Etienne Burdet

Lin Yang, Anirvan Dutta, Yuan Ji et al.

Achieving human-level dexterity in contact-rich, tool-mediated manipulation remains a significant challenge due to visual occlusion and the underdetermined nature of haptic sensing. This paper introduces a parameterized Equilibrium Manifold (EM) as a unified representation for tool-mediated interaction, and develops a closed-loop framework that integrates haptic estimation, online planning, and adaptive stiffness control. We establish a physical-geometric duality using an adaptive manipulation potential incorporating a differentiable contact model, which induces the manifold's geometric structure and ensures that complex physical interactions are encapsulated as continuous operations on the EM. Within this framework, we reformulate haptic estimation as a manifold parameter estimation problem. Specifically, a hybrid inference strategy (haptic SLAM) is employed in which discrete object shapes are classified via particle filtering, while the continuous object pose is estimated using analytical gradients for efficient optimization. By continuously updating the parameters of the manipulation potential, the framework dynamically reshapes the induced EM to guide online trajectory replanning and implement uncertainty-aware impedance control, thereby closing the perception-action loop. The system is validated through simulation and over 260 real-world screw-loosening trials. Experimental results demonstrate robust identification and manipulation success in standard scenarios while maintaining accurate tracking. Furthermore, ablation studies confirm that haptic SLAM and uncertainty-aware stiffness modulation outperform fixed impedance baselines, effectively preventing jamming during tight tolerance interactions.

Pakorn Uttayopas, Xiaoxiao Cheng, Etienne Burdet

while most of the tactile robots are operated in close-set conditions, it is challenging for them to operate in open-set conditions where test objects are beyond the robots' knowledge. We proposed an open-set recognition framework using mechanical properties to recongise known objects and incrementally label novel objects. The main contribution is a clustering algorithm that exploits knowledge of known objects to estimate cluster centre and sizes, unlike a typical algorithm that randomly selects them. The framework is validated with the mechanical properties estimated from a real object during interaction. The results show that the framework could recognise objects better than alternative methods contributed by the novelty detector. Importantly, our clustering algorithm yields better clustering performance than other methods. Furthermore, the hyperparameters studies show that cluster size is important to clustering results and needed to be tuned properly.

Atsushi Takagi, Yanan Li, Hiroaki Gomi et al.

Robot systems for teleoperation commonly use a spring-like force pulling the follower robot towards the leader's position to track their movements. With this control strategy, the tracking accuracy deteriorates when the follower' stiffness is low, but high stiffness poses a danger to objects or people in the follower robot's environment. To address this trade-off between tracking accuracy and safety, we propose an alternative intention assimilation control (IAC) strategy where the robot's tracking accuracy can be ensured without high stiffness. Different from traditional approaches, which transmit the leader's current position to the follower, this new controller estimates the leader's target position and transmits it to the follower. With this strategy, the follower impedance can be changed on-the-fly to continuously reflect the user's desired impedance or modulated automatically to fulfill the task requirements. Our controller was validated on two 7 degree-of-freedom manipulators, yielding high tracking accuracy with varying impedance. Four experiments were conducted to compare {teleoperation} with IAC to tele-impedance control (TIC) during free tracking, interaction with a balloon, during peg insertion, and table polishing with force feedback. The results show that IAC increases tracking accuracy, improves task completion rate and reduces completion time. IAC enables the robot to accurately replicate the user's movement while giving them freedom to modulate the impedance according to their intention, providing an unprecedented level of control of the follower's position and its impedance during unilateral and bilateral teleoperation.

Anirvan Dutta, Simone Tasciotti, Claudia Cusseddu et al.

