ICRA 2019 Papers

Past work on acquisition of word-object associations in robots has focused on either fast instruction-based methods which accept highly constrained input or gradual cross-situational learning methods, but not a mixture of both. In this paper, we present an integrated robotic system which allows for a combination of these methods to contribute to the task of learning the labels of objects in AI agents. We demonstrate the expanded word learning capabilities in the outcome system and how learning from both human-human and human-robot dialogues can be achieved in one integrated system.

Highly accurate localization with very limited amount of memory and computational power is one of the big challenges for next generation series cars. We propose localization based on geometric primitives which are compact in representation and further valuable for other tasks like planning and behavior generation. The primitives lack distinctive signature which makes association between detections and map elements highly ambiguous. We resolve ambiguities early in the pipeline by online building up a local map which is key to runtime efficiency. Further, we introduce a new framework to fuse association and odometry measurements based on robust pose graph optimization.We evaluate our localization framework on over 30 min of data recorded in urban scenarios. Our map is memory efficient with less than 8 kB/km and we achieve high localization accuracy with a mean position error of less than 10 cm and a mean yaw angle error of less than 0. 25° at a localization update rate of 50Hz.

In this paper, we address the problem of combining 3D laser scanner and camera information to estimate the motion of a mobile platform. We propose a direct laser-visual odometry approach building upon photometric image alignment. Our approach is designed to maximize the information usage of both, the image and the laser scan, to compute an accurate frame-to-frame motion estimate. To deal with the sparsity of the range measurements, our approach identifies planar point sets within individual point clouds and subsequently extract their corresponding pixel patches from the camera image. The extracted planar image patches are used together with the non-planar pixels to estimate the frame-to-frame motion using a homography formulation capable of incorporating both types of pixel alignments. To achieve high estimation accuracy, we explicitly predict possible occlusions caused by observations taken from different locations. We evaluate our proposed approach using the KITTI dataset as well as data recorded with a Clearpath Husky platform. The experiments suggest that our approach can achieve competitive estimation accuracy and produce consistently registered, colored point clouds.

To minimize the operation time, mobile manipulators need to pick-up parts while the mobile base and the gripper are moving. The gripper speed needs to be selected to ensure that the pick-up operation does not fail due to uncertainties in part pose estimation. This, in turn, affects the mobile base trajectory. This paper presents an active learning based approach to construct a meta-model to estimate the probability of successful part pick-up for a given level of uncertainty in the part pose estimate. Using this model, we present an optimization-based framework to generate time-optimal trajectories that satisfy the given level of success probability threshold for picking-up the part.

The task of robotic mobile manipulation poses several scientific challenges that need to be addressed to execute complex manipulation tasks in unstructured environments, in which collaboration with humans might be required. Therefore, we present ALMA, a motion planning and control framework for a torque-controlled quadrupedal robot equipped with a six degrees of freedom robotic arm capable of performing dynamic locomotion while executing manipulation tasks. The online motion planning framework, together with a whole-body controller based on a hierarchical optimization algorithm, enables the system to walk, trot and pace while executing operational space end-effector control, reactive human-robot collaboration and torso posture optimization to increase the arm's workspace. The torque control of the whole system enables the implementation of compliant behavior, allowing a user to safely interact with the robot. We verify our framework on the real robot by performing tasks such as opening a door and carrying a payload together with a human.

Autonomous driving has gained significant advancements in recent years. However, obtaining a robust control policy for driving remains challenging as it requires training data from a variety of scenarios, including rare situations (e.g., accidents), an effective policy architecture, and an efficient learning mechanism. We propose ADAPS for producing robust control policies for autonomous vehicles. ADAPS consists of two simulation platforms in generating and analyzing accidents to automatically produce labeled training data, and a memoryenabled hierarchical control policy. Additionally, ADAPS offers a more efficient online learning mechanism that reduces the number of iterations required in learning compared to existing methods such as DAGGER [1]. We present both theoretical and experimental results. The latter are produced in simulated environments, where qualitative and quantitative results are generated to demonstrate the benefits of ADAPS.

