arXiv subjects

Fulvio Forni

Publications and source records attributed to Fulvio Forni.

At least 19 recordsLinked to original sources

arXiv2026

A Virtual Mechanical Interaction Layer Enables Resilient Human-to-Robot Object Handovers

Object handover is a common form of interaction that is widely present in collaborative tasks. However, achieving it efficiently remains a challenge. We address the problem of ensuring resilient robotic actions that can adapt to complex changes in object pose during human-to-robot object handovers. We propose the use of Virtual Model Control to create an interaction layer that controls the robot and adapts to the dynamic changes in the handover process. Additionally, we propose the use of augmented reality to facilitate bidirectional communication between humans and robots during handovers. We assess the performance of our controller in a set of experiments that demonstrate its resilience to various sources of uncertainties, including complex changes to the object's pose during the handover. Finally, we performed a user study with 16 participants to understand human preferences for different robot control profiles and augmented reality visuals in object handovers. Our results showed a general preference for the proposed approach and revealed insights that can guide further development in adapting the interaction with the user.

cs.RO↗

arXiv2026

Passive iFIR filters for data-driven velocity control in robotics

We present a passive, data-driven velocity control method for nonlinear robotic manipulators that achieves better tracking performance than optimized PID with comparable design complexity. Using only three minutes of probing data, a VRFT-based design identifies passive iFIR controllers that (i) preserve closed-loop stability via passivity constraints and (ii) outperform a VRFT-tuned PID baseline on the Franka Research 3 robot in both joint-space and Cartesian-space velocity control, achieving up to a 74.5% reduction in tracking error for the Cartesian velocity tracking experiment with the most demanding reference model. When the robot end-effector dynamics change, the controller can be re-learned from new data, regaining nominal performance. This study bridges learning-based control and stability-guaranteed design: passive iFIR learns from data while retaining passivity-based stability guarantees, unlike many learning-based approaches.

cs.RO↗

arXiv2026

A Neuromorphic Architecture for Scalable Event-Based Control

This paper introduces the ``rebound Winner-Take-All (RWTA)" motif as the basic element of a scalable neuromorphic control architecture. From the cellular level to the system level, the resulting architecture combines the reliability of discrete computation and the tunability of continuous regulation: it inherits the discrete computation capabilities of winner-take-all state machines and the continuous tuning capabilities of excitable biophysical circuits. The proposed event-based framework addresses continuous rhythmic generation and discrete decision-making in a unified physical modeling language. We illustrate the versatility, robustness, and modularity of the architecture through the nervous system design of a snake robot.

cs.AI↗

arXiv2026

Distributed Virtual Model Control for Scalable Human-Robot Collaboration in Shared Workspace

We present a decentralized, agent agnostic, and safety-aware control framework for human-robot collaboration based on Virtual Model Control (VMC). In our approach, both humans and robots are embedded in the same virtual-component-shaped workspace, where motion is the result of the interaction with virtual springs and dampers rather than explicit trajectory planning. A decentralized, force-based stall detector identifies deadlocks, which are resolved through negotiation. This reduces the probability of robots getting stuck in the block placement task from up to 61.2% to zero in our experiments. The framework scales without structural changes thanks to the distributed implementation: in experiments we demonstrate safe collaboration with up to two robots and two humans, and in simulation up to four robots, maintaining inter-agent separation at around 20 cm. Results show that the method shapes robot behavior intuitively by adjusting control parameters and achieves deadlock-free operation across team sizes in all tested scenarios.

cs.RO↗

arXiv2025

Optimal Virtual Model Control for Robotics: Design and Tuning of Passivity-Based Controllers

Passivity-based control is a cornerstone of control theory and an established design approach in robotics. Its strength is based on the passivity theorem, which provides a powerful interconnection framework for robotics. However, the design of passivity-based controllers and their optimal tuning remain challenging. We propose here an intuitive design approach for fully actuated robots, where the control action is determined by a `virtual-mechanism' as in classical virtual model control. The result is a robot whose controlled behavior can be understood in terms of physics. We achieve optimal tuning by applying algorithmic differentiation to ODE simulations of the rigid body dynamics. Overall, this leads to a flexible design and optimization approach: stability is proven by passivity of the virtual mechanism, while performance is obtained by optimization using algorithmic differentiation.