Estimating physical properties is critical for safe and efficient autonomous robotic manipulation, particularly during contact-rich interactions. In such settings, vision and tactile sensing provide complementary information about object geometry, pose, inertia, stiffness, and contact dynamics, such as stick-slip behavior. However, these properties are only indirectly observable and cannot always be modeled precisely (e.g., deformation in non-rigid objects coupled with nonlinear contact friction), making the estimation problem inherently complex and requiring sustained exploitation of visuo-tactile sensory information during action. Existing visuo-tactile perception frameworks have primarily emphasized forceful sensor fusion or static cross-modal alignment, with limited consideration of how uncertainty and beliefs about object properties evolve over time. Inspired by human multi-sensory perception and active inference, we propose the Cross-Modal Latent Filter (CMLF) to learn a structured, causal latent state-space of physical object properties. CMLF supports bidirectional transfer of cross-modal priors between vision and touch and integrates sensory evidence through a Bayesian inference process that evolves over time. Real-world robotic experiments demonstrate that CMLF improves the efficiency and robustness of latent physical properties estimation under uncertainty compared to baseline approaches. Beyond performance gains, the model exhibits perceptual coupling phenomena analogous to those observed in humans, including susceptibility to cross-modal illusions and similar trajectories in learning cross-sensory associations. Together, these results constitutes a significant step toward generalizable, robust and physically consistent cross-modal integration for robotic multi-sensory perception.

Prajval Kumar Murali, Anirvan Dutta, Michael Gentner et al.

This work presents a novel active visuo-tactile based framework for robotic systems to accurately estimate pose of objects in dense cluttered environments. The scene representation is derived using a novel declutter graph (DG) which describes the relationship among objects in the scene for decluttering by leveraging semantic segmentation and grasp affordances networks. The graph formulation allows robots to efficiently declutter the workspace by autonomously selecting the next best object to remove and the optimal action (prehensile or non-prehensile) to perform. Furthermore, we propose a novel translation-invariant Quaternion filter (TIQF) for active vision and active tactile based pose estimation. Both active visual and active tactile points are selected by maximizing the expected information gain. We evaluate our proposed framework on a system with two robots coordinating on randomized scenes of dense cluttered objects and perform ablation studies with static vision and active vision based estimation prior and post decluttering as baselines. Our proposed active visuo-tactile interactive perception framework shows upto 36% improvement in pose accuracy compared to the active vision baseline.

Ziwei Wang, Yanpei Huang, Xiaoxiao Cheng et al.

Traditional telesurgery relies on the surgeon's full control of the robot on the patient's side, which tends to increase surgeon fatigue and may reduce the efficiency of the operation. This paper introduces a Robotic Partner (RP) to facilitate intuitive bimanual telesurgery, aiming at reducing the surgeon workload and enhancing surgeon-assisted capability. An interval type-2 polynomial fuzzy-model-based learning algorithm is employed to extract expert domain knowledge from surgeons and reflect environmental interaction information. Based on this, a bimanual shared control is developed to interact with the other robot teleoperated by the surgeon, understanding their control and providing assistance. As prior information of the environment model is not required, it reduces reliance on force sensors in control design. Experimental results on the DaVinci Surgical System show that the RP could assist peg-transfer tasks and reduce the surgeon's workload by 51\% in force-sensor-free scenarios.

Jonathan Eden, Mario Bräcklein, Jaime Ibáñez Pereda et al.

Augmenting the body with artificial limbs controlled concurrently to the natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this vision possible. By allowing individuals to achieve otherwise impossible actions, this movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with their environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning, and propose a vision for the field.

Yanpei Huang, Jonathan Eden, Ekaterina Ivanova et al.

Many teleoperation tasks require three or more tools working together, which need the cooperation of multiple operators. The effectiveness of such schemes may be limited by communication. Trimanipulation by a single operator using an artificial third arm controlled together with their natural arms is a promising solution to this issue. Foot-controlled interfaces have previously shown the capability to be used for the continuous control of robot arms. However, the use of such interfaces for controlling a supernumerary robotic limb (SRLs) in coordination with the natural limbs, is not well understood. In this paper, a teleoperation task imitating physically coupled hands in a virtual reality scene was conducted with 14 subjects to evaluate human performance during tri-manipulation. The participants were required to move three limbs together in a coordinated way mimicking three arms holding a shared physical object. It was found that after a short practice session, the three-hand tri-manipulation using a single subject's hands and foot was still slower than dyad operation, however, they displayed similar performance in success rate and higher motion efficiency than two person's cooperation.

