Mass-Adaptive Admittance Control for Robotic Manipulators

Handling objects with unknown or changing masses is a common challenge in robotics, often leading to errors or instability if the control system cannot adapt in real-time. In this paper, we present a novel approach that enables a six-degrees-of-freedom robotic manipulator to reliably follow waypoints while automatically estimating and compensating for unknown payload weight. Our method integrates an admittance control framework with a mass estimator, allowing the robot to dynamically update an excitation force to compensate for the payload mass. This strategy mitigates end-effector sagging and preserves stability when handling objects of unknown weights. We experimentally validated our approach in a challenging pick-and-place task on a shelf with a crossbar, improved accuracy in reaching waypoints and compliant motion compared to a baseline admittance-control scheme. By safely accommodating unknown payloads, our work enhances flexibility in robotic automation and represents a significant step forward in adaptive control for uncertain environments.

Simultaneous Collision Detection and Force Estimation for Dynamic Quadrupedal Locomotion

In this paper we address the simultaneous collision detection and force estimation problem for quadrupedal locomotion using joint encoder information and the robot dynamics only. We design an interacting multiple-model Kalman filter (IMM-KF) that estimates the external force exerted on the robot and multiple possible contact modes. The method is invariant to any gait pattern design. Our approach leverages pseudo-measurement information of the external forces based on the robot dynamics and encoder information. Based on the estimated contact mode and external force, we design a reflex motion and an admittance controller for the swing leg to avoid collisions by adjusting the leg's reference motion. Additionally, we implement a force-adaptive model predictive controller to enhance balancing. Simulation ablatation studies and experiments show the efficacy of the approach.

CATCH-FORM-3D: Compliance-Aware Tactile Control and Hybrid Deformation Regulation for 3D Viscoelastic Object Manipulation

This paper investigates a framework (CATCH-FORM-3D) for the precise contact force control and surface deformation regulation in viscoelastic material manipulation. A partial differential equation (PDE) is proposed to model the spatiotemporal stress-strain dynamics, integrating 3D Kelvin-Voigt (stiffness-damping) and Maxwell (diffusion) effects to capture the material's viscoelastic behavior. Key mechanical parameters (stiffness, damping, diffusion coefficients) are estimated in real time via a PDE-driven observer. This observer fuses visual-tactile sensor data and experimentally validated forces to generate rich regressor signals. Then, an inner-outer loop control structure is built up. In the outer loop, the reference deformation is updated by a novel admittance control law, a proportional-derivative (PD) feedback law with contact force measurements, ensuring that the system responds adaptively to external interactions. In the inner loop, a reaction-diffusion PDE for the deformation tracking error is formulated and then exponentially stabilized by conforming the contact surface to analytical geometric configurations (i.e., defining Dirichlet boundary conditions). This dual-loop architecture enables the effective deformation regulation in dynamic contact environments. Experiments using a PaXini robotic hand demonstrate sub-millimeter deformation accuracy and stable force tracking. The framework advances compliant robotic interactions in applications like industrial assembly, polymer shaping, surgical treatment, and household service.

On the Analysis of Stability, Sensitivity and Transparency in Variable Admittance Control for pHRI Enhanced by Virtual Fixtures

The interest in Physical Human-Robot Interaction (pHRI) has significantly increased over the last two decades thanks to the availability of collaborative robots that guarantee user safety during force exchanges. For this reason, stability concerns have been addressed extensively in the literature while proposing new control schemes for pHRI applications. Because of the nonlinear nature of robots, stability analyses generally leverage passivity concepts. On the other hand, the proposed algorithms generally consider ideal models of robot manipulators. For this reason, the primary objective of this paper is to conduct a detailed analysis of the sources of instability for a class of pHRI control schemes, namely proxy-based constrained admittance controllers, by considering parasitic effects such as transmission elasticity, motor velocity saturation, and actuation delay. Next, a sensitivity analysis supported by experimental results is carried out, in order to identify how the control parameters affect the stability of the overall system. Finally, an adaptation technique for the proxy parameters is proposed with the goal of maximizing transparency in pHRI. The proposed adaptation method is validated through both simulations and experimental tests.

Digital Modeling of Massage Techniques and Reproduction by Robotic Arms

This paper explores the digital modeling and robotic reproduction of traditional Chinese medicine (TCM) massage techniques. We adopt an adaptive admittance control algorithm to optimize force and position control, ensuring safety and comfort. The paper analyzes key TCM techniques from kinematic and dynamic perspectives, and designs robotic systems to reproduce these massage techniques. The results demonstrate that the robot successfully mimics the characteristics of TCM massage, providing a foundation for integrating traditional therapy with modern robotics and expanding assistive therapy applications.

Teleoperation in Robot-assisted MIS with Adaptive RCM via Admittance Control

This paper presents the development and assessment of a teleoperation framework for robot-assisted minimally invasive surgery (MIS). The framework leverages our novel integration of an adaptive remote center of motion (RCM) using admittance control. This framework operates within a redundancy resolution method specifically designed for the RCM constraint. We introduce a compact, low-cost, and modular custom-designed instrument module (IM) that ensures integration with the manipulator, featuring a force-torque sensor, a surgical instrument, and an actuation unit for driving the surgical instrument. The paper details the complete teleoperation framework, including the telemanipulation trajectory mapping, kinematic modelling, control strategy, and the integrated admittance controller. Finally, the system capability to perform various surgical tasks was demonstrated, including passing a thread through the rings, picking and placing objects, and trajectory tracking.

