Abstract--In human-robot systems, ensuring safety during force control in the presence of both internal and external disturbances is crucial. As a typical loosely coupled floating-base robot system, the supernumerary robotic leg (SRL) system is particularly susceptible to strong internal disturbances. T o address the challenge posed by floating base, we investigated the dynamics model of the loosely coupled SRL and designed a hybrid position/force impedance controller to fit dynamic torque input. An efficient variable impedance control (VIC) method is developed to enhance human-robot interaction, particularly in scenarios involving external force disturbances. By dynamically adjusting impedance parameters, VIC improves the dynamic switching between rigidity and flexibility, so that it can adapt to unknown environmental disturbances in different states. An efficient real-time stability guaranteed impedance parameters generating network is specifically designed for the proposed SRL, to achieve shock mitigation and high rigidity supporting. Simulations and experiments validate the system's effectiveness, demonstrating its ability to maintain smooth signal transitions in flexible states while providing strong support forces in rigid states. This approach provides a practical solution for accommodating individual gait variations in interaction, and significantly advances the safety and adaptability of human-robot systems.

As robotics gains popularity, interaction control becomes crucial for ensuring force tracking in manipulator-based tasks. Typically, traditional interaction controllers either require extensive tuning, or demand expert knowledge of the environment, which is often impractical in real-world applications. This work proposes a novel control strategy leveraging Neural Networks (NNs) to enhance the force-tracking behavior of a Direct Force Controller (DFC). Unlike similar previous approaches, it accounts for the manipulator's tangential velocity, a critical factor in force exertion, especially during fast motions. The method employs an ensemble of feedforward NNs to predict contact forces, then exploits the prediction to solve an optimization problem and generate an optimal residual action, which is added to the DFC output and applied to an impedance controller. The proposed Velocity-augmented Artificial intelligence Interaction Controller for Ambiguous Models (VAICAM) is validated in the Gazebo simulator on a Franka Emika Panda robot. Against a vast set of trajectories, VAICAM achieves superior performance compared to two baseline controllers.

While visuomotor policy has made advancements in recent years, contact-rich tasks still remain a challenge. Robotic manipulation tasks that require continuous contact demand explicit handling of compliance and force. However, most visuomotor policies ignore compliance, overlooking the importance of physical interaction with the real world, often leading to excessive contact forces or fragile behavior under uncertainty. Introducing force information into vision-based imitation learning could help improve awareness of contacts, but could also require a lot of data to perform well. One remedy for data scarcity is to generate data in simulation, yet computationally taxing processes are required to generate data good enough not to suffer from the Sim2Real gap. In this work, we introduce a framework for generating force-informed data in simulation, instantiated by a single human demonstration, and show how coupling with a compliant policy improves the performance of a visuomotor policy learned from synthetic data. We validate our approach on real-robot tasks, including non-prehensile block flipping and a bi-manual object moving, where the learned policy exhibits reliable contact maintenance and adaptation to novel conditions. Project Website: https://flow-with-the-force-field.github.io/webpage/

Contact-rich manipulation is crucial for robots to perform tasks requiring precise force control, such as insertion, assembly, and in-hand manipulation. However, most imitation learning (IL) policies remain position-centric and lack explicit force awareness, and adding force/torque sensors to collaborative robot arms is often costly and requires additional hardware design. To overcome these issues, we propose FILIC, a Force-guided Imitation Learning framework with impedance torque control. FILIC integrates a Transformer-based IL policy with an impedance controller in a dual-loop structure, enabling compliant force-informed, force-executed manipulation. For robots without force/torque sensors, we introduce a cost-effective end-effector force estimator using joint torque measurements through analytical Jacobian-based inversion while compensating with model-predicted torques from a digital twin. We also design complementary force feedback frameworks via handheld haptics and VR visualization to improve demonstration quality. Experiments show that FILIC significantly outperforms vision-only and joint-torque-based methods, achieving safer, more compliant, and adaptable contact-rich manipulation. Our code can be found in https://github.com/TATP-233/FILIC.

With robots increasingly operating in human-centric environments, ensuring soft and safe physical interactions, whether with humans, surroundings, or other machines, is essential. While compliant hardware can facilitate such interactions, this work focuses on impedance controllers that allow torque-controlled robots to safely and passively respond to contact while accurately executing tasks. From inverse dynamics to quadratic programming-based controllers, the effectiveness of these methods relies on accurate dynamics models of the robot and the object it manipulates. Any model mismatch results in task failures and unsafe behaviors. Thus, we introduce Rapid Mismatch Estimation (RME), an adaptive, controller-agnostic, probabilistic framework that estimates end-effector dynamics mismatches online, without relying on external force-torque sensors. From the robot's proprioceptive feedback, a Neural Network Model Mismatch Estimator generates a prior for a Variational Inference solver, which rapidly converges to the unknown parameters while quantifying uncertainty. With a real 7-DoF manipulator driven by a state-of-the-art passive impedance controller, RME adapts to sudden changes in mass and center of mass at the end-effector in $\sim400$ ms, in static and dynamic settings. We demonstrate RME in a collaborative scenario where a human attaches an unknown basket to the robot's end-effector and dynamically adds/removes heavy items, showcasing fast and safe adaptation to changing dynamics during physical interaction without any external sensory system.