In this work, we are proposing an intuitive Augmented SLAM method (A-SLAM) that allows the user to interact, in real-time, with a robot running SLAM to correct for pose and map errors. We built an AR application that works on HoloLens and allows the operator to view the robot's map superposed on the physical environment and edit it. Through map editing, the operator can account for errors affecting real environment's representation by adding navigation-forbidden areas to the map in addition to the ability to correct errors affecting the localization. The proposed system allows the operator to edit the robot's pose (based on SLAM request) and can be extended to sending navigation goals to the robot, viewing the planned path to evaluate it before execution, and teleoperating the robot. The proposed solution could be applied on any 2D-based SLAM algorithm and can easily be extended to 3D SLAM techniques. We validated our system through experimentation on pose correction and map editing. Experiments demonstrated that through A-SLAM, SLAM runtime is cut to half, post-processing of maps is totally eliminated, and high quality occupancy grid maps could be achieved with minimal added computational and hardware costs.

This paper presents a novel sensing approach for the physical interaction between a human user and a serial robotic arm. The approach is inspired from the concept of macro-mini robot architecture. The framework is developed for a general multi-degree-of-freedom serial robot and a corresponding impedance control scheme is proposed. In order to illustrate the concept, a five-degree-of-freedom robotic arm was built as well as a six-degree-of-freedom low-impedance sensing device that is used to control the robot. Experimental results are provided.

Robotization of medical acts often requires the evaluation of contacts between a robotic system and a patient, for safety or efficiency reasons. When contact occurs with a stiff environment, instabilities and vibrations can appear and a passive compliance is therefore needed. In this paper, we propose to embed compliance in a force sensor and to develop a novel force sensor with large compliance, i.e. a zero stiffness at contact transition to ease robot control. To get at the same time a satisfying measurement range and low off-axis sensitivity, an optical measurement process based on an optical line generated thanks to additive manufacturing is exploited. A compliant sensor body allowing the desired stiffness profile is presented and the specific optical measurement technique is developed. Finally, a prototype of the proposed force sensor is evaluated experimentally.

In this work we present the design and application of an active soft fingertip for robotic hands. This fingertip is based on a new type of hydrogel which has been designed with the purpose of overcoming some of the major drawbacks of previous hydrogels such as the dependency of aqueous solutions. Fingertip applications benefit from the changes of stiffness and volume which take place in our hydrogel when electric fields are applied. Theoretical modeling and experimental verification of the fingertip properties are presented in this work, showing its potential usability in grasping and manipulation tasks.

The amazing ability of water striders on water surface has attracted many scholars. Especially the flexible driving mechanism enable the driving legs conform to the deformation of the water surface, which effectively improving water striders’ floating ability and stability. However, the current research on water striders has never designed a flexible driven robot prototype like water striders. This paper proposes a new water strider robot that can walk on water surface based on flexible driving mechanism. The robot’s driving legs are designed with flexible materials and possess ellipse-like spatial trajectories like water striders through a limit pin-linkage mechanism. Based on microelement cantilever method, the flexible driving effect was analyzed with different elastic modulus and diameter. It shows that the flexible legs can row at a higher frequency before puncturing the water surface and achieve bigger work in one period compared with the rigid one. At last, the skating experiment of the robot under different stiffness and rowing frequency was carried out. The results verified that the limit frequency of the flexible driving legs and maximum moving speed of the robot are about 41.3% and 36.2% higher than those with rigid legs, respectively. Moreover, a similarity analysis of hydrodynamic characteristic constants reveals that the locomotion of the flexible driving robot is more analogous to the biological water striders than the rigid one.

This paper presents an approach for a car navigation system to follow a road path consisting on a sequence of lanelets. The motion control is divided into high-level planning that produces the road path and lower-level reactive control that safely follows the path. The approach presented here is the lower-level reactive control that combines the simple pure pursuit method to obtain a reference curvature with the beam curvature method (BCM) that keeps the car in the center of the free space in the lane avoiding obstacles that can partially block the lane. The whole system has been applied to an autonomous vehicle aimed for elderly or disable people.

In this contribution, a new adaptive motion cueing algorithm for a full motion driving simulator at the University of Stuttgart is presented, which allows kinematic vehicle movements to be taken into account. These are adequately processed via a state-flow chart and transferred to the motion cueing algorithm in such a way that the dynamic of the Stuttgart Driving Simulator can be used much more efficiently. Furthermore, a linear quadratic error minimization of the mentioned algorithm is presented. The primary objective is to provide a more realistic driving experience to the driver.

