In this paper, we propose a Contact Diffusion Model (CDM), a novel learning-based approach for multi-contact point localization. We consider a robot equipped with joint torque sensors and a force/torque sensor at the base. By leveraging a diffusion model, CDM addresses the singularity where multiple pairs of contact points and forces produce identical sensor measurements. We formulate CDM to be conditioned on past model outputs to account for the time-dependent characteristics of the multi-contact scenarios. Moreover, to effectively address the complex shape of the robot surfaces, we incorporate the signed distance field in the denoising process. Consequently, CDM can localize contacts at arbitrary locations with high accuracy. Simulation and real-world experiments demonstrate the effectiveness of the proposed method. In particular, CDM operates at 15.97ms and, in the real world, achieves an error of 0.44cm in single-contact scenarios and 1.24cm in dual-contact scenarios.

In this paper, we propose a model predictive control (MPC) that accomplishes interactive robotic tasks, in which multiple contacts may occur at unknown locations. To address such scenarios, we made an explicit contact feedback loop in the MPC framework. An algorithm called Multi-Contact Particle Filter with Exploration Particle (MCP-EP) is employed to establish real-time feedback of multi-contact information. Then the interaction locations and forces are accommodated in the MPC framework via a spring contact model. Moreover, we achieved real-time control for a 7 degrees of freedom robot without any simplifying assumptions by employing a Differential-Dynamic-Programming algorithm. We achieved 6.8kHz, 1.9kHz, and 1.8kHz update rates of the MPC for 0, 1, and 2 contacts, respectively. This allows the robot to handle unexpected contacts in real time. Real-world experiments show the effectiveness of the proposed method in various scenarios.

Disturbance observer (DOB) is a well-known two-loop control structure that imparts robustness to a controller with a simple implementation. As a nonlinear DOB for the robotic systems, we proposed so-called nonlinear robust internal-loop compensator (NRIC) framework in our previous work. In this paper, we further extend the NRIC in such a way that an optimization scheme can be embedded in the control structure. The proposed method is called constrained NRIC (C-NRIC), because the optimization allows us to impose constraints, by which a controller acquires additional properties. As a particular use case of the C-NRIC framework, we design contact-responsive motion controllers that enables a robot to react to unknown interactions while accurately tracking the desired trajectory in free motion. The effectiveness of such designs is validated through the real-world experiments.

We aim to solve the problem of data-driven collision-distance estimation given 3-dimensional (3D) geometries. Conventional algorithms suffer from low accuracy due to their reliance on limited representations, such as point clouds. In contrast, our previous graph-based model, GraphDistNet, achieves high accuracy using edge information but incurs higher message-passing costs with growing graph size, limiting its applicability to 3D geometries. To overcome these challenges, we propose GDN-R, a novel 3D graph-based estimation network. GDN-R employs a layer-wise probabilistic graph-rewiring algorithm leveraging the differentiable Gumbel-top-K relaxation. Our method accurately infers minimum distances through iterative graph rewiring and updating relevant embeddings. The probabilistic rewiring enables fast and robust embedding with respect to unforeseen categories of geometries. Through 41, 412 random benchmark tasks with 150 pairs of 3D objects, we show GDN-R outperforms state-of-the-art baseline methods in terms of accuracy and generalizability. We also show that the proposed rewiring improves the update performance reducing the size of the estimation model. We finally show its batch prediction and auto-differentiation capabilities for trajectory optimization in both simulated and real-world scenarios.

Admittance control is a control scheme to enable physical interactions of a robot, but it easily induces instability when the robot contacts a rigid surface. In this study, a passivity analysis was conducted on a robotic system with admittance control. The results showed that coupled stability with the environment can be ensured when the velocity error between the proxy and the real robot is eliminated. Thus, an adaptive structure modification method is proposed to suppress the possible source of instability. In addition, the energy tank method is combined with the proposed method to ensure system passivity. As a proof of concept, three robot experiments were performed, and the results of the proposed method were compared with those of conventional admittance control and impedance control (with friction compensation). The comparison showed that the proposed method could make the system passive while realizing the desired dynamics during the interaction.

