ICRA 2017 Papers

Optical Tweezers (OT) have the advantage of non-contact interaction with target objects such as cells, overcoming the pitfall of obstructive adhesion forces which are present in contact micromanipulation. It is also feasible to manipulate a number of small microparts simultaneously or 3D structures by using multiple laser traps. These capabilities give rise to the potential to develop a human-robot interface to facilitate microassembly tasks. This paper presents a gaze contingent control framework and a method for 3D orientation estimation for optical micromanipulation. The proposed strategy aims to use OT as an interactive microassembly platform. The framework comprises I) a strategy to recognize the operator's intentions in order to interactively place and reconfigure the optical traps using the operator's eye fixation point, II) haptic constraints generated from the user's eye gaze to assist positioning of the assembled microparts and III) a method for 3D orientation estimation. The performance of the proposed framework is assessed through a set of experiments comparing it to the standard OT user interface. Three-dimensional manipulation and orientation estimation of a non-spherical microstructure are also performed.

In this paper, we propose a real-time classification scheme to cope with noisy Radio Signal Strength Indicator (RSSI) measurements utilized in indoor positioning systems. RSSI values are often converted to distances for position estimation. However due to multipathing and shadowing effects, finding a unique sensor model using both parametric and non-parametric methods is highly challenging. We learn decision regions using the Gaussian Processes classification to accept measurements that are consistent with the operating sensor model. The proposed approach can perform online, does not rely on a particular sensor model or parameters, and is robust to sensor failures. The experimental results achieved using hardware show that available positioning algorithms can benefit from incorporating the classifier into their measurement model as a meta-sensor modeling technique.

Since early in robotics the performance of odometry techniques has been of constant research for mobile robots. This is due to its direct influence on localization. The pose error grows unbounded in dead-reckoning systems and its uncertainty has negative impacts in localization and mapping (i.e. SLAM). The dead-reckoning performance in terms of residuals, i.e. the difference between the expected and the real pose state, is related to the statistical error or uncertainty in probabilistic motion models. A novel approach to model odometry errors using Gaussian processes (GPs) is presented. The methodology trains a GP on the residual between the non-linear parametric motion model and the ground truth training data. The result is a GP over odometry residuals which provides an expected value and its uncertainty in order to enhance the belief with respect to the parametric model. The localization and mapping benefits from a comprehensive GP-odometry residuals model. The approach is applied to a planetary rover in an unstructured environment. We show that our approach enhances visual SLAM by efficiently computing image frames and effectively distributing keyframes.

We use a conductive fabric substrate as a building material for a soft sensor to extend the functionality of soft actuators. We use PCB etching techniques to apply a pattern to the fabric, yielding distinct conductive surfaces within the same textile. We connect these via flexible wire bus embedded in silicone, terminating in a flexible PCB. We show how touch and metal objects can be localized along the length of the composite fabric strip. We demonstrate an example soft robotic application, by replacing the constraint layer component in a PneuFlex-style soft actuator with the self contained sensing strip. We show that the augmented composite actuator is able to interact with conductive objects in the environment using a capacitive touch sensing with applications in grasping and human-robot interaction.

We present parametric trajectory optimization, a method for simultaneously computing physical parameters, actuation requirements, and robot motions for more efficient robot designs. In this scheme, robot dimensions, masses, and other physical parameters are solved for concurrently with traditional motion planning variables, including dynamically consistent robot states, actuation inputs, and contact forces. Our method requires minimal user domain knowledge, requiring only a coarse guess of the target robot configuration sequence and a parameterized robot topology as input. We demonstrate our results on four simulated robots, one of which we physically fabricated in order to demonstrate physical consistency. We demonstrate that by optimizing robot body parameters alongside robot trajectories, motion planning problems which would otherwise be infeasible can be made feasible, and actuation requirements can be significantly reduced.

Learning from demonstration for motion planning is an ongoing research topic. In this paper we present a model that is able to learn the complex mapping from raw 2D-laser range findings and a target position to the required steering commands for the robot. To our best knowledge, this work presents the first approach that learns a target-oriented end-to-end navigation model for a robotic platform. The supervised model training is based on expert demonstrations generated in simulation with an existing motion planner. We demonstrate that the learned navigation model is directly transferable to previously unseen virtual and, more interestingly, real-world environments. It can safely navigate the robot through obstacle-cluttered environments to reach the provided targets. We present an extensive qualitative and quantitative evaluation of the neural network-based motion planner, and compare it to a grid-based global approach, both in simulation and in real-world experiments.

