IROS 2019 Papers

This paper reports the design and evaluation of a novel robotic device for cornea transplant surgery. The device enables the OCT-sensor guided Big Bubble hydro-dissection approach for deep anterior lamellar keratoplasty (DALK) cornea transplant surgery. DALK is highly challenging because it requires precise placement of a needle into the stroma of the cornea down to Descemets Membrane (DM) and injects a fluid to separate the remaining stroma from Descemet's membrane. Finally, the stroma is removed and replaced with the donor cornea graft. Compared to traditional penetrating keratoplasty (PK), which involves a full-thickness graft, this method significantly reduces the risk of rejection of the donor cornea by keeping the DM intact. A comparison of autonomous OCT guided needle insertions with expert manual needle insertions showed that the device significantly increased the precision and consistency of the needle placement, which could lead to better visual outcomes and fewer complications. In a study on cadaver porcine eyes, the measured insertion depth as a percentage of cornea thickness for the robotic device was 90.05% +/- 2.33% compared to 79.16% +/- 5.68% for manual insertions.

Torque controlled robots have the capability of implementing compliant behavior with back-drivability. In practice, however, joint elasticity often prevents an accurate position tracking performance of a robot. In particular, humanoid robots are influenced more by elasticity because of a long kinematic chain between the feet and hands. In this paper, we present a new inverse dynamics based control approach for torque controlled humanoid robots with joint elasticity. When formulating the operational space control framework, feedback control consists of only motor-related parts with measured motor angle values, and the link dynamics is compensated by the feedforward terms. The experiment results of the proposed approach show a noticeable improvement in the position tracking performance in 6-DoF manipulator. Finally, the proposed method was applied to a torque controlled biped robot for walking. Both stiff motion control of the CoM and compliant motion control of the foot were simultaneously achieved, demonstrating the advantage of the torque controlled robot.

This paper presents a soft manipulator which mimics the muscle structure of an octopus arm and its operation by the master-slave method by using a wearable interface. The soft manipulator consists of a rubber structure and McKibben artificial muscles arranged in axial and oblique directions. This manipulator has high safety and shape adaptability and realizes smooth and continuous movement like the octopus arm such as bending and twisting. For the intuitive operation of the manipulator, the wearable interface using flexible strain sensors is developed. It is easy to wear on the human arm with high comfortability because it consists only of soft materials and stretches easily with following the arm motion. We can operate the soft manipulator by using the motions of the wrist and forearm as an operation input. The soft manipulator can move following the arm motion of the operator, and it shows the high possibility to conduct actual tasks.

Actively exploring and mapping an unknown environment requires integration of both simultaneous localization and mapping (SLAM) and path planning methods. Path planning relies on a map that contains free and occupied space information and is efficient to query, while the role of SLAM is to keep the map consistent as new measurements are continuously added. A key challenge, however, lies in ensuring a map representation compatible with both these objectives: that is, a map that maintains free space information for planning but can also adapt efficiently to dynamically changing pose estimates from a graph-based SLAM system. In this paper, we propose an online global occupancy map that can be corrected for accumulated drift efficiently based on incremental solutions from a sparse graph-based SLAM optimization. Our map maintains free space information for real-time path planning while undergoing a bounded number of updates in each loop closure iteration. We evaluate performance for both simulated and real-world datasets for an application involving underwater exploration and mapping.

As humans and robots work more and more closely, robots must quickly react to unforeseen human behavior. Online Trajectory Generation (OTG), based on simple trajectory models like series of polynomial cubic functions, has demonstrated its efficiency to plan and control reactive motions of robots. However to ensure the safety and comfort of humans, fast trajectory adaptation algorithms are necessary to bring back the robot inside an acceptable domain that is defined by a set of kinematic constraints. The algorithm presented herein extends a time-optimal OTG to cope with non admissible robot's state. This feature enables time-variant kinematic constraints. With the possibility to specify velocity and acceleration at both ends under short computation times, it makes the robot able to react quickly to unforeseen events. Short computation times can lead to more refined architectures where sensors can be integrated to a low control level and make the system more reactive.

This work deals with a moving target chasing mission of an aerial vehicle equipped with a vision sensor in a cluttered environment. In contrast to obstacle-free or sparse environments, the chaser should be able to handle collision and occlusion together with flight efficiency. In order to tackle these challenges in real-time, we introduce a metric for target visibility and propose a hierarchical chasing planner. In the first phase, we generate a sequence of waypoints and chasing corridors which ensure safety and optimize visibility. In the following phase, the corridors and waypoints are utilized as constraints and objective respectively in quadratic programming from which we complete a dynamically feasible trajectory for chasing. The proposed algorithm is tested in multiple dense environments. The simulator AutoChaser with full code implementation & GUI can be found in https://github.com/icsl-Jeon/traj_gen_vis and video is available at https://youtu.be/-2d3uDlYR_M.