In this paper, we propose an algorithm that estimates contact point and force simultaneously. We consider a collaborative robot equipped with proprioceptive sensors, in particular, joint torque sensors (JTSs) and a base force/torque (F/T) sensor. The proposed method has the following advan-tages. First, fast computation is achieved by proper preprocessing of robot meshes. Second, multi-contact can be identified with the aid of the base F/T sensor, while this is challenging when the robot is equipped with only JTSs. The proposed method is a modification of the standard particle filter to cope with mesh preprocessing and with available sensor data. In simulation validation, for a 7 degree-of-freedom robot, the algorithm runs at 2200Hz with 99.96% success rate for the single-contact case. In terms of the run-time, the proposed method was ≥3.5X faster compared to the existing methods. Dual and triple contacts are also reported in the manuscript.

This paper proposes a decentralized passive impedance control scheme for collaborative grasping using under-actuated aerial manipulators (AMs). The AM system is formulated, using a proper coordinate transformation, as an inertially decoupled dynamics with which a passivity-based control design is conducted. Since the interaction for grasping can be interpreted as a feedback interconnection of passive systems, an arbitrary number of AMs can be modularly combined, leading to a decentralized control scheme. Another interesting consequence of the passivity property is that the AMs automatically converge to a certain configuration to accomplish the grasping. Collaborative grasping using 10 AMs is presented in simulation.

This paper presents a novel algorithm for robot task and motion planning (TAMP) problems by utilizing a reachability tree. While tree-based algorithms are known for their speed and simplicity in motion planning (MP), they are not well-suited for TAMP problems that involve both abstracted and geometrical state variables. To address this challenge, we propose a hierarchical sampling strategy, which first generates an abstracted task plan using Monte Carlo tree search (MCTS) and then fills in the details with a geometrically feasible motion trajectory. Moreover, we show that the performance of the proposed method can be significantly enhanced by selecting an appropriate reward for MCTS and by using a pre-generated goal state that is guaranteed to be geometrically feasible. A comparative study using TAMP benchmark problems demonstrates the effectiveness of the proposed approach.

During operation, aerial manipulation systems are affected by various disturbances. Among them is a gravitational torque caused by the weight of the robotic arm. Common propeller-based actuation is ineffective against such disturbances because of possible overheating and high power consumption. To overcome this issue, in this paper we propose a winch-based actuation for the crane-stationed cable-suspended aerial manipulator. Three winch-controlled suspension rigging cables produce a desired cable tension distribution to generate a wrench that reduces the effect of gravitational torque. In order to coordinate the robotic arm and the winch-based actuation, a model-based hierarchical whole-body controller is adapted. It resolves two tasks: keeping the robotic arm end-effector at the desired pose and shifting the system center of mass in the location with zero gravitational torque. The performance of the introduced actuation system as well as control strategy is validated through experimental studies.

This paper proposes a parameterization method to represent SO (3) over multiple turns. This method is called a memory-based parameterization, because the idea is to integrate the past trajectory of exponential coordinates. The parameterization is consistent in the sense that the true rotation matrix can be reconstructed by using the exponential map. As an application of the proposed method, a 6D impedance controller is designed with a radially unbounded potential function. Consequently, in contrast to the conventional methods, an arbitrarily large angular deflection can be accommodated, resulting in a more realistic impedance behavior. The proposed schemes are validated through simulations and experiments.

This article presents passive impedance control of flexible joint robots (FJRs) via inner-loop torque control of elastic joints. However, according to our theoretical analysis, the torque control methods of series elastic actuators (SEAs) are often limited by the fact that the acceleration signals are amplified by the control gains. Since the acceleration signals are often affected by differentiation noise, the analysis may become invalid in practice. To alleviate this limitation, we propose the use of the so-called series viscoelastic actuator (SvEA), which significantly reduces the acceleration amplification. Consequently, in contrast to the SEA case, the theoretical analysis of an SvEA-based FJR is valid in real implementations. We would like to highlight the fact that the theoretical analysis (more specifically, passivity analysis) is performed for nonlinear robot dynamics without linearization. As a result, the passive impedance controller can be realized more robustly with enhanced inner-loop torque control.

