ICRA 2018 论文列表

Path planning classically focuses on avoiding environmental contact. However, some assembly tasks permit contact through compliance, and such contact may allow for more efficient and reliable solutions under action uncertainty. But, optimal manipulation plans that leverage environmental contact are difficult to compute. Environmental contact produces complex kinematics that create difficulties for planning. This complexity is usually addressed by discretization over state and action space, but discretization quickly becomes computationally intractable. To overcome the challenge, we use the insight that only actions on configurations near the contact manifold are likely to involve complex kinematics, while segments of the plan through free space do not. Leveraging this structure can greatly reduce the number of states considered and scales much better with problem complexity. We develop an algorithm based on this idea and show that it performs comparably to full MDP solutions at a fraction of the computational cost.

In this paper, we study the problem of autonomous exploration in unknown indoor environments using mobile robot. We use mutual information (MI) to evaluate the information the robot would get at a certain location. In order to get the most informative sensing location, we first propose a sampling method that can get random sensing patches in free space. Each sensing patch is extended to informative locations to collect information with true values. Then we use Gaussian Markov Random Fields (GMRF) to model the distribution of MI in environment. Compared with the traditional methods that employ Gaussian Process (GP) model, GMRF is more efficient. MI of every sensing location can be estimated using the training sample patches and the established GMRF model. We utilize an efficient computation algorithm to estimate the GMRF model hyperparameters so as to speed up the computation. Besides the information gain of the candidates regions, the path cost is also considered in this work. We propose a utility function that can balance the path cost and the information gain the robot would collect. We tested our algorithm in both simulated and real experiment. The experiment results demonstrate that our proposed method can explore the environment efficiently with relatively shorter path length.

In the context of robotic manipulation and grasping, the shift from a view that is static (force closure of a single posture) and contact-deprived (only contact for force closure is allowed, everything else is obstacle) towards a view that is dynamic and contact-rich (soft manipulation) has led to an increased interest in soft hands. These hands can easily exploit environmental constraints and object surfaces without risk, and safely interact with humans, but present also some challenges. Designing them is difficult, as well as predicting, modelling, and “programming” their interactions with the objects and the environment. This paper tackles the problem of simulating them in a fast and effective way, leveraging on novel and existing simulation technologies. We present a triple-layered simulation framework where dynamic properties such as stiffness are determined from slow but accurate FEM simulation data once, and then condensed into a lumped parameter model that can be used to fast simulate soft fingers and soft hands. We apply our approach to the simulation of soft pneumatic fingers.

This paper presents novel event-driven forward kinematics algorithms for open kinematic chains with O(log n) complexity. This event-driven algorithm can efficiently update forward kinematics only when new sensory data comes. This will also contribute to localization of computational resources at sensitive joints to the position of the endpoint (e.g. a fingertip), like a root joint. We constructed 3 event-driven FK algorithms. We proved that the algorithms have the complexity of O(logn) for updating 1 joint angle, and O(logn) for obtaining a homogeneous transformation matrix between links. We compared the 3 algorithms with a conventional forward kinematics algorithm in the viewpoint of complexity, computation time, time-variance and algebraic structures. The results showed that the computation time is well adequate for real-time computation. Computation time is less than 2 us per 1 query, for 40,000 kinematic chains.

Modern 3D laser-range scanners have a high data rate, making online simultaneous localization and mapping (SLAM) computationally challenging. Recursive state estimation techniques are efficient but commit to a state estimate immediately after a new scan is made, which may lead to misalignments of measurements. We present a 3D SLAM approach that allows for refining alignments during online mapping. Our method is based on efficient local mapping and a hierarchical optimization back-end. Measurements of a 3D laser scanner are aggregated in local multiresolution maps by means of surfel-based registration. The local maps are used in a multi-level graph for allocentric mapping and localization. In order to incorporate corrections when refining the alignment, the individual 3D scans in the local map are modeled as a sub-graph and graph optimization is performed to account for drift and misalignments in the local maps. Furthermore, in each sub-graph, a continuous-time representation of the sensor trajectory allows to correct measurements between scan poses. We evaluate our approach in multiple experiments by showing qualitative results. Furthermore, we quantify the map quality by an entropy-based measure.

