designed for laparoscopic haptic training applications. The proposed design uses Hall-effect sensors to measure the change in the position of magnets embedded in a silicone layer that results from an applied wrench to the device. Preliminary experimental validation demonstrates that these sensors can achieve an accuracy of 0.45 N and 0.014 Nm, and a theoretical XY range of ±50N, Z range of ±20N, and torque range of ±0.2Nm. This study indicates that the proposed low-cost 6-axis force/torque sensor can accurately measure user force and provide useful feedback during laparoscopic training on a haptic device.

Robots often have to perform manipulation tasks in close proximity to people (Fig 1). As such, it is desirable to use a robot arm that has limited joint torques so as to not injure the nearby person. Unfortunately, these limited torques then limit the payload capability of the arm. By using contact with the environment, robots can expand their reachable workspace that, otherwise, would be inaccessible due to exceeding actuator torque limits. We adapt our recently developed INSAT algorithm [1] to tackle the problem of torque-limited whole arm manipulation planning through contact. INSAT requires no prior over contact mode sequence and no initial template or seed for trajectory optimization. INSAT achieves this by interleaving graph search to explore the manipulator joint configuration space with incremental trajectory optimizations seeded by neighborhood solutions to find a dynamically feasible trajectory through contact. We demonstrate our results on a variety of manipulators and scenarios in simulation. We also experimentally show our planner exploiting robot-environment contact for the pick and place of a payload using a Kinova Gen3 robot. In comparison to the same trajectory running in free space, we experimentally show that the utilization of bracing contacts reduces the overall torque required to execute the trajectory.

Continuum robots suffer large deflections due to internal and external forces. Accurate modeling of their passive compliance is necessary for accurate environmental interaction, especially in scenarios where direct force sensing is not practical. This paper focuses on deriving analytic formulations for the compliance of continuum robots that can be modeled as Kirchhoff rods. Compared to prior works, the approach presented herein is not subject to the constant-curvature assumptions to derive the configuration space compliance, and we do not rely on computationally-expensive finite difference approximations to obtain the task space compliance. Using modal approximations over curvature space and Lie group integration, we obtain closed-form expressions for the task and configuration space compliance matrices of continuum robots, thereby bridging the gap between constant-curvature analytic formulations of configuration space compliance and variable curvature task space compliance. We first present an analytic expression for the compliance of a single Kirchhoff rod. We then extend this formulation for computing both the task space and configuration space compliance of a tendon-actuated continuum robot. We then use our formulation to study the tradeoffs between computation cost and modeling accuracy as well as the loss in accuracy from neglecting the Jacobian derivative term in the compliance model. Finally, we experimentally validate the model on a tendon-actuated continuum segment, demonstrating the model's ability to predict passive deflections with error below 11.5% percent of total arc length.

In the future, human-robot interaction will include collaboration in close-quarters where the environment geometry is partially unknown. As a means for enabling such interaction, this paper presents a multi-modal sensor array capable of contact detection and localization, force sensing, proximity sensing, and mapping. The sensor array integrates Hall effect and time-of-flight (ToF) sensors in an I2C communication network. The design, fabrication, and characterization of the sensor array for a future in-situ collaborative continuum robot are presented. Possible perception benefits of the sensor array are demonstrated for accidental contact detection, mapping of the environment, selection of admissible zones for bracing, and constrained motion control of the end effector while maintaining a bracing constraint with an admissible rolling motion.