This paper presents a novel telepresence system for enhancing aerial manipulation capabilities. It involves not only a haptic device, but also a virtual reality that provides a 3D visual feedback to a remotely-located teleoperator in real-time. We achieve this by utilizing onboard visual and inertial sensors, an object tracking algorithm and a pregenerated object database. As the virtual reality has to closely match the real remote scene, we propose an extension of a marker tracking algorithm with visual-inertial odometry. Both indoor and outdoor experiments show benefits of our proposed system in achieving advanced aerial manipulation tasks, namely grasping, placing, force exertion and peg-in-hole insertion.

This paper presents a design of oscillation damping control for the cable-Suspended Aerial Manipulator (SAM). The SAM is modeled as a double pendulum, and it can generate a body wrench as a control action. The main challenge is the fact that there is only one onboard IMU sensor which does not provide full information on the system state. To overcome this difficulty, we design a controller motivated by a simplified SAM model. The proposed controller is very simple yet robust to model uncertainties. Moreover, we propose a gain tuning rule by formulating the proposed controller in the form of output feedback linear quadratic regulation problem. Consequently, it is possible to quickly dampen oscillations with minimal energy consumption. The proposed approach is validated through simulations and experiments.

Aerial robotic manipulation is an emergent trend that poses several challenges. To overcome some of these, the DLR cable-Suspended Aerial Manipulator (SAM) has been envisioned. SAM is composed of a fully actuated multi-rotor anchored to a main carrier through a cable and a KUKA LWR attached below the multi-rotor. This work presents a control method to allow SAM, which is a holonomically constrained system, to perform such interaction tasks using a hierarchical control framework. Within this framework, compliance control of the manipulator end-effector is considered to have the highest priority. The second priority is the control of the oscillations induced by, for example, the motion of the arm or physical contact with the environment. A third priority task is related to the internal motion of the manipulator. The proposed approach is validated through simulations and experiments.

High risk of a collision between rotor blades and the obstacles in a complex environment imposes restrictions on the aerial manipulators. To solve this issue, a novel system cable-Suspended Aerial Manipulator (SAM) is presented in this paper. Instead of attaching a robotic manipulator directly to an aerial carrier, it is mounted on an active platform which is suspended on the carrier by means of a cable. As a result, higher safety can be achieved because the aerial carrier can keep a distance from the obstacles. For self-stabilization, the SAM is equipped with two actuation systems: winches and propulsion units. This paper presents an overview of the SAM including the concept behind, hardware realization, control strategy, and the first experimental results.

This paper tackles a friction compensation problem without using a friction model. The unique feature of the proposed friction observer is that the nominal motor-side signal is fed back into the controller instead of the measured signal. By doing so, asymptotic stability and passivity of the controller are maintained. Another advantage of the proposed observer is that it provides a clear understanding for the stiction compensation which is hard to be captured in model-free approaches. This allows to design observers that do not overcompensate for the stiction. The proposed scheme is validated through simulations and experiments.

This paper presents a passive compliance control for aerial manipulators to achieve stable environmental interactions. The main challenge is the absence of actuation along body-planar directions of the aerial vehicle which might be required during the interaction to preserve passivity. The controller proposed in this paper guarantees passivity of the manipulator through a proper choice of end-effector coordinates, and that of vehicle fuselage is guaranteed by exploiting time domain passivity technique. Simulation studies validate the proposed approach.

This paper presents that the joint torque control capability can be enhanced by adding physical damper to a series elastic actuator (SEA). Joint torque tracking of standard SEA has known limitations that the torque dynamics has an relative order of two, and, as a consequence, the torque controller often requires acceleration feedback when the desired torque is defined by a function of velocity (for example, compliance control). This limitation can be removed by introducing physical damping, reducing the relative degree of torque dynamics by one. Based on this observation, we design a robust controller using the disturbance observer technique. The resulting control law is given by a feed-forward term combined with PI control. The proposed controller is verified in simulation and experiment.

