An adaptive scheme that estimates the kinematic relationship between a robot's joints and the task features it senses online, using only joint-angle sensing and a wrist-mounted force/torque sensor, with no exteroceptive measurement of the tool tip is developed.
Abstract
Contact-rich robotic manipulation requires an accurate model of the kinematic relationship between a robot's joints and the task features it senses. This relationship is rarely known exactly: it changes with each tool the robot picks up and shifts, sometimes almost instantaneously, as contact modes change --- especially for multi-fingered hands that make and break contact at points that are not exactly prescribed, as in full-hand grasping. This paper develops an adaptive scheme that estimates that relationship online, using only joint-angle sensing and a wrist-mounted force/torque sensor, with no exteroceptive measurement of the tool tip. We derive a provably stable kinematic update law that identifies the kinematics of an unknown tool from force/torque feedback alone, and prove stability of both the rigid case and the case with a compliance controller as an inner loop. We show that identification is confined to the directions the motion excites --- so that, for example, a tool's length is unobservable under a rigid insertion push, while a compliant loop's passive yielding partially excites it; and that with a second-order admittance the compliant certificate holds unconditionally in continuous time. We also pose the combined control and estimation problem as a Quadratic Program (QP): the formulation yields the prediction term of the update law exactly but, instructively, cannot reproduce the tracking adaptation term. We validate the scheme in simulation on a peg-in-hole insertion. This work is the first step in a research program aimed at factoring manipulation learning into a task policy which can be learned in isolation of the robot, for instance by reinforcement learning, and an adaptive kinematic component that adapts online to the particular robot, hand, or tool in use.
On-body robots that travel around a human limb must keep a firm enough grip to avoid slipping or detaching, while never pressing hard enough to hurt—a balance that is hardest to strike precisely when the robot is orbiting the limb and gravity continually redistributes the contact loads. This paper presents an open, non-anthropomorphic robot that wraps around a compliant cylindrical surface with a three-contact grasp: a central traction module with two in-line driven wheels, and two lateral spring-loaded arms with distal wheels. Its central contribution is an actuation-space decomposition in which the two lateral wheel torques, expressed in a common-mode/differential basis, simultaneously drive the orbital motion and regulate the central normal force. We show that this basis diagonalises both the rolling kinematics and the static force balance, so the differential (grip-regulating) channel is provably orthogonal to the common-mode (propulsion) channel: a single pair of actuators perform both tasks without mutual interference and without a dedicated force mechanism. A model-based feedforward law derived from the static contact model, corrected by a PI term fed back from the compliant arms—which double as the force sensor—keeps the central force within a safe band; in a full-revolution simulation the differential command reverses sign to counteract the gravitational load swing while leaving the orbit undisturbed. The same compliant arms yield a closed-form estimate of the cylinder radius and contact geometry, accurate to below one millimetre across a 45–87 mm diameter range, from proprioception alone. Preliminary prototype tests reproduce the predicted behaviour, supporting the approach for future wearable and assistive applications.
Luz M. Tobar-Subía-Contento, Juan A. Cabrera, Anthony Mandow et al.· Biomimetics· 0 citations
This thesis investigates advanced modeling and control strategies for robotic manipulators, focusing on the DLR-HIT II robotic hand and the KUKA LBR iiwa. It presents three core contributions that integrate simulation, model-based control, and data-driven methods to improve torque and position control under uncertainties and disturbances. First, a dual-platform simulation framework is developed using MATLAB Simscape Multibody and CoppeliaSim. The system accurately models the DLR-HIT II hand’s kinematics and dynamics, enabling both control validation and realistic interaction with virtual environments. The use of Unified Robot Description Format (URDF)-based modeling sup-ports reusability and modular analysis. Second, a physics-informed neural network (PINN) is proposed for direct torque and position control. This method uses only time and joint position inputs, internally computes derivatives, and generalizes well across various trajectory types. It eliminates the need for separate feedback controllers and shows strong robustness under disturbances, while maintaining low computational cost. Third, a Spike-Aware Hybrid Torque Control (SA-HTC) architecture is enhanced with a feedforward neural network (NN) trained offline. The network learns to improve Computed Torque Control (CTC) for unmodeled effects, friction, and external forces by refining the CTC torque output in real time. Simulation results across diverse trajectories and noise levels demonstrate that the SA-HTC method significantly improves tracking accuracy and robustness compared to classical CTC. To support deployment on position-controlled hardware, a physics-informed torque-to-position interface is introduced and compared with virtual stiffness and admittance wrappers, yielding lower steady-state bias, reduced phase lag, lower tracking error, and robust cross-trajectory generalization. Thus, these contributions advance the integration of learning-based and model-based control strategies in robotics. The results highlight scalable and efficient methods for accurate trajectory tracking and torque control, offering practical potential for robotic manipulation and automation applications.