Bats are unique in that they can achieve unrivaled agile maneuvers due to their functionally versatile wing conformations. Among these maneuvers, roosting (landing) has captured attentions because bats perform this acrobatic maneuver with a great composure. This work attempts to reconstruct bat landing maneuvers with a Micro Aerial Vehicle (MAV) called Allice. Allice is capable of adjusting the position of its Center of Gravity (CG) with respect to the Center of Pressure (CP) using a nonlinear closed-loop feedback. This nonlinear control law, which is based on the method of input-output feedback linearization, enables attitude regulations through variations in CG-CP distance. To design the model-based nonlinear controller, the Newton-Euler dynamic model of the robot is considered, in which the aerodynamic coefficients of lift and drag are obtained experimentally. The performance of the proposed control architecture is validated by conducting several experiments.

An emerging application of multirobot systems is the monitoring of a dynamic event. Here, the goal is to enclose and track a moving target by attaining a desired geometric formation around it. By considering a circular pattern configuration for the target enclosing, the multirobot system is able to perform full perception of the target along its motion. In the proposed system, the robots rely only on their onboard vision sensor without external input to complete the task. The key problem resides in overcoming the motion and visual constraints of the agents. In particular, differential-drive robots with limited sensing, that must maintain visibility of the moving target as it navigates in the environment, are considered. A novel approach to characterize the motion of the robots in the formation that allows to enclose and track the target while overcoming their limited field of view (FOV) is presented. The proposed approach is illustrated through simulations.

Free-flying robots have the potential to autonomously fulfill a wide range of tasks involving manipulation of objects in space. In this paper we study the design of a wrist mechanism for free-flying robots that are equipped with an adhesive gripper for attaching to objects and surfaces. The wrist and gripper allow the robots to apply moments in addition to forces, which increases their versatility for object manipulation. We apply grasp optimization to establish limitations on the forces/moments that the wrist can impart, subject to adhesion capabilities. Building on these results, we present considerations for tuning a passive wrist mechanism, or controlling an active wrist, to broaden the range of forces and moments that the robot can exert. Our theoretical insights and wrist designs are validated in simulations and on a planar micro-gravity test bed.

Traversing environments with arbitrary obstacles poses significant challenges for bipedal robots. In some cases, whole body motions may be necessary to maneuver around an obstacle, but most existing footstep planners can only select from a discrete set of predetermined footstep actions; they are unable to utilize the continuum of whole body motion that is truly available to the robot platform. Existing motion planners that can utilize whole body motion tend to struggle with the complexity of large-scale problems. We introduce a planning method, called the “Randomized Possibility Graph”, which uses high-level approximations of constraint manifolds to rapidly explore the “possibility” of actions, thereby allowing lower-level motion planners to be utilized more efficiently. We demonstrate simulations of the method working in a variety of semi-unstructured environments. In this context, “semi-unstructured” means the walkable terrain is flat and even, but there are arbitrary 3D obstacles throughout the environment which may need to be stepped over or maneuvered around using whole body motions.

We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models. It is efficient because (1) local models are estimated only when the planner requires them; (2) the planner focuses on the most relevant states to the current planning problem; and (3) the planner focuses on the most informative and/or high-value actions. Our theoretical analysis shows the validity and asymptotic optimality of the proposed approach. Empirically, we demonstrate the effectiveness of our algorithm on a simulated multi-modal pushing problem.

This paper discusses the design and control methods of a thrust-vectored tailsitter that combines the advantages of both fixed wing and rotary wing systems. Separable takeoff bracket and controllable forward landing are implemented to reduce the flight weight and mitigate the effects of crosswinds. A six-degrees-of-freedom model especially for this tailsitter is then proposed to describe the dynamics of the whole system. Attitude representation based on horizontal /vertical Euler angles is presented to avoid the problem of singularity. Attitude and altitude controllers that switch between horizontal and vertical modes are used. In these controllers linear/constant acceleration approximation and filtered feed-forward acceleration algorithm are implemented. Effectiveness and reliability of the proposed control methods are demonstrated and evaluated by experimental results of the whole flight envelope.

We present the design and implementation of a flexible force-vision-based interface; allowing local operators to visually specify a path constraint to a remote robot manipulator in an on-line fashion during the teleoperation. Using bilateral and unilateral configurations, we compare our system to direct teleoperation through user studies. Three performance metrics (smoothness, error and execution time) and a subjective evaluation (NASA TLX) were used to quantify user performance. The trials show that our system outperforms direct teleoperation and reduces cognitive load. Our findings show that the performance of a unilateral teleop configuration with visual-force constraints surpass a bilateral teleop configuration in terms of displacement error and variance, as well as allowing users to complete tasks faster and with a smoother trajectory.