cs.RO↗

arXiv2025

Collaborative Drill Alignment in Surgical Robotics

Robotic assistance allows surgeries to be reliably and accurately executed while still under direct supervision of the surgeon, combining the strengths of robotic technology with the surgeon's expertise. This paper describes a robotic system designed to assist in surgical procedures by implementing a virtual drill guide. The system integrates virtual-fixture functionality using a novel virtual-mechanism controller with additional visual feedback. The controller constrains the tool to the desired axis, while allowing axial motion to remain under the surgeon's control. Compared to prior virtual-fixture approaches -- which primarily perform pure energy-shaping and damping injection with linear springs and dampers -- our controller uses a virtual prismatic joint to which the robot is constrained by nonlinear springs, allowing us to easily shape the dynamics of the system. We detail the calibration procedures required to achieve sufficient precision, and describe the implementation of the controller. We apply this system to a veterinary procedure: drilling for transcondylar screw placement in dogs. The results of the trials on 3D-printed bone models demonstrate sufficient precision to perform the procedure and suggest improved angular accuracy and reduced exit translation errors compared to patient specific guides (PSG). Discussion and future improvements follow.

eess.SY↗

arXiv2025

Design of MIMO Lur'e oscillators via dominant system theory with application in multi-agent rhythm synchronization

This paper presents a new design framework for dynamic output-feedback controllers for Lur'e oscillation in a multiple-input multiple-output setting. We first revisit and extend dominant system theory to state-dependent rates, with the goal of deriving conditions based on linear matrix inequalities. Then, we introduce a separation principle for Lur'e oscillator design, which allows for the independent design of a state-feedback oscillator and an observer. Our proposed control synthesis is demonstrated through the rhythm synchronization in multi-agent systems, illustrating how networks of stable, heterogeneous linear agents can be driven into phase-locked rhythmic behavior.

eess.SY↗

arXiv2025

Passive Lifted FIR Filters for Nonlinear System Identification

Passivity is a fundamental property of physical systems. In data-driven modeling, ensuring that a learned model preserves this structural property is critical to avoiding instability in close loop. Although linear passive system identification is well-established, nonlinear extensions remain challenging. We propose nonlinear operators defined through passivity-preserving lifting of linear passive FIR filters. Passivity is enforced efficiently through frequency-domain constraints, and the nonlinear lifting includes output feedback for expressivity. Numerical and real-world experiments demonstrate the framework capabilities, including the computational advantage of frequency-domain constraints against LMI-based alternatives.

eess.SY↗

arXiv2025

Periodic robust robotic rock chop via virtual model control

Robotic cutting is a challenging, contact-rich manipulation task where the robot must simultaneously negotiate unknown object mechanics, large contact forces, and precise motion requirements. Our hypothesis is that this complexity can be alleviated through the design of a physically structured virtual-model controller that uses switched virtual mechanisms to generate a robust, rhythmic rock-chop motion for robotic cutting, without requiring pre-planned trajectories or precise environmental information. Motion is generated by the interaction between the environment, the robot's dynamics, and the virtual forces of the switching virtual mechanism, ultimately realized through the available actuation. Through theoretical and numerical analysis, together with experimental validation, we demonstrate that the controlled robot behavior settles into a stable periodic motion. Experiments with a Franka manipulator demonstrate robust cuts across five different vegetables, achieving sub-millimeter slice accuracy for thicknesses from 1 mm to 7 mm at a rate of nearly one cut per second. The controller maintains high performance despite changes in knife shape or cutting board height, and successfully adapts to a different humanoid manipulator, demonstrating robustness and platform independence.

cs.RO↗

arXiv2025

Gradient modelling of memristive systems

We introduce a gradient modeling framework for memristive systems. Our focus is on memristive systems as they appear in neurophysiology and neuromorphic systems. Revisiting the original definition of Chua, we regard memristive elements as gradient operators of quadratic functionals with respect to a metric determined by the memristance. We explore the consequences of gradient properties for the analysis and design of neuromorphic circuits.

