Acoustic Feedback for Closed-Loop Force Control in Robotic Grinding
Authors: Zongyuan Zhang, Christopher Lehnert, Will N. Browne, Jonathan M. Roberts
Organizations: School of Electrical Engineering and Robotics, Queensland University of Technology, 2 George St, Brisbane, 4000, Queensland, Australia · Australian Cobotics Centre, Queensland University of Technology, 2 George St, Brisbane, 4000, Queensland, Australia · Centre for Robotics, Queensland University of Technology, 2 George St, Brisbane, 4000, Queensland, Australia
Acoustic feedback is a critical indicator for assessing the contact condition between the tool and the workpiece when humans perform grinding tasks with rotary tools. In contrast, robotic grinding systems typically rely on force sensing, with acoustic information largely ignored. This reliance on force sensors is costly and difficult to adapt to different grinding tools, whereas audio sensors (microphones) are low-cost and can be mounted on any medium that conducts grinding sound. This paper introduces a low-cost Acoustic Feedback Robotic Grinding System (AFRG) that captures audio signals with a contact microphone, estimates grinding force from the audio in real time, and enables closed-loop force control of the grinding process. Compared with conventional force-sensing approaches, AFRG achieves a 4-fold improvement in consistency across different grinding disc conditions. AFRG relies solely on a low-cost microphone, which is approximately 200-fold cheaper than conventional force sensors, as the sensing modality, providing an easily deployable, cost-effective robotic grinding solution.
Figures & tables
Fig. 1: Acoustic Feedback Robotic Grinding System (AFRG) uses acoustic signals for closed-loop force control in robotic grinding. Instead of expensive force sensors, AFRG employs a cost-effective contact microphone to estimate the grinding force. The workflow involves audio recording, signal processing, and regression to estimate the force, which is then used as feedback to regulate the grinding process. An example of the grinding result is shown below the figure, where the uniform grinding depth demonstrates a consistent Material Removal Rate (MRR).
Fig. 2: (a) The forces, torque, and velocity of the tool relative to the workpiece surface (side view). (b) Different conditions of the grinding disc on the rotary tool during operation (top view).
Fig. 3: Schematic of the AFRG. It consists of three components: Real-Time PSD Encoder for data processing, PSDRegNet for force estimation, and the Force-Position Hybrid Controller for closed-loop manipulator control. Here, ti denotes a given time step, Xi is the stacked PSD encoder output over the n -frame receptive field, Fi represents the estimated normal force, Pi is the end-effector position, and Ui is the joint velocity commands.
Layer
Output Size
Details
Input PSD array
1×35×40
–
Conv2D
16×33×38
kernel=5, padding=1
BatchNorm2D
16×33×38
–
ReLU
16×33×38
–
Conv2D
32×31×36
kernel=5, padding=1
BatchNorm2D
32×31×36
–
TABLE I: Network architecture for PSDRegNet
Fig. 4: Experimental setup consisting of a 6-DOF UR5 robotic arm, an F/T sensor, a rotary grinding tool with an aluminium oxide disc, a contact microphone, and rectangular workpiece bars made of aluminium alloy or stainless steel. This setup allows grinding to be performed using either F/T sensor-measured forces or acoustically estimated forces, thereby providing a reliable benchmark to evaluate the proposed method.
Fig. 5: Representative dataset samples are shown for continuous (left) and intermittent (right) grinding. The force axis is inverted to better visualise the correlation between the PSD arrays and the force signal, which are not directly aligned.
Setting
Value
Loss function
MSE loss
Optimizer
Adam
Weight decay
1×10−4
Train/validation split
80% / 20%
Number of epochs
5
Batch size
64
TABLE II: Training settings for PSDRegNet
Fig. 6: Force estimation results. (a) Distribution of measured and estimated forces, where closer overlap indicates higher accuracy. (b) Residual distribution, which ideally follows a normal distribution. (c) Scatter plot of estimated versus measured forces, where clustering along the x=y line indicates better accuracy. Outliers are highlighted in orange.
Fig. 7: Workpieces (left side of each image) after grinding along a straight-line trajectory from top to bottom, horizontally stretched for clarity. (a, b) 3 N target force: (a) annotated, (b) unannotated. (c, d) 4 N target force: (c) annotated, (d) unannotated. (e) Image in original aspect ratio corresponding to (d).
Fig. 8: Force control performance of AFRG during a straight-line grinding. The measured normal force is compared to the target force, with annotations indicating rise time, overshoot, and steady-state error.
Fig. 9: Absolute error between estimated and measured forces during straight-line grinding with AFRG, evaluated within the steady-state window.