This paper presents a method for closed-loop propulsion of a screw-type magnetic capsule with embedded Hall-effect sensors using a single rotating actuator magnet. The method estimates the six-degree-of-freedom (6-DOF) pose of the capsule while it is synchronously rotating with the applied field. It is intended for application in active capsule endoscopy of the intestines. An extended Kalman filter, which uses a simplified 2-DOF process model restricting the capsule to forward or backward movement and rotation about its principle axis, is used to provide a full 6-DOF estimate of the capsule's pose as the capsule travels through a lumen. The capsule's movement in the applied field is constantly monitored to determine if the capsule is synchronously rotating with the applied field. Based on this information, the rotation speed of the external source is adjusted to prevent a loss in the desired magnetic coupling. We experimentally demonstrate, for the first time, simultaneous localization and closed-loop propulsion of a capsule through a lumen using a single rotating magnet. Prior work assumed the capsule had no net motion during the localization phase, requiring decoupled localization and propulsion. This closed-loop performance results in a three times speed up in completion time, compared to the previous decoupled approach.

In this paper we present a robust, efficient and affordable approach to self-localization which requires neither GPS nor knowledge about the appearance of the world. Towards this goal, we utilize freely available cartographic maps and derive a probabilistic model that exploits semantic cues in the form of sun direction, presence of an intersection, road type, speed limit and ego-car trajectory to produce very reliable localization results. Our experimental evaluation shows that our approach can localize much faster (in terms of driving time) with less computation and more robustly than competing approaches, which ignore semantic information.

We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths and obstacles without requiring manual annotation, which we then use to train a deep semantic segmentation network. With the trained network we can segment proposed paths and obstacles at run-time using a vehicle equipped with only a monocular camera without relying on explicit modelling of road or lane markings. We evaluate our method on the large-scale KITTI and Oxford RobotCar datasets and demonstrate reliable path proposal and obstacle segmentation in a wide variety of environments under a range of lighting, weather and traffic conditions. We illustrate how the method can generalise to multiple path proposals at intersections and outline plans to incorporate the system into a framework for autonomous urban driving.

This paper presents a new feedforward controller based on a continuous-time finite impulse response filter, designed to minimize the vibrations that usually affect robot manipulators with elastic joints. In particular, Variable Stiffness Joints (VSJ) robots are considered, since they are usually characterized by a very low level of damping which makes the problem of the oscillations quite important. The proposed approach allows to simplify the overall control structure of VSJ robots, which is based on a decentralized control of each servomotor, imposing the desired position and the desired stiffness at each joint, and on a novel feedforward control, filtering the reference signals. After analyzing some of the filter properties and the method for the parameters choice, experimental results on a VSJ robot demonstrate the importance of the proposed filtering action for minimizing vibrations and oscillations.

For a manipulation skill to be applicable to a wide range of scenarios, it must generalize between different objects and object configurations. Robots should therefore learn skills that adapt to features describing the objects being manipulated. Most of these object features will however be irrelevant for generalizing the skill and, hence, the robot should select a small set of relevant features for adapting the skill. We use a framework for learning versatile manipulation skills that adapt to a sparse set of object features. Skills are initially learned from demonstrations and subsequently improved using reinforcement learning. The robot also learns a meta prior over the features' relevances to guide the feature selection process. In this paper, we explore using either desired trajectories or observed trajectories for selecting the relevant features. The framework was evaluated on placing, tilting, and wiping tasks. The evaluations showed that using the desired trajectories to select the relevant features lead to better skill learning performance.

This paper proposes new software and hardware platforms named ROS-TMS and Big Sensor Box, respectively, for an informationally structured environment. We started the development of a management system for an informationally structured environment named Town Management System (TMS) in the Robot Town Project in 2005. Since then we have been continuing our efforts to improve performance and to enhance TMS functions. Recently, we launched a new version of TMS named ROS-TMS, which resolves some critical problems in TMS by adopting the Robot Operating System (ROS) and utilizing the high scalability and numerous resources of ROS. In this paper, we first discuss the structure of a software platform for the informationally structured environment and describe in detail our latest system, ROS-TMS version 4.0. Next, we introduce a hardware platform for the informationally structured environment named Big Sensor Box, in which a variety of sensors are embedded and service robots are operated according to the structured information under the management of ROS-TMS. Robot service experiments including a fetch-and-give task and autonomous control of a wheelchair robot are also conducted in Big Sensor Box.