This paper investigates an online system identification problem of estimating unknown parameters in nonlinear system dynamics in the absence of persistently excitation. To estimate parameters, we develop an algorithm that updates parameter estimates using sensor data and a basis that is built on a finite number of recorded sensor data. Based on our proposed approach we show that the algorithm achieves exponential convergence in both state and parameter estimation errors without the persistent excitation condition. We demonstrate the effectiveness of the proposed approach using both simulations and experiments on a reconfiguration autonomous multi-vessel platform: Simulation results illustrate that the parameter estimated by the developed algorithm converge to their ground truths. Experiment results validate the performance of the developed algorithm in estimating platform's system parameters across different multi-vessel configurations.

We present a unified control framework that generates dynamic walking motions for biped and quadruped robots with online relative footstep optimization. The footstep optimization is formulated as a discrete-time Model Predictive Control problem which determines future footstep locations. The framework has a hierarchical structure consisting of three layers: footstep planner, trajectory generator and whole-body controller. The footstep planner plans next footstep position based on Linear Inverted Pendulum (LIP) model. Relative footstep optimization is proposed to enable automatic footstep planning without the use of any predefined footstep sequences. The trajectory generator will generate CoM and feet trajectory given the next footstep placement. In order to generalize to quadruped robots, “virtual leg” concept has been used to coordinate leg pair movement. The whole-body inverse dynamic controller calculates joint torques to track given Cartesian reference trajectories. To include under-actuation into consideration, contact vertices formulation of ground reaction forces (GRFs) has been adopted. Generalized whole-body controller can handle biped robot with line feet as well as quadruped robots with point feet walking with dynamic gaits. Several simulations have been performed to demonstrate the robustness and generality of the proposed framework.

We present an anytime Monte Carlo tree search (MCTS) algorithm to generate real-time, near-optimal search paths in large subsea environments. The MCTS planner continuously builds a tree of the search space until either the allowed time per move is reached or the budget constraint for the search mission is met. In order to improve the performance of the MCTS planner, we propose a novel heuristic action selection policy to determine the value of a leaf node. The proposed heuristic is tailored to problems where making a turn incurs a higher cost than moving straight, such as the case on autonomous underwater vehicles. Through extensive simulations, we show that our heuristic yields a significant performance improvement over a lawnmover path planner - a commonly employed approach in subsea search applications - and over a simple MCTS planner where actions are selected uniformly at random. In our numerical illustrations, we use a real data set abstracted from sonar measurements acquired from the Boston Harbor.

The capacity of a system to automatically analyze and predict the performance of a human in a particular task can provide important information in Human-Robot Interaction. Despite its usefulness, the above topic has received rather limited attention in the literature. In the current work, we introduce a method for performance prediction and profiling of human activities. Using little information about a task, our method is able to extract the characteristic motion patterns of an agent, analyze them and predict his/her performance in a given activity. We demonstrate the robustness of the method in several different activities, that involve both periodic and oscillatory primitive motions. In addition, we evaluate it thoroughly on data obtained from public datasets and discuss its usefulness for contemporary robotic applications.

Real world applications require robots to operate in unstructured environments. This kind of scenarios may lead to unexpected environmental contacts or undesired interactions, which may harm people or impair the robot. Adjusting the behavior of the system through impedance control techniques is an effective solution to these problems. However, selecting an adequate impedance is not a straightforward process. Normally, robot users manually tune the controller gains with trial and error methods. This approach is generally slow and requires practice. Moreover, complex tasks may require different impedance during different phases of the task. This paper introduces an optimization algorithm for online planning of the Cartesian robot impedance to adapt to changes in the task, robot configuration, expected disturbances, external environment and desired performance, without employing any direct force measurements. We provide an analytical solution leveraging the mass-spring-damper behavior that is conferred to the robot body by the Cartesian impedance controller. Stability during gains variation is also guaranteed. The effectiveness of the method is experimentally validated on the quadrupedal robot ANYmal. The variable impedance helps the robot to tackle challenging scenarios like walking on rough terrain and colliding with an obstacle.

Efficient planning in dynamic and uncertain environments is a fundamental challenge in robotics. In the context of trajectory optimization, the feasibility of paths can change as the environment evolves. Therefore, it can be beneficial to reason about multiple possible paths simultaneously. We build on prior work that considers graph-based trajectories to find solutions in multiple homotopy classes concurrently. Specifically, we extend this previous work to an online setting where the unreachable (in time) part of the graph is pruned and the remaining graph is reoptimized at every time step. As the robot moves within the graph on the path that is most promising, the pruning and reoptimization allows us to retain candidate paths that may become more viable in the future (a) as the environment changes, essentially enabling the robot to dynamically switch between numerous homotopy classes. We compare our approach against prior work without the homotopy switching capability and show improved performance across several metrics in simulation with a 2D robot in multiple dynamic environments under noisy measurements and execution.

Future manufacturing environments will see an increased need for cooperation between humans and machines. In this paper we propose a method that allows industrial manipulators to safely operate around humans. This approach guarantees that the manipulator will never collide with human operators while performing its normal tasks. This is done in an near-optimal way by considering how forward reachable sets of human operators grow with time, and by continuously updating these reachable sets based on current position estimates of the operators near the robot. An implicit active set invariance filter is then used to constrain the system-in a minimally invasive way-to stay in the complement of that forward reachable set. We demonstrate this approach in simulation on an industrial robotic arm: the ABB IRB 6640.