The active SLAM problem considered in this paper aims to plan a robot trajectory for simultaneous localization and mapping (SLAM) as well as for an area coverage task with robot pose uncertainty. Based on a model predictive control (MPC) framework, these two problems are solved respectively by different methods. For the uncertainty minimization MPC problem, based on the graphical structure of the 2D feature-based SLAM, a non-convex constrained least-squares problem is presented to approximate the original problem. Then, using variable substitutions, it is further transformed into a convex problem, and then solved by a convex optimization method. For the coverage task considering robot pose uncertainty, it is formulated and solved by the MPC framework and the sequential quadratic programming (SQP) method. In the whole process, considering the computation complexity, we use linear SLAM, which is a submap joining approach, to reduce the time for planning and estimation. Finally, various simulations are presented to validate the effectiveness of the proposed approach.

Latency and refresh rate are known to adversely affect human force perception in bilateral teleoperators and virtual environments using kinesthetic force feedback, motivating the use of sensory substitution of force. The purpose of this study is to quantify the effects of latency and refresh rate on force perception using sensory substitution by skin deformation feedback. A force-controlled skin deformation feedback device was attached to a 3-degree-of-freedom kinesthetic force feedback device used for position tracking and gravity support. A human participant study was conducted to determine the effects of latency and refresh rate on perceived stiffness and damping with skin deformation feedback. Participants compared two virtual objects: a comparison object with stiffness or damping that could be tuned by the participant, and a reference object with either added latency or reduced refresh rate. Participants modified the stiffness or damping of the tunable object until it resembled the stiffness or damping of the reference object. We found that added latency and reduced refresh rate both increased perceived stiffness but had no effect on perceived damping. Specifically, participants felt significantly different stiffness when the latency exceeded 300 ms and the refresh rate dropped below 16.6 Hz. The impact of latency and refresh rate on force perception via skin deformation feedback was significantly less than what has been previously shown for kinesthetic force feedback.

This paper presents a novel eddy current damper design for chatter suppression in robotic milling process. The designed eddy current dampers are installed on a milling spindle to damp the tool tip vibrations. The structural design of the eddy current dampers and the working principle of the proposed vibration attenuation method are explained. Finite element method is used to analyze the magnetic flux density and the magnetic force generated by the designed eddy current. The dynamics of the robotic milling system without and with eddy current dampers are modeled, and the damping performance of the proposed method is verified through simulations in both frequency and time domains. The results show that the peaks of the tool tip frequency response function caused by the spindle and milling tool modes are damped by 3.2 dB and 5.3 dB, respectively, and the chatter stability is improved by about 43% in the high spindle speed zone, compared to the case without eddy current dampers.

Turn-taking is essential to the structure of human teamwork. Humans are typically aware of team members' intention to keep or relinquish their turn before a turn switch, where the responsibility of working on a shared task is shifted. Future co-robots are also expected to provide such competence. To that end, this paper proposes the Cognitive Turn-taking Model (CTTM), which leverages cognitive models (i.e., Spiking Neural Network) to achieve early turn-taking prediction. The CTTM framework can process multimodal human communication cues (both implicit and explicit) and predict human turn-taking intentions in an early stage. The proposed framework is tested on a simulated surgical procedure, where a robotic scrub nurse predicts the surgeon's turn-taking intention. It was found that the proposed CTTM framework outperforms the state-of-the-art turn-taking prediction algorithms by a large margin. It also outperforms humans when presented with partial observations of communication cues (i.e., less than 40 % of full actions). This early prediction capability enables robots to initiate turn-taking actions at an early stage, which facilitates collaboration and increases overall efficiency.