In order to improve the machining efficiency and the flexibility of manufacturing system, the study of multi-duty parallel manipulators has attracted the interest of some researchers. In this paper, according to the effects of different operations on the driving element, a demarcation diagram for distinguishing different duties, such as statics, low-speed but heavy-load, high-speed but low-load and high-speed but heavy-load, is proposed, and a defect identification approach to prevent the occurrence of defects for multi-duty parallel manipulators is presented. Taking the 1PU+3UPS parallel manipulator as an instance, an analysis method to the statics and dynamics is investigated by means of the screw theory and the proposed virtual screw. Based on the numerical example, the results show that the classification and practicability of operations can be accurately identified by the proposed demarcation diagram and defect identification approach, respectively.

This paper presents an interconnected control-planning strategy for redundant manipulators, subject to system and environmental constraints. The method incorporates low-level control characteristics and high-level planning components into a robust strategy for manipulators acting in complex environments, subject to joint limits. This strategy is formulated using an adaptive control rule, a computational efficient estimation of the robot's mathematical model and the nullspace of the constraints. A path is generated that takes into account the capabilities of the platform. The proposed method is computationally efficient, enabling its implementation on a real multi-body robotic system. Through experimental results with a 7 degree-of-freedom (DOF) manipulator, we demonstrate the performance of the method in real-world scenarios.

Telepresence robots have been recently used for Comprehensive Geriatric Assessment (CGA). Since the robot can not track a person continuously, there are several strategies to decide when to check them, from cyclic checks to simple requests from users and/or caregivers. In order to adapt to the user needs and condition, it is preferable to perform CGA as soon as regularities appear. However, this requires detection of potential issues in users to offer immediate service. In this work we propose a new low cost force sensor system to detect user's condition and attract attention of CGA robots, so they can perform a full examination on a need basis. The main advantages of this system are: i) it can be attached to any standard commercial cane; ii) its power consumption is very reduced; and iii) it provides continuous information as long as the user walks. It has been tested with several elderly volunteers in care facilities. Results have proven that the sensor readings are indeed correlated with the users' condition.

Remoras employ their adhesive discs to rapidly attach to and detach from a wide range of marine surfaces. By analyzing high-speed images of remoras' (Echeneis naucrates) hitchhiking behavior, we describe the fish's detachment mechanism as a lip curling up to break the seal between the disc and substrate. By mimicking the kinematic and morphological properties of the biological disc, we fabricated a multi-material biomimetic disc (whose stiffness spans four orders of magnitude) that is capable of both attachment and detachment. Detachment is realized by a flexible cable-driven mechanism that curls the anterior region of the silicone soft lip, allows leakage under the disc, and equalizes the internal pressure to the external pressure. The disc lamellae with attached carbon fiber spinules can be rotated by hydraulic soft actuators whose internal pressure is precisely tuned to the ambient underwater pressure. During attachment, increasing the rotational angle of the lamellae and the preload of the disc significantly enhanced the adhesive forces. We found that curling up the soft lip and folding down the lamellae rapidly reduced the pulling force of the disc by a factor of 254 compared to that under the attached state, which lead to detachment. Based on these mechanisms, underwater maneuvers involving repeated attachment and detachment were demonstrated with an integrated ROV unit that had a self-contained actuation and control system for the disc. This study lays a foundation for the development of fully untethered robotic systems for underwater hitchhiking in real-world marine environments.

We present a Bayesian object observation model for complete probabilistic semantic SLAM. Recent studies on object detection and feature extraction have become important for scene understanding and 3D mapping. However, 3D shape of the object is too complex to formulate the probabilistic observation model; therefore, performing the Bayesian inference of the object-oriented features as well as their pose is less considered. Besides, when the robot equipped with an RGB mono camera only observes the projected single view of an object, a significant amount of the 3D shape information is abandoned. Due to these limitations, semantic SLAM and viewpoint-independent loop closure using volumetric 3D object shape is challenging. In order to enable the complete formulation of probabilistic semantic SLAM, we approximate the observation model of a 3D object with a tractable distribution. We also estimate the variational likelihood from the 2D image of the object to exploit its observed single view. In order to evaluate the proposed method, we perform pose and feature estimation, and demonstrate that the automatic loop closure works seamlessly without additional loop detector in various environments.