In minimally invasive surgical robotics, catheter-scale Fiber Bragg Grating (FBG) sensors are promising due to their ability to estimate multi-dimensional forces by multiplexing several optical channels. However, deploying these compact multi-channel sensors introduces two critical engineering challenges: inherent nonlinear cross-axis coupling during complex deformations, and intermittent channel dropouts caused by fiber fractures in constrained workspaces. These compounding issues severely degrade force estimation. Existing fault-tolerant approaches rely on combinatorial model banks, which scale exponentially with the channel count and demand prohibitively expensive per-pattern calibration. In this paper, we propose a unified, self-supervised mask-aware Transformer that explicitly models channel availability to enable graceful degradation under diverse and dynamic sensor failures. The encoder is pretrained via masked-channel reconstruction on unlabeled data streams and fine-tuned for force regression using a balanced clean-and-corrupted-view objective alongside a dynamic corruption curriculum. Furthermore, a parallel uncertainty head, trained via heteroscedastic Gaussian negative log-likelihood, predicts per-axis confidence in a single forward pass, circumventing the overhead of multi-pass ensembles. Evaluated on a catheter-scale 8-channel FBG dataset, our single unified model achieves a nominal Root Mean Square Error (RMSE) of 0.0066N and degrades gracefully to 0.0126N under severe 4-channel failures. This significantly outperforms a comprehensive model bank of 255 per-pattern neural networks (0.0154~N at 4-channel loss) while eliminating pattern-specific calibration.
Robot-Assisted Minimally Invasive Surgery (RAMIS) enhances surgeon dexterity, with newer platforms leveraging haptic feedback to further improve performance. Such force information has broader potential to inform performance assessment, tactile localization, and surgical autonomy. This motivates the need for accessible approaches to integrating force sensing into RAMIS tools. This work presents a method for integrating a six-axis commercial force sensor into the distal end of a standard cable-driven surgical instrument, enabling end-effector force measurement while preserving the original mechanical functionality of the device. The proposed design emphasizes reproducibility and accessibility for research applications, requiring no specialized manufacturing tools. A transformer neural network integrates force sensor measurements with robot state information to aid estimation of applied forces at the end-effector, compensating for internal cable forces arising from actuation. Our proposed approach achieved normalized errors below 6%, and generalized to unseen conditions better than purely proximal data-driven sensing approaches. High internal cable forces caused sensor saturation and reduced axial force observability, which can degrade performance along the tool's major axis and under higher load conditions. Given current levels of performance, the balance of system integrability and performance enables applications and research into timely topics of haptic feedback, skill assessment, and force-informed autonomy in RAMIS. Videos and code are available at https://enhanced-telerobotics.github.io/shaft_force_sensing/.
Haptic feedback has been a long-missed feature in robotic-assisted surgery, one that would allow surgeons to perceive tissue properties and apply controlled forces during delicate procedures. Although commercial robotic systems have begun to integrate haptic technologies, their high costs limit accessibility for training and research purposes. To address this gap, we extend our previously developed low-cost robotic surgery training setup, RoboScope, by incorporating a wrist-mounted force/torque (F/T) sensor for haptic feedback training. Wrist-mounted sensing avoids many challenges associated with tip-mounted sensors but introduces additional non-contact forces, such as gravity, sensor bias, installation offsets, and associated torques, which compromise measurement accuracy. In this paper, we propose a robust real-time compensation method based on recursive least squares (RLS). This method eliminates the need for dataset collection and frequent recalibration while adapting to changing operating conditions. Experimental validation demonstrates that the proposed approach achieves over 95% error reduction in non-contact force compensation and more than 91% in non-contact torque compensation, significantly outperforming existing methods. These results highlight the potential of our approach for providing reliable haptic feedback in robotic surgery training and research.
Existing stretch and touch sensors for robots are generally expensive with respect to at least one of material costs, required manufacturing equipment, or manufacturing time. We present and experimentally characterize a conductive fiber made using only inexpensive commercial off-the-shelf parts (conductive thread at 0.07/ft,siliconetubingat0.94/ft) and tools (loop-style needle threader at $2), which can be manufactured quickly (20 cm length in 2 minutes.) We demonstrate its use as a resistive strain sensor with three applications: Triggering a grasp in a pneumatically actuated assistive finger, sensing the pose of a pneumatically actuated robotic strap, and estimating the pose of a flexible solid. We also demonstrate that it can be used as a capacitive sensor with two applications: First, as a touch sensor which triggers a commercial robot arm to move, and second, as a near-field sensor enabling the robot arm to follow a moving hand. The capacitive sensors are knitted, showcasing the high flexibility of the fiber. We discuss methods for improving manufacturing scalability and their cost trade-offs. Finally, we demonstrate a method for repairing a cut fiber.
Christian Diaz Herrera, Srushti Raste, Simin Liu +10