Direct policy search has shown to be a successful method to optimize robot controller parameters. However, defining a good parametric form for the controller can be challenging for complex problems. Non-parametric methods provide a flexible alternative and are thus a promising tool in robot skill learning. In this paper, we investigate two nonparametric methods based on similar principles but utilizing differing computing schedules: an eager learner and a memory-based learner. We compare the methods experimentally on two different control problems. Furthermore, we define and evaluate a new `hybrid' controller that combines the strong points of both of these methods.

Gastrointestinal (GI) cancers account for 1.5 million deaths worldwide. Endoscopic Submucosal Dissection (ESD) is an advanced therapeutic endoscopy technique with superior clinical outcome due to the minimally invasive and en bloc removal of tumours. In the western world, ESD is seldom carried out, due to its complex and challenging nature. Various surgical systems are being developed to make this therapy accessible, however, these solutions have shown limited operational workspace, dexterity, or low force exertion capabilities. The current paper shows the ESD CYCLOPS system, a bimanual surgical robotic attachment that can be mounted at the end of any flexible endoscope. The system is able to achieve forces of up to 46N, and showed a mean error of 0.217mm during an elliptical tracing task. The workspace and instrument dexterity is shown by pre-clinical ex vivo trials, in which ESD is successfully performed by a GI surgeon. The system is currently undergoing pre-clinical in vivo validation.

Simulation of planetary rovers moving on complex terrains is critical for Mars exploration. Equivalent stiffness is proposed and used to characterize the pressure-sinkage property of terrain, while friction angle to characterize the shearing property. Terramechanics model for calculating forces between rigid wheel and soil is proved to be the same with that contact model for calculating forces between rigid wheel and rock. A Digital Elevation Map with Physical Properties is developed and applied to simulate terrain physical properties along with its geometry information. The established methods are validated using simulation and experimental tests with a three-wheel-rover.

This paper presents a real-time programming and parameter reconfiguration method for autonomous underwater robots in human-robot collaborative tasks. Using a set of intuitive and meaningful hand gestures, we develop a syntactically simple framework that is computationally more efficient than a complex, grammar-based approach. In the proposed framework, a convolutional neural network is trained to provide accurate hand gesture recognition; subsequently, a finite-state machine- based deterministic model performs efficient gesture-to-instruction mapping and further improves robustness of the interaction scheme. The key aspect of this framework is that it can be easily adopted by divers for communicating simple instructions to underwater robots without using artificial tags such as fiducial markers or requiring memorization of a potentially complex set of language rules. Extensive experiments are performed both on field-trial data and through simulation, which demonstrate the robustness, efficiency, and portability of this framework in a number of different scenarios. Finally, a user interaction study is presented that illustrates the gain in the ease of use of our proposed interaction framework compared to the existing methods for the underwater domain.

Long-term situation prediction plays a crucial role for intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challenge by combining a Bayesian filtering technique for environment representation, and machine learning as long-term predictor. More specifically, a dynamic occupancy grid map is utilized as input to a deep convolutional neural network. This yields the advantage of using spatially distributed velocity estimates from a single time step for prediction, rather than a raw data sequence, alleviating common problems dealing with input time series of multiple sensors. Furthermore, convolutional neural networks have the inherent characteristic of using context information, enabling the implicit modeling of road user interaction. Pixel-wise balancing is applied in the loss function counteracting the extreme imbalance between static and dynamic cells. One of the major advantages is the unsupervised learning character due to fully automatic label generation. The presented algorithm is trained and evaluated on multiple hours of recorded sensor data and compared to Monte-Carlo simulation. Experiments show the ability to model complex interactions.