eess.SY↗

arXiv2025

Amplitude response and square wave describing functions

An analog of the describing function method is developed using square waves rather than sinusoids. Static nonlinearities map square waves to square waves, and their behavior is characterized by their response to square waves of varying amplitude - their amplitude response. The output of an LTI system to a square wave input is approximated by a square wave, to give an analog of the describing function. The classical describing function method for predicting oscillations in feedback interconnections is generalized to this square wave setting, and gives accurate predictions when oscillations are approximately square.

eess.SY↗

arXiv2025

Dissipative iFIR filters for data-driven design

We tackle the problem of providing closed-loop stability guarantees with a scalable data-driven design. We combine virtual reference feedback tuning with dissipativity constraints on the controller for closed-loop stability. The constraints are formulated as a set of linear inequalities in the frequency domain. This leads to a convex problem that is scalable with respect to the length of the data and the complexity of the controller. An extension of virtual reference feedback tuning to include disturbance dynamics is also discussed. The proposed data-driven control design is illustrated by a soft gripper impedance control example.

eess.SY↗

arXiv2024

A homotopy theorem for incremental stability

A theorem is proved to verify incremental stability of a feedback system via a homotopy from a known incrementally stable system. A first corollary of that result is that incremental stability may be verified by separation of Scaled Relative Graphs, correcting two assumptions in [1, Theorem 2]. A second corollary provides an incremental version of the classical IQC stability theorem.

math.OC↗

arXiv2024

Passive iFIR Filters for Data-Driven Control

We consider the design of a new class of passive iFIR controllers given by the parallel action of an integrator and a finite impulse response filter. iFIRs are more expressive than PID controllers but retain their features and simplicity. The paper provides a model-free data-driven design for passive iFIR controllers based on virtual reference feedback tuning. Passivity is enforced through constrained optimization (three different formulations are discussed). The proposed design does not rely on large datasets or accurate plant models.

eess.SY↗

arXiv2024

Neuromorphic Control of a Pendulum

We illustrate the potential of neuromorphic control on the simple mechanical model of a pendulum, with both event-based actuation and sensing. The controller and the pendulum are regarded as event-based systems that occasionally interact to coordinate their respective rhythms. Control occurs through a proper timing of the interacting events. We illustrate the mixed nature of the control design: the design of a rhythmic automaton, able to generate the right sequence of events, and the design of a feedback regulator, able to tune the timing of events.

eess.SY↗

arXiv2023

Lettuce modelling for growth control in precision agriculture

Improving the efficiency of agriculture is a growing priority due to food security issues, environmental concerns, and economics. Precision agriculture and variable rate application technology could enable increases in yield while maintaining or reducing fertiliser use. However, this requires the development of control algorithms which are suitable for the challenges of agriculture. In this paper, we propose a new mechanistic open model of lettuce growth for use in control of precision agriculture. We demonstrate that our model is cooperative and fits well to experimental data. We use the model to show, via simulations, that a simple proportional distributed control law increases crop uniformity and yield without increasing nitrogen use, even in the presence of sparse actuation and noisy observations.

eess.SY↗

arXiv2023

Dominant Mixed Feedback Design for Stable Oscillations

We present a design framework to induce stable oscillations through mixed feedback control. We provide conditions on the feedback gain and on the balance between positive and negative feedback contributions to guarantee robust oscillations. Using linear matrix inequalities, we later derive a systematic design for robustness to bounded dynamic uncertainties and for passive interconnections. The results of the paper provide a system-theoretic justification to several observations from system biology and neuroscience pointing at the mixed feedback as a fundamental enabler for robust oscillations. Our results are illustrated through a distributed electrical circuit mimicking (simplified) neural dynamics.

eess.SY↗

arXiv2023

A Generalized Approach to Impedance Control Design for Robotic Minimally Invasive Surgery

Energy based control methods are at the core of modern robotic control algorithms. In this paper we present a general approach to virtual model/mechanism control, which is a powerful design tool to create energy based controllers. We present two novel virtual-mechanisms designed for robotic minimally invasive surgery, which control the position of a surgical instrument while passing through an incision. To these virtual mechanisms we apply the parameter tuning method of Larby and Forni 2022, which optimizes for local performance while ensuring global stability.

cs.RO↗