Yanpei Huang, Wenjie Lai, Lin Cao et al.

Flexible endoscopy requires high skills to manipulate both the endoscope and associated instruments. In most robotic flexible endoscopic systems, the endoscope and instruments are controlled separately by two operators, which may result in communication errors and inefficient operation. We present a novel teleoperation robotic endoscopic system that can be commanded by a surgeon alone. This 13 degrees-of-freedom (DoF) system integrates a foot-controlled robotic flexible endoscope and two hand-controlled robotic endoscopic instruments (a robotic grasper and a robotic cauterizing hook). A foot-controlled human-machine interface maps the natural foot gestures to the 4-DoF movements of the endoscope, and two hand-controlled interfaces map the movements of the two hands to the two instruments individually. The proposed robotic system was validated in an ex-vivo experiment carried out by six subjects, where foot control was also compared with a sequential clutch-based hand control scheme. The participants could successfully teleoperate the endoscope and the two instruments to cut the tissues at scattered target areas in a porcine stomach. Foot control yielded 43.7% faster task completion and required less mental effort as compared to the clutch-based hand control scheme. The system introduced in this paper is intuitive for three-limb manipulation even for operators without experience of handling the endoscope and robotic instruments. This three-limb teleoperated robotic system enables one surgeon to intuitively control three endoscopic tools which normally require two operators, leading to reduced manpower, less communication errors, and improved efficiency.

Rejin John Varghese, Anh Nguyen, Etienne Burdet et al.

The compliant nature of soft wearable robots makes them ideal for complex multiple degrees of freedom (DoF) joints, but also introduce additional structural nonlinearities. Intuitive control of these wearable robots requires robust sensing to overcome the inherent nonlinearities. This paper presents a joint kinematics estimator for a bio-inspired multi-DoF shoulder exosuit capable of compensating the encountered nonlinearities. To overcome the nonlinearities and hysteresis inherent to the soft and compliant nature of the suit, we developed a deep learning-based method to map the sensor data to the joint space. The experimental results show that the new learning-based framework outperforms recent state-of-the-art methods by a large margin while achieving 12ms inference time using only a GPU-based edge-computing device. The effectiveness of our combined exosuit and learning framework is demonstrated through real-time teleoperation with a simulated NAO humanoid robot.

Yanan Li, Jonathan Eden, Gerolamo Carboni et al.

This paper introduces human-robot sensory augmentation and illustrates it on a tracking task, where performance can be improved by the exchange of sensory information between the robot and its human user. It was recently found that during interaction between humans, the partners use each other's sensory information to improve their own sensing, thus also their performance and learning. In this paper, we develop a computational model of this unique human ability, and use it to build a novel control framework for human-robot interaction. The human partner's control is formulated as a feedback control with unknown control gains and desired trajectory. A Kalman filter is used to estimate first the control gains and then the desired trajectory. The estimated human partner's desired trajectory is used as augmented sensory information about the system and combined with the robot's measurement to estimate an uncertain target trajectory. Simulations and an implementation of the presented framework on a robotic interface validate the proposed observer-predictor pair for a tracking task. The results obtained using this robot demonstrate how the human user's control can be identified, and exhibit similar benefits of this sensory augmentation as was observed between interacting humans.

Hsien-Yung Huang, Ildar Farkhatdinov, Arash Arami et al.

This paper presents a versatile cable-driven robotic interface to investigate the single-joint joint neuromechanics of the hip, knee and ankle. This endpoint-based interface offers highly dynamic interaction and accurate position control, as is typically required for neuromechanics identification. It can be used with the subject upright, corresponding to natural posture during walking or standing, and does not impose kinematic constraints on a joint, in contrast to existing interfaces. Mechanical evaluations demonstrated that the interface yields a rigidity above 500N/m with low viscosity. Tests with a rigid dummy leg and linear springs show that it can identify the mechanical impedance of a limb accurately. A smooth perturbation is developed and tested with a human subject, which can be used to estimate the hip neuromechanics.