Efficient and Safe Contact-rich pHRI via Subtask Detection and Motion Estimation using Deep Learning

This paper proposes an adaptive admittance controller for improving efficiency and safety in physical human-robot interaction (pHRI) tasks in small-batch manufacturing that involve contact with stiff environments, such as drilling, polishing, cutting, etc. We aim to minimize human effort and task completion time while maximizing precision and stability during the contact of the machine tool attached to the robot's end-effector with the workpiece. To this end, a two-layered learning-based human intention recognition mechanism is proposed, utilizing only the kinematic and kinetic data from the robot and two force sensors. A ``subtask detector" recognizes the human intent by estimating which phase of the task is being performed, e.g., \textit{Idle}, \textit{Tool-Attachment}, \textit{Driving}, and \textit{Contact}. Simultaneously, a ``motion estimator" continuously quantifies intent more precisely during the \textit{Driving} to predict when \textit{Contact} will begin. The controller is adapted online according to the subtask while allowing early adaptation before the \textit{Contact} to maximize precision and safety and prevent potential instabilities. Three sets of pHRI experiments were performed with multiple subjects under various conditions. Spring compression experiments were performed in virtual environments to train the data-driven models and validate the proposed adaptive system, and drilling experiments were performed in the physical world to test the proposed methods' efficacy in real-life scenarios. Experimental results show subtask classification accuracy of 84\% and motion estimation R\textsuperscript{2} score of 0.96. Furthermore, 57\% lower human effort was achieved during \textit{Driving} as well as 53\% lower oscillation amplitude at \textit{Contact} as a result of the proposed system.

Admittance Control for Adaptive Remote Center of Motion in Robotic Laparoscopic Surgery

In laparoscopic robot-assisted minimally invasive surgery, the kinematic control of the robot is subject to the remote center of motion (RCM) constraint at the port of entry (e.g., trocar) into the patient's body. During surgery, after the instrument is inserted through the trocar, intrinsic physiological movements such as the patient's heartbeat, breathing process, and/or other purposeful body repositioning may deviate the position of the port of entry. This can cause a conflict between the registered RCM and the moved port of entry. To mitigate this conflict, we seek to utilize the interaction forces at the RCM. We develop a novel framework that integrates admittance control into a redundancy resolution method for the RCM kinematic constraint. Using the force/torque sensory feedback at the base of the instrument driving mechanism (IDM), the proposed framework estimates the forces at RCM, rejects forces applied on other locations along the instrument, and uses them in the admittance controller. In this paper, we report analysis from kinematic simulations to validate the proposed framework. In addition, a hardware platform has been completed, and future work is planned for experimental validation.

A Cooperation Control Framework Based on Admittance Control and Time-varying Passive Velocity Field Control for Human--Robot Co-carrying Tasks

Human--robot co-carrying tasks demonstrate their potential in both industrial and everyday applications by leveraging the strengths of both parties. Effective control of robots in these tasks requires minimizing position and velocity errors to complete the shared tasks while also managing the energy level within the closed-loop systems to prevent potential dangers such as instability and unintended force exertion. However, this collaboration scenario poses numerous challenges due to varied human intentions in adapting to workspace characteristics, leading to human--robot conflicts and safety incidents. In this paper, we develop a robot controller that enables the robot partner to re-plan its path leveraging conflict information, follow co-carrying motions accurately, ensure passivity, and regular the energy of the closed-loop system. A cooperation control framework for human--robot co-carrying tasks is constructed by utilizing admittance control and time-varying Passive Velocity Field Control with a fractional exponent energy compensation control term. By measuring the interaction force, the desired trajectory of co-carrying tasks for the robot partner is first generated using admittance control. Thereafter, the new Passive Velocity Field Control with the energy compensation feature is designed to track the desired time-varying trajectory and guarantee passivity. Furthermore, the proposed approach ensures that the system's kinetic energy converges to the desired level within a finite time interval, which is critical for time-critical applications. Numerical simulation demonstrates the efficiency of the proposed cooperation control method through four collaborative transportation scenarios.

Design and Control of a Low-cost Non-backdrivable End-effector Upper Limb Rehabilitation Device

This paper presents the development of an upper limb end-effector based rehabilitation device for stroke patients, offering assistance or resistance along any 2-dimensional trajectory during physical therapy. It employs a non-backdrivable ball-screw-driven mechanism for enhanced control accuracy. The control system features three novel algorithms: First, the Implicit Euler velocity control algorithm (IEVC) highlighted for its state-of-the-art accuracy, stability, efficiency and generalizability in motion restriction control. Second, an Admittance Virtual Dynamics simulation algorithm that achieves a smooth and natural human interaction with the non-backdrivable end-effector. Third, a generalized impedance force calculation algorithm allowing efficient impedance control on any trajectory or area boundary. Experimental validation demonstrated the system's effectiveness in accurate end-effector position control across various trajectories and configurations. The proposed upper limb end-effector-based rehabilitation device, with its high performance and adaptability, holds significant promise for extensive clinical application, potentially improving rehabilitation outcomes for stroke patients.