Position-based proportional derivative (PD) controllers are known to be able to render compliant behaviors to a robot, and they are usually used in conjunction with a friction compensator to improve control performance. Existing methods are effective when applied to actuation systems with mechanical transmissions; however, they cannot be applied to actuation systems that use fluid transmissions owing to the characteristics resulting from fluid parameters. To solve this problem, we propose a stability-guaranteed PD control method that incorporates two observers: one for observing friction and the other is for observing flexible joint effects due to fluid compliance and internal leakage. This allows robots with fluid transmissions to asymptotically converge to the desired position. We verified the proposed approach by conducting simulations and experiments.

This paper proposes a passivity-based nonlinear disturbance observer (DOB) design for a powered upper-limb robot control. The proposed DOB allows for the nonlinearities of the robot dynamics, whereas the typical DOB designs cannot. Moreover, by virtue of the passivity property, human operator and environmental interactions can be embedded in the control loop. As a DOB, the proposed approach has a disturbance observation property that makes the actual robot behave like a nominal model selected by the user. Performance analysis proposes a gain tuning rule. In experimental validation, actual powered upper-limb robot is used to perform unknown payload carrying applications.

This paper proposes a simple model-free joint torque control strategy for hydraulic robots. The undesirable effect called “natural velocity feedback effect” is discussed by introducing mechanical impedance of the system. The proposed model-free joint torque control consists of internal-loop control and external-loop control (2 degrees of freedom (DOF)). Based on a mechanically represented hydraulic robot joint, the first-order internal-loop control is designed to reduce the mechanical impedance of a hydraulic robot joint in a passive manner. To improve joint torque tracking performance, a simple proportional-integral-derivative external-loop control is applied to the modified system by the internal-loop control. Robustness to external disturbance is demonstrated by transforming the proposed 2DOF control structure to the disturbance observer form. The proposed joint torque control strategy is validated on a 1DOF rotary hydraulic robot joint.

Admittance control is a well-established and popular control strategy in modern robotics. However, the standard admittance controller has several limitations. First, the passivity can be guaranteed by finding a set of control parameters that makes the admittance function positive real. However, this approach cannot be applied to multi degrees-of-freedom robot because it requires transfer function analysis. Second, standard admittance controller is not suitable when the system is exposed to unexpected environmental interaction (of which interaction force is not measured) due to the wall sticking effect. To overcome these limitations, this paper proposes an admittance controller of which structure can be constructed by feedback interconnection of passive subsystems. The proposed approach was verified using experiments and simulations.

This paper proposes joint torque servo control of an electro-hydrostatic actuator (EHA) for effective dynamic control of robot manipulators. In the control design, the fluid parameters are not considered to develop a simple low-level controller used for multi degree-of-freedom (DOF) robot manipulators. Instead, mechanical impedance, which represents the effect of joint velocity on joint torque, is reduced using a disturbance observer. The torque tracking performance of the modified system is then improved by a simple proportional-integral-derivative control. When evaluated on two 1-DOF EHAs, the proposed controller guaranteed robustness to external disturbances and variations in the fluid parameters, even though the fluid parameters are not considered.

Carrying unknown heavy payload is one of the classical problems in robotics. To realize heavy payload capability, the use of high reduction gears, which causes large motor-side inertia and large friction, is inevitable. This paper proposes a disturbance observer (DOB)-based approach that (1) reduces highly amplified motor-side inertia (2) compensates for large friction (3) compensates for the unknown heavy payload without sensory information. However, in principle, the DOB is applicable only for linear system. To overcome this, a concept of effective joint torque is introduced, which allows us to separate linear motor-side dynamics from the nonlinear robot dynamics. As a result, it becomes possible to apply high order DOB to compensate for the uncertainties. Mathematical analysis shows closed-loop stability, optimality, and passivity. Experiments and simulations are performed to verify the method.