Fig. 10: Workpiece surfaces after 1–5 fixed-point grinding trials, horizontally stretched for clarity. (a, b) F/T sensor-based force control: (a) annotated, (b) unannotated. (c, d) AFRG: (c) annotated, (d) unannotated. AFRG shows significantly improved MRR stability, with grinding depth variation approximately four times smaller compared to F/T sensor-based control.
Force-controlled robotic manipulation studies how robots regulate contact forces and compliance to grasp, assemble, and work safely with objects and surfaces.
Robotic grinding is widely used for shaping workpieces in manufacturing, but it remains difficult to automate this process efficiently. In particular, efficiently grinding workpieces of different shapes and material hardness is challenging because removal resistance varies with local contact conditions. Moreover, it is difficult to achieve accurate estimation of removal resistance and analytical modeling of shape transition, and learning-based approaches often require large amounts of training data to cover diverse processing conditions. To address these challenges, we decompose robotic grinding into two components: removal-shape planning and contact-force adaptation. Based on this formulation, we propose DecompGrind, a framework that combines Global Cutting-Surface Planning (GCSP) and Local Contact-Force Adaptation (LCFA). GCSP determines removal shapes through geometric analysis of the current and target shapes without learning, while LCFA learns a contact-force adaptation policy using bilateral control-based imitation learning during the grinding of each removal shape. This decomposition restricts learning to local contact-force adaptation, allowing the policy to be learned from a small number of demonstrations, while handling global shape transition geometrically. Experiments using a robotic grinding system and 3D-printed workpieces demonstrate efficient robotic grinding of workpieces having different shapes and material hardness while maintaining safe levels of contact force.
Shunsuke Araki, Takumi Hachimine, Yuki Saito +3
Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), Nara, Japan. · Haptics Research Center, Keio University, Kanagawa, Japan. · Graduate School of Informatics, Kyoto University, Kyoto, Japan. +1
Universal jamming grippers excel at grasping unknown objects due to their compliant bodies. Traditional tactile sensors can compromise this compliance, reducing grasping performance. We present acoustic sensing as a form of morphological sensing, where the gripper's soft body itself becomes the sensor. A speaker and microphone are placed inside the gripper cavity, away from the deformable membrane, fully preserving compliance. Sound propagates through the gripper and object, encoding object properties, which are then reconstructed via machine learning. Our sensor achieves high spatial resolution in sensing object size (2.6 mm error) and orientation (0.6 deg error), remains robust to external noise levels of 80 dBA, and discriminates object materials (up to 100% accuracy) and 16 everyday objects (85.6% accuracy). We validate the sensor in a realistic tactile object sorting task, achieving 53 minutes of uninterrupted grasping and sensing, confirming the preserved grasping performance. Finally, we demonstrate that disentangled acoustic representations can be learned, improving robustness to irrelevant acoustic variations.
Lion Weber, Theodor Wienert, Martin Splettstößer +2
Robotics and Biology Laboratory, Technische Universität Berlin · Science of Intelligence, Research Cluster of Excellence, Berlin · Robotics Institute Germany
Reliable in-hand manipulation requires accurate real-time estimation of slip between a gripper and a grasped object. Existing tactile sensing approaches based on vision, capacitance, or force-torque measurements face fundamental trade-offs in form factor, durability, and their ability to jointly estimate slip direction and magnitude. We present A-SLIP, a multi-channel acoustic sensing system integrated into a parallel-jaw gripper for estimating continuous slip in the grasp plane. The A-SLIP sensor consists of piezoelectric microphones positioned behind a textured silicone contact pad to capture structured contact-induced vibrations. The A-SLIP model processes synchronized multi-channel audio as log-mel spectrograms using a lightweight convolutional network, jointly predicting the presence, direction, and magnitude of slip. Across experiments with robot- and externally induced slip conditions, the fine-tuned four-microphone configuration achieves a mean absolute directional error of 14.1 degrees, outperforms baselines by up to 12 percent in detection accuracy, and reduces directional error by 32 percent. Compared with single-microphone configurations, the multi-channel design reduces directional error by 64 percent and magnitude error by 68 percent, underscoring the importance of spatial acoustic sensing in resolving slip direction ambiguity. We further evaluate A-SLIP in closed-loop reactive control and find that it enables reliable, low-cost, real-time estimation of in-hand slip. Project videos and additional details are available at https://a-slip.github.io.
Uksang Yoo, Yuemin Mao, Jean Oh +1
Robotics Institute, Carnegie Mellon University, USA