Yanpei Huang, Etienne Burdet, Lin Cao et al.

Robotic minimally invasive interventions typically require using more than two instruments. We thus developed a foot pedal interface which allows the user to control a robotic arm (simultaneously to working with the hands) with four degrees of freedom in continuous directions and speeds. This paper evaluates and compares the performances of ten naive operators in using this new pedal interface and a traditional button interface in completing tasks. These tasks are geometrically complex path-following tasks similar to those in laparoscopic training, and the traditional button interface allows axis-by-axis control with constant speeds. Precision, time, and smoothness of the subjects' control movements for these tasks are analysed. The results demonstrate that the pedal interface can be used to control a robot for complex motion tasks. The subjects kept the average error rate at a low level of around 2.6% with both interfaces, but the pedal interface resulted in about 30% faster operation speed and 60% smoother movement, which indicates improved efficiency and user experience as compared with the button interface. The results of a questionnaire show that the operators found that controlling the robot with the pedal interface was more intuitive, comfortable, and less tiring than using the button interface.

Yanpei Huang, Etienne Burdet, Lin Cao et al.

In robotic surgery, the surgeon controls robotic instruments using dedicated interfaces. One critical limitation of current interfaces is that they are designed to be operated by only the hands. This means that the surgeon can only control at most two robotic instruments at one time while many interventions require three instruments. This paper introduces a novel four-degree-of-freedom foot-machine interface which allows the surgeon to control a third robotic instrument using the foot, giving the surgeon a "third hand". This interface is essentially a parallel-serial hybrid mechanism with springs and force sensors. Unlike existing switch-based interfaces that can only un-intuitively generate motion in discrete directions, this interface allows intuitive control of a slave robotic arm in continuous directions and speeds, naturally matching the foot movements with dynamic force & position feedbacks. An experiment with ten naive subjects was conducted to test the system. In view of the significant variance of motion patterns between subjects, a subject-specific mapping from foot movements to command outputs was developed using Independent Component Analysis (ICA). Results showed that the ICA method could accurately identify subjects' foot motion patterns and significantly improve the prediction accuracy of motion directions from 68% to 88% as compared with the forward kinematics-based approach. This foot-machine interface can be applied for the teleoperation of industrial/surgical robots independently or in coordination with hands in the future.

Renjie Li, Yanan Li, Shengbo Eben Li et al.

It is widely acknowledged that drivers should remain in the control loop of automated vehicles before they completely meet real-world operational conditions. This paper introduces an `indirect shared control' scheme for steer-by-wire vehicles, which allows the vehicle control authority to be continuously shared between the driver and automation through unphysical cooperation. This paper first balances the control objectives of the driver and automation in a weighted summation, and then models the driver's adaptive control behavior using a predictive control approach. The driver adaptation modeling enables off-line evaluations of indirect shared control systems and thus facilitates the design of the assistant controller. Unlike any conventional driver model for manual driving, this model assumes that the driver can learn and incorporate the controller strategy into his internal model for more accurate path following. To satisfy the driving demands in different scenarios, a sliding-window detector is designed to continuously monitor the driver intention and automatically switch the authority weights between the driver and automation. The simulation results illustrate the advantages of considering the driver adaptation in path-following and obstacle-avoidance tasks, and show the effectiveness of indirect shared control for cooperative driving.

Yanan Li, Etienne Burdet

When carrying out tasks in contact with the environment, humans are found to concurrently adapt force, impedance and trajectory. Here we develop a robotic model of this mechanism in humans and analyse the underlying dynamics. We derive a general adaptive controller for the interaction of a robot with an environment solely characterised by its stiffness and damping, using Lyapunov theory.