We present a robust contact-aware control framework for aerial writing on an underactuated platform. The framework combines nonlinear model predictive control (NMPC) for accurate end-effector position and normal-force tracking at small reference penetration depths, with consistent performance across controller tunings, with whole-body incremental nonlinear dynamic inversion (INDI) for robustness to frictional and aerodynamic disturbances during contact. The proposed controllers are validated on a quadrotor-based aerial manipulator with a rigid, single-link, one-degree-of-freedom (DoF) arm in simulation and real-world experiments. The aerial writing experiments span vertical and inclined surfaces, multiple reference forces, different friction conditions, and wind disturbances. The results demonstrate that robust simultaneous five-DoF end-effector pose and contact-force tracking is achievable on a standard underactuated quadrotor with a simple, rigid, single-link arm, without requiring a fully actuated platform, a complex arm, or dedicated force/torque sensing.
In robotic manipulation studies, grasping is often treated as a binary success or failure problem, usually defined by whether the object simply stays in the hand. For forceful tool use, however, this view is insufficient because grasp compliance becomes a critical factor governing how the hand and tool behave under load. Compliance arises from coupled kinematics, grasp configuration, passive mechanics, and contact conditions, producing nonlinear behavior in which deformation and interaction forces influence each other. Understanding this relationship is essential for predictive models of how a grasped tool and a compliant hand jointly respond to external loading. In underactuated hands, these effects are amplified: such designs offer low cost and adaptive grasping, but make compliance behavior more difficult to model and predict. Our goal is therefore to develop a predictive model for grasped tool behavior during forceful interactions. To address this challenge, we introduce an analytical model informed neural network (AMINN), a hybrid predictive model that combines an analytical mechanics layer with data driven learning to estimate grasp stability and in hand tool displacement under external loading. The model is evaluated on a three finger underactuated robotic hand and shows strong predictive capability with mechanically meaningful outputs across diverse loading conditions. Compared with a black box multilayer perceptron baseline, AMINN also achieves better energy based physical consistency. Beyond prediction accuracy alone, this framework advances physically interpretable learning for robotic manipulation and supports more reliable, safer, and more trustworthy autonomous tool use in safety critical settings during forceful interaction.
This study proposes a motion trajectory correction method for a robot based on iterative learning and force information to automate contact-based glue application tasks. To apply glue using a sponge, force information is essential because a uniform coating requires subtle changes in the contact force at the fingertips. Most studies on the automation of coating tasks focus on noncontact processes such as spray painting that do not require force sensing at the end effector. By contrast, we aim to automate contact-based glue application tasks using a robot-teaching system that matches the forces produced by a human operator and robot system. In a similar system, force in the pressing direction of the sponge was successfully reproduced. However, force errors remained along the other axes. Hence, in our system, the target motion trajectory is also corrected to match the torques about the two axes orthogonal to the pressing direction, improving the accuracy of the robot's movements. Experimental results demonstrate the effectiveness of the proposed method and its potential for use in contact-based coating tasks. In addition to the force and torque evaluation, a water-droplet spreading task on color-changing paper is used as a task-level surrogate assessment; applying the correction improved the spreading uniformity relative to no compensation.
Yuina Takahashi, Yoshiyuki Hatta, Rin Ikeda et al.· IEEE Robotics and Automation...· 0 citations
Difficulty in haptic feedback for surgical robots has been a long-term problem for decades. In recent years, learning-based force estimation from robot states suggests desirable accuracy without the necessity of extra sensors. However, challenges remain in obtaining representative training data in which the robot moves in the workspace under various external forces. In this work, a parallel motor-cable system is developed. With six motor-cable units installed around the robot workspace, cables with controllable tension connected to the robot end-effector can provide the desired external force without interfering with the movement of the surgical robot. The development of the system includes motor-unit hardware, control software, sensor drivers, simulations, and more. Preliminary experiments suggest an accuracy of force actuation with errors less than 1 N.
Haonan Peng, Dun-Tin Chiang, Jordan Hendricks et al.· 0 citations
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