This paper deals with the dynamic modeling and identification of an electrically driven underwater robot manipulator. The proposed study includes the dynamic modeling of the actuators of the arm as well as the identification of the parameters of the model. The proposed method deals with the specific case of heterogeneously actuated arms, namely arms with actuators behaving differently for each joint, being considered at the kinematic level. Indeed, we show how to estimate the arms parameters when some of their revolute joints are directly actuated by geared motors, while the others are actuated by linear actuators. A minimum set of identifiable parameters is determined, and adequate excitation trajectories are generated and used in the identification procedure. Realtime experimental validation on the manipulator arms of Ifremer's HROV (Hybrid Remotely Operated Vehicle) Ariane underwater vehicle demonstrates that the proposed method improves the estimation of the dynamic model.

In the present paper we discuss a novel dynamic controller for Cable Driven Parallel Robots, based on the Backstepping technique. The main challenge in controlling these robots, is expressing the dynamic equilibrium with respect to the joint variables. This drawback makes the definition of closed-loop controllers more challenging, in comparison with their serial counterparts. The problem is tackled by considering redundant dynamics, expressed in both task and joints space and solved through the method of quasi-velocity. We propose the usage of the observer linearization to estimate the end effector pose and stiffness, by just measuring the motor position, velocity and torque. These variables are used in the feedback loop to control the pose of the end effector. A 3-tendon planar platform is used for the experimental analysis.

The design of robots that are light, soft, powerful is a grand challenge. Since they can easily adapt to dynamic environments, soft robotic systems have the potential of changing the status-quo of bulky robotics. A crucial component of soft robotics is a soft actuator that is activated by external stimuli to generate desired motions. Unfortunately, there is a lack of powerful soft actuators that operate through lightweight power sources. To that end, we recently designed a highly scalable, flexible, biocompatible Electromagnetic Soft Actuator (ESA). With ESAs, artificial muscles can be designed by integrating a network of ESAs. The main research gap addressed in this work is in the absence of system-theoretic understanding of the impact of the realtime control and actuator selection algorithms on the performance of networked soft-body actuators and ESAs. The objective of this paper is to establish a framework that guides the analysis and robust control of networked ESAs. A novel ESA is described, and a configuration of soft actuator matrix to resemble artificial muscle fiber is presented. A mathematical model which depicts the physical network is derived, considering the disturbances due to external forces and linearization errors as an integral part of this model. Then, a robust control and minimal actuator selection problem with logistic constraints and control input bounds is formulated, and tractable computational routines are proposed with numerical case studies.

We describe a method for automatically building structures from stacked, irregularly shaped objects. This is a simplified model for the problem of building dry stacked structures (i.e. no mortar) from found stones. Although automating such construction methods would be ideally suited for disaster areas or remote environments, currently such structures need to be built by skilled masons. No practical methods for automating the assembly planning process are known. The problem is challenging since each assembly action can be drawn from a continuous space poses for an object and several local geometric and physical considerations strongly affect the overall stability. We show that structures that are built following a stacking order for perfect bricks can accommodate a limited amount of irregularity, however, their performance degrades quickly when objects deviate from their ideal shape. We present a strategy for stacking irregular shapes that first considers geometric and physical constraints to find a small set of feasible actions and then further refines this set by using heuristics gathered from instructional literature for masons. The proposed method of choosing assembly actions allows construction with objects that contain a significant amount of variation.

Dropout Variational Inference, or Dropout Sampling, has been recently proposed as an approximation technique for Bayesian Deep Learning and evaluated for image classification and regression tasks. This paper investigates the utility of Dropout Sampling for object detection for the first time. We demonstrate how label uncertainty can be extracted from a state-of-the-art object detection system via Dropout Sampling. We evaluate this approach on a large synthetic dataset of 30,000 images, and a real-world dataset captured by a mobile robot in a versatile campus environment. We show that this uncertainty can be utilized to increase object detection performance under the open-set conditions that are typically encountered in robotic vision. A Dropout Sampling network is shown to achieve a 12.3 % increase in recall (for the same precision score as a standard network) and a 15.1 % increase in precision (for the same recall score as the standard network).