Haptic bilateral teleoperation system for free-hand dental procedures
Authors: Lorenzo Pagliara, Vincenzo Petrone, Enrico Ferrentino, Andrea Chiacchio, Giovanni Russo
Organizations: Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084 Fisciano, Italy · Specialization School in Oral Surgery, University of Naples Federico II, 80138 Napoli, Italy
Free-hand dental procedures are typically repetitive, time-consuming and require high precision and manual dexterity. Robots can play a key role in improving procedural accuracy and safety, enhancing patient comfort, and reducing operator workload. However, robotic solutions for free-hand procedures remain limited or completely lacking. To address this gap, we develop a haptic bilateral teleoperation system (HBTS) for free-hand dental procedures (FH-HBTS). The system includes a mechanical end-effector, compatible with standard clinical tools, and equipped with an endoscopic camera for improved visibility of the intervention site. By ensuring motion and force correspondence between the operator's and the robot's actions, monitored through visual feedback, we enhance the operator's sensory awareness and motor accuracy. Furthermore, to ensure procedural safety, we limit interaction forces by scaling the motion references provided to the admittance controller based solely on measured contact forces. This ensures effective force limitation in all contact states without requiring prior knowledge of the environment. The proposed FH-HBTS is validated through a technical evaluation and an in-vitro pre-clinical study conducted on a dental model under clinically representative, yet simplified, conditions. The results show that the system improves the naturalness, safety, and accuracy of teleoperation, highlighting its potential to enhance free-hand dental procedures.
Algorithm 1 Eye-hand coordination mapping and scaling
Fig. 6: Integrated mechanical end-effector for free-hand procedures. Visible in the left side of the figure is also one of the dental models used in the experiments.
Fig. 7: Comparison between the conditional expectation of the interaction force in all three scenarios.
Fig. 8: Eye-hand coordination assessment.
Group 1
Group 2
Group 3
K–W test
Mean ± STD
Mean ± STD
Mean ± STD
χ2
p
NASA-TLX
Mental Demand
30.00±27.57
51.43±15.74
36.67±23.38
2.91
0.23
Physical Demand
26.67±24.22
20.00±11.55
16.67±19.66
0.70
0.70
Temporal Demand
36.67±23.38
42.86±13.80
36.67±15.06
0.52
0.77
Performance
43.33±8.17
37.14±13.80
33.33±10.33
2.42
0.30
TABLE I: Summary of the NASA-TLX and 5-point Likert scale questionnaire responses and SUS scores. NASA-TLX subscales are reported on their native 0–100 scale; the Overall row is the average of the six subscales. Likert-scale items are reported on a 1–5 scale. For the Ergonomics item, the scale was anchored relative to the participant’s experience of manual execution, so that 3 denotes the neutral midpoint (perceived parity with manual practice), while scores above/below 3 denote a perceived improvement/deterioration relative to manual practice.
In robot teleoperation, haptic feedback can be used to help human operators accomplish dexterous manipulation tasks. However, existing haptic feedback methods try to replicate high-fidelity sensory haptics that are felt in real world interactions, which are constrained by the sensing and feedback hardware capability and may lead to higher workload. To addresses these limitations, this work introduces semantic haptics for teleoperation, which uses abstract haptic patterns to convey critical information about robot states. We categorize robot states into "Confirmations" and "Exceptions", implement a modular haptic rendering pipeline in robot simulation, and deliver semantic haptic feedback to operators through pneumatic and vibrotactile wristbands. This simplifies hardware requirements and enables one-to-many mappings between haptic patterns and robot states. Through three evaluation studies, we identify the most effective semantic haptic design for a common pick and place teleoperation task and compare semantic haptics to other teleoperation feedback approaches including sensory haptics and visual feedback. Results suggest that while semantic haptics performs similarly as other feedback in unimanual tasks, it achieves superior performance in bimanual tasks, with reduced task workload, increased situational awareness, and overall preference.
Tooth preparation refers to the controlled removal of tooth structure to create an optimal substrate for fixed restorations and is a core procedure in restorative dentistry. Automating this task is particularly challenging for robots because the dental bur must operate within a densely constrained intraoral workspace, where even sub-millimeter deviations can compromise outcomes or damage adjacent structures. This paper presents a novel robotic system for autonomous full-crown tooth preparation. The proposed framework includes: 1) an anatomy-aware toolpath planning algorithm that conforms precisely to a technician-designed preparation model while protecting adjacent teeth, and 2) a clearance-oriented end-effector yaw assignment strategy that allows intraoral access while reducing the risk of soft-tissue interference. Together, these features enable the robot to accurately mill the irregular tooth surface with an average geometric deviation of 0.117 mm (RMSE), achieving both restoration quality and clinical safety. A series of simulations and phantom-head experiments validate the system's feasibility and effectiveness.
Yunwen Li, Chen Chen, Xiangjie Yan +4
Department of Automation, Tsinghua University, China · Department of Mechanical and Process Engineering, ETH Zürich, Switzerland · Tsingscribe Medical Ltd., China +1
Collecting high-quality robot data for contact-rich manipulation tasks is essential for enabling robots to acquire real-world skills. However, existing data collection solutions often lack the capability to obtain stable and high-frequency tactile feedback, limiting their effectiveness in contact-rich manipulation scenarios. In this work, we propose a versatile teleoperation system with tactile-driven assistance to enable high-frequency and stable contact-rich manipulation. The proposed XRoboToolKit-T teleoperation system incorporates a tactile-informed force control architecture, designed to ensure both stable and precise force control in contact-rich manipulation during teleoperation. The stabilizer haptic module rapidly analyzes the normal force distribution and infers pseudo shear force, enabling real-time tactile-based assistance during manipulation. The refiner haptic module integrates a vision-language-action model to predict and refine manipulation actions based on tactile sensing data and task descriptions. We apply the proposed teleoperation system to challenging contact-rich manipulation tasks, including grasping a deformable rubber pipette for liquid transfer and inserting a medical syringe into a vascular training pad, to demonstrate the effectiveness of tactile-informed force control. Furthermore, the system achieves higher data collection efficiency and improved manipulation stability compared to state-of-the-art teleoperation without tactile assistance.
Xiwen Dengxiong, Xueting Wang, Ke Jing +2
Rochester Institute of Technology, Rochester, NY · TikTok Pico Lab, San Jose, CA · The Hong Kong University of Science and Technology, Guangzhou, China