A robot that can operate in novel and unstructured environments must be capable of recognizing new, previously unseen, objects. In this work, a visual cue is used to specify a novel object of interest which must be localized in new environments. An end-to-end neural network equipped with a Siamese network is used to learn the cue, infer the object of interest, and then to localize it in new environments. We show that a simulated robot can pick-and-place novel objects pointed to by a laser pointer. We also evaluate the performance of the proposed approach on a dataset derived from the Omniglot handwritten character dataset and on a small dataset of toys.

We consider the problem of learning multi-stage vision-based tasks on a real robot from a single video of a human performing the task, while leveraging demonstration data of subtasks with other objects. This problem presents a number of major challenges. Video demonstrations without teleoperation are easy for humans to provide, but do not provide any direct supervision. Learning policies from raw pixels enables full generality but calls for large function approximators with many parameters to be learned. Finally, compound tasks can require impractical amounts of demonstration data, when treated as a monolithic skill. To address these challenges, we propose a method that learns both how to learn primitive behaviors from video demonstrations and how to dynamically compose these behaviors to perform multi-stage tasks by “watching” a human demonstrator. Our results on a simulated Sawyer robot and real PR2 robot illustrate our method for learning a variety of order fulfillment and kitchen serving tasks with novel objects and raw pixel inputs. Video results are linked at https://sites.google.com/view/one-shot-hil.

The capability to precisely rotate the cells and other microscale objects is invaluable in biomedicine, bioengineering, and biophysics. We propose a novel on-chip three-dimension (3D) cell rotation method using whirling flows generated by oscillating asymmetrical microstructures. In an acoustic field excited by the vibration of a piezoelectric transducer, two different modes of microvortices are generated around our custom-designed microstructures that are utilized to precisely achieve in-plane and out-of-plane rotational manipulation of microparticles and cells. The rotation mechanism is studied and verified using numerical simulations. We also investigate the effect of various parameters on the acoustically induced flows such as the frequency, the driving voltage and the distance from the microstructure tip to the oocyte center, thus indicating the rotational speed can be effectively tuned on demand for single-cell studies. Finally, by observing the maturation stages of M2 after excluding the first polar body of operated oocytes, the proposed method is proved noninvasive. Comparing with the conventional works, our acoustofluidic cell rotation approach is simple-to-fabricate and easy-to-operate, thereby allowing rotations irrespective of the physical properties of the specimen under investigation.

This paper studies the effects of varying rollover curvature on the passive dynamic gait of a biped walker. The dynamic model of a compliant biped robot is extended with the implementation of semielliptical feet, to mimic human rollingradius progression during a step. The process of modeling the semielliptical foot shape and integrating its kinematics to the biped’s dynamics is presented in detail. The passive dynamic behavior of the biped for elliptic feet of various dimensions is investigated through numerical simulations to provide results about gait stability, walking speed, energetic efficiency, and impact force levels. The concept of energetic efficiency in passive walking is discussed thoroughly, and an efficiency comparison methodology is proposed. Finally, it is shown that the biomimetically-inspired semielliptical foot profile can lead to higher gait efficiency. The results of this study can be used to optimize energetic efficiency in biped walking machines and/or gait assisting prosthetic equipment by means of foot shape optimization.

We report a tunable sparse optimization solver that can trade a slight decrease in accuracy for significant speed improvement in pose graph optimization in visual simultaneous localization and mapping (vSLAM). The solver is designed for devices with significant computation and power constraints such as mobile phones or tablets. Two approaches have been combined in our design. The first is a graph pruning strategy by exploiting objective function structure to reduce the optimization problem size which further sparsifies the optimization problem. The second step is to accelerate each optimization iteration in solving increments for the gradient-based search in Gauss-Newton type optimization solver. We apply a modified Cholesky factorization and reuse the decomposition result from last iteration by using Cholesky update/downdate to accelerate the computation. We have implemented our solver and tested it with open source data. The experimental results show that our solver can be twice as fast as the counterpart while maintaining a loss of less than 5% in accuracy.

Stability and transparency are key design requirements in haptic devices. Transparency can be significantly improved by replacing conventional electric motors with passive actuators such as brakes or dampers. Passive actuators can display a wide range of impedance and since they can only dis-sipate energy, stability is guaranteed. However, passive haptic devices suffer from a serious drawback; the direction of the force output is difficult to control. This issue was addressed extensively for planar manipulators but devices with higher degrees-of-freedom (DOF) have not been examined. In this paper, we introduce a new analytical framework to evaluate the feasibility and performance of non-redundant passive haptic manipulators with any DOF. The method identifies different regions in the workspace where a force can be created or approximated, and regions where a passive system cannot create force at all for a given user input. The results indicate that the range of forces a passive device can display increases with the number of DOF. This framework can aid in the design of control methods for multi-DOF passive haptic devices.