This paper proposes a robust PD control scheme for flexible-joint robots based on a disturbance observer (DOB). In this paper, the DOB is applied only to the motor-side dynamics of the robot, and the uncertainties on the motor-side are successfully eliminated. It is shown that the proposed DOB-based approach guarantees global asymptotic stability. To this end, two special treatments are required. First, unlike the typical configuration of the DOB, nominal states of the motor-side are fed back to the PD controller. Second, a control input that makes the nominal states stable is additionally introduced. The proposed approach was verified using multi-degree-of-freedom experiments.

This paper proposes a framework called nonlinear robust internal-loop compensator that enables us to bring nonlinear H∞optimality to robot controllers in a unified and simple way. Using the framework, a controller designed for the nominal plant can achieve additional robustness by simply adding PID-type auxiliary input to the original control law. Robust performance is guaranteed by the nonlinear 1-1 optimality and robust stability is guaranteed by proving the extended disturbance input-to-state stability. Moreover, the framework preserves the passivity property of the original controller. Finally, the performance bound can be predicted and leads to the gain tuning rules. By virtue of the tuning rules, the performance can be tuned using only a single variable. The proposed method was validated through the simulations and experiments.

This paper proposes a robust control scheme based on a disturbance observer (DOB) for flexible joint robots. In this paper, the DOB is applied only on the motor-side dynamics of the robot, and the uncertainties on the motor-side are successfully eliminated. It is shown that, to guarantee the stability, the estimated motor-side position should be fed back into the controller, which is different from usual setup of the DOB. The experiments/simulations show that the estimated signal feedback indeed guarantees the stability, whereas the measured signal feedback does not.

In this paper, nonlinear H∞optimal design of impedance controllers is proposed based on the nonlinear robust internal-loop compensator (NRIC) framework. Simply adding PD-type auxiliary input to the original control law, the robust performance and the robust stability are achieved. Nonlinear H∞optimality is guaranteed by solving Hamilton-Jacobi-Isaacs (HJI) equation and the disturbance input-to-state stability (ISS) is guaranteed by finding ISS-Lyapunov function. Moreover, it is shown that the proposed method preserves the passivity of the impedance controllers. The proposed method can be applied to various types of impedance controllers in a unified way. Through simulations and experimental studies, the proposed method is verified.

Robustness is a very classical issue in the robotics field. Disturbance observer (DOB) can be a good choice to improve the robustness of the system. It is easy to implement and shows successful results. DOB, however, cannot bring nonlinearity of the system into the formulation. To overcome this problem, nonlinear robust internal loop compensator (NRIC) is proposed in this paper. NRIC is attached to a existing controller and improves robustness of the system like DOB. NRIC makes an auxiliary input that compensates the difference between the output from the real plant and that from the nominal model. An auxiliary input is designed in a nonlinear ℋ∞optimal control framework and the resulting input is given as a simple PID form. The stability of a real plant is guaranteed if a controller is designed as an exponentially stable one (a sufficient condition). The benefit of NRIC is that it is easy to implement and it effectively brings nonlinearities of the system into the formulation successfully. The performance of NRIC is verified through the simulation and experiments.

In this paper, an advanced preoperative planning framework for spinal fusion is presented. The framework is based on spinal pedicle data obtained from computed tomography (CT) images, and provides optimal insertion trajectories and pedicle screw sizes. The proposed approach begins with a safety margin estimation for each potential insertion trajectory that passes through the pedicle volume, followed by procedures to collect a set of insertion trajectories that satisfy operation safety objectives. Among the trajectory candidates, the insertion trajectory, which maximizes the insertable depth of a pedicle screw into the vertebral body, is then chosen as optimal, because the insertable depth enhances the strength of the transpedicular screw-vertebra interface after spinal fusion surgery. The radius of a pedicle screw was chosen as 70% of the pedicle radius. This framework has been tested on 68 spinal pedicles of 8 patients requiring spinal fusion. It was successfully applied, resulting in an average success rate of 100% and a final safety margin of 2.11±0.17mm.