Robots that interact with humans or perform delicate manipulation tasks must exhibit compliance. However, most commercial manipulators are rigid and suffer from significant friction, limiting end-effector tracking accuracy in torque-controlled modes. To address this, we present a real-time, open-source impedance controller that smoothly interpolates between joint-space and task-space compliance. This hybrid approach ensures safe interaction and precise task execution, such as sub-centimetre pin insertions. We deploy our controller on Frank, a dual-arm platform with two Kinova Gen3 arms, and compensate for modelled friction dynamics using a model-free observer. The system is real-time capable and integrates with standard ROS tools like MoveIt!. It also supports high-frequency trajectory streaming, enabling closed-loop execution of trajectories generated by learning-based methods, optimal control, or teleoperation. Our results demonstrate robust tracking and compliant behaviour even under high-friction conditions. The complete system is available open-source at https://github.com/applied-ai-lab/compliant_controllers.

--Humans are experts in collaborating with others physically by regulating compliance behaviors based on the perception of their partners' states and the task requirements. Enabling robots to develop proficiency in human collaboration skills can facilitate more efficient human-robot collaboration (HRC). This paper introduces an innovative impedance regulation skill learning framework for achieving HRC in multiple physical collaborative tasks. The framework is designed to adjust the robot compliance to the human partner's states while adhering to reference trajectories provided by human-human demonstrations. Specifically, electromyography (EMG) signals from human muscles are collected and analyzed to extract limb impedance, representing compliance behaviors during demonstrations. Human endpoint motions are captured and represented using a probabilistic learning method to create reference trajectories and corresponding impedance profiles. Meanwhile, an LSTM-based module is implemented to develop task-oriented impedance regulation policies by mapping the muscle synergistic contributions between two demonstrators. Finally, we propose a whole-body impedance controller for a human-like robot, coordinating joint outputs to achieve the desired impedance and reference trajectory during task execution. Experimental validation was conducted through a collaborative transportation task and two interactive T ai Chi pushing hands tasks, demonstrating superior performance from the perspective of interactive forces compared to a constant impedance control method. OLLABORA TIVE robots (cobots) have emerged as a solution for more efficient human-robot collaboration (HRC) in both industrial and domestic scenarios. Co-manipulation outperforms fully robotic manipulation by offering enhanced flexibility and effectiveness while surpasses fully human manipulation by reducing labor costs, maintaining concentration, and minimizing errors due to fatigue [1]. This work was supported in part by the Research Grants Council of the Hong Kong SAR under Grant 24209021, 14222722, 14211723 and C7100-22GF and in part by InnoHK of the Government of Hong Kong via the Hong Kong Centre for Logistics Robotics. Darwin Caldwell is with the Department of Advanced Robotics, Istituto Italiano di Tecnologia, 16163 Genoa, Italy (e-mail: darwin.caldwell@iit.it).

Physical movement therapy is a crucial method of rehabilitation aimed at reinstating mobility among patients facing motor dysfunction due to neurological conditions or accidents. Such therapy is usually featured as patient-specific, repetitive, and labor-intensive. The conventional method, where therapists collaborate with patients to conduct repetitive physical training, proves strenuous due to these characteristics. The concept of robot-assisted rehabilitation, assisting therapists with robotic systems, has gained substantial popularity. However, building such systems presents challenges, such as diverse task demands, uncertainties in dynamic models, and safety issues. To address these concerns, in this paper, we proposed a bilateral teleoperation system for rehabilitation. The control scheme of the system is designed as an integrated framework of impedance control and disturbance observer where the former can ensure compliant human-robot interaction without the need for force sensors while the latter can compensate for dynamic uncertainties when only a roughly identified dynamic model is available. Furthermore, the scheme allows free switching between tracking tasks and physical human-robot interaction (pHRI). The presented system can execute a wide array of pre-defined trajectories with varying patterns, adaptable to diverse needs. Moreover, the system can capture therapists' demonstrations, replaying them as many times as necessary. The effectiveness of the teleoperation system is experimentally evaluated and demonstrated.

This paper addresses the challenge of autonomous excavation of challenging terrains, in particular those that are prone to jamming and inter-particle adhesion when tackled by a standard penetrate-drag-scoop motion pattern. Inspired by human excavation strategies, our approach incorporates oscillatory rotation elements -- including swivel, twist, and dive motions -- to break up compacted, tangled grains and reduce jamming. We also present an adaptive impedance control method, the Reactive Attractor Impedance Controller (RAIC), that adapts a motion trajectory to unexpected forces during loading in a manner that tracks a trajectory closely when loads are low, but avoids excessive loads when significant resistance is met. Our method is evaluated on four terrains using a robotic arm, demonstrating improved excavation performance across multiple metrics, including volume scooped, protective stop rate, and trajectory completion percentage.

Attaching a heavy payload to the wrist force/moment (F/M) sensor of a manipulator can cause conventional impedance controllers to fail in establishing the desired impedance due to the presence of non-contact forces; namely, the inertial and gravitational forces of the payload. This paper presents an impedance control scheme designed to accurately shape the force-response of such a manipulator without requiring acceleration measurements. As a result, neither wrist accelerometers nor dynamic estimators for compensating inertial load forces are necessary. The proposed controller employs an inner-outer loop feedback structure, which not only addresses uncertainties in the robot's dynamics but also enables the specification of a general target impedance model, including nonlinear models. Stability and convergence of the controller are analytically proven, with results showing that the control input remains bounded as long as the desired inertia differs from the payload inertia. Experimental results confirm that the proposed impedance controller effectively shapes the impedance of a manipulator carrying a heavy load according to the desired impedance model.