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Automated vehicles (AVs) must be evaluated thoroughly before their release and deployment. A widely-used evaluation approach is the Naturalistic-Field Operational Test (N-FOT), which tests prototype vehicles directly on the public roads. Due to the low exposure to safety-critical scenarios, N-FOTs are time-consuming and expensive to conduct. In this paper, we propose an accelerated evaluation approach for AVs. The results can be used to generate motions of the primary other vehicles to accelerate the verification of AVs in simulations and controlled experiments. Frontal collision due to unsafe cut-ins is the target crash type of this paper. Human-controlled vehicles making unsafe lane changes are modeled as the primary disturbance to AVs based on data collected by the University of Michigan Safety Pilot Model Deployment Program. The cut-in scenarios are generated based on skewed statistics of collected human driver behaviors, which generate risky testing scenarios while preserving the statistical information so that the safety benefits of AVs in non-accelerated cases can be accurately estimated. The Cross Entropy method is used to recursively search for the optimal skewing parameters. The frequencies of occurrence of conflicts, crashes and injuries are estimated for a modeled automated vehicle, and the achieved accelerated rate is around 2,000 to 20,000. In other words, in the accelerated simulations, driving for 1,000 miles will expose the AV with challenging scenarios that will take about 2 to 20 million miles of real-world driving to encounter. This technique thus has the potential to reduce greatly the development and validation time for AVs. | Accelerated Evaluation of Automated Vehicles Safety in Lane Change
Scenarios Based on Importance Sampling Techniques | 8,700 |
Humans and animals developed a sophisticated motor control apparatus and there is much evidence that it has a modular structure. The modularity offers a range of benefits, e.g. ability to learn dissociable motion styles without interference and forgetting, fast adaptation and de-adaptation to changes conditions. However in robotics, building a controller that can efficiently incrementally learn new motion styles and provide switching between them is a formidable challenge. In this paper we address the problem by proposing a novel biologically inspired compositional neuro-controller. We have shown that the compositional controller is able to reproduce a set of trajectories more efficiently comparing to a simple controller, exploiting incremental learning benefits. Second, we have demonstrated that the proposed controller is able to learn different locomotion styles and switch between them in a simulated robot-snake. | A Compositional Neuro-Controller for Advanced Motor Control Tasks | 8,701 |
Path planning in the presence of dynamic obstacles is a challenging problem due to the added time dimension in search space. In approaches that ignore the time dimension and treat dynamic obstacles as static, frequent re-planning is unavoidable as the obstacles move, and their solutions are generally sub-optimal and can be incomplete. To achieve both optimality and completeness, it is necessary to consider the time dimension during planning. The notion of adaptive dimensionality has been successfully used in high-dimensional motion planning such as manipulation of robot arms, but has not been used in the context of path planning in dynamic environments. In this paper, we apply the idea of adaptive dimensionality to speed up path planning in dynamic environments for a robot with no assumptions on its dynamic model. Specifically, our approach considers the time dimension only in those regions of the environment where a potential collision may occur, and plans in a low-dimensional state-space elsewhere. We show that our approach is complete and is guaranteed to find a solution, if one exists, within a cost sub-optimality bound. We experimentally validate our method on the problem of 3D vehicle navigation (x, y, heading) in dynamic environments. Our results show that the presented approach achieves substantial speedups in planning time over 4D heuristic-based A*, especially when the resulting plan deviates significantly from the one suggested by the heuristic. | Path Planning in Dynamic Environments with Adaptive Dimensionality | 8,702 |
We present a multimodal interaction framework suitable for a human rescuer that operates in proximity with a set of co-located drones during search missions. This work is framed in the context of the SHERPA project whose goal is to develop a mixed ground and aerial robotic platform to support search and rescue activities in a real-world alpine scenario. Differently from typical human-drone interaction settings, here the operator is not fully dedicated to the drones, but involved in search and rescue tasks, hence only able to provide sparse, incomplete, although high-value, instructions to the robots. This operative scenario requires a human-interaction framework that supports multimodal communication along with an effective and natural mixed-initiative interaction between the human and the robots. In this work, we illustrate the domain and the proposed multimodal interaction framework discussing the system at work in a simulated case study. | Multimodal Interaction with Multiple Co-located Drones in Search and
Rescue Missions | 8,703 |
When carrying out tasks in contact with the environment, humans are found to concurrently adapt force, impedance and trajectory. Here we develop a robotic model of this mechanism in humans and analyse the underlying dynamics. We derive a general adaptive controller for the interaction of a robot with an environment solely characterised by its stiffness and damping, using Lyapunov theory. | Dynamic analysis of simultaneous adaptation of force, impedance and
trajectory | 8,704 |
General-purpose mobile manipulators have the potential to serve as a versatile form of assistive technology. However, their complexity creates challenges, including the risk of being too difficult to use. We present a proof-of-concept robotic system for assistive feeding that consists of a Willow Garage PR2, a high-level web-based interface, and specialized autonomous behaviors for scooping and feeding yogurt. As a step towards use by people with disabilities, we evaluated our system with 5 able-bodied participants. All 5 successfully ate yogurt using the system and reported high rates of success for the system's autonomous behaviors. Also, Henry Evans, a person with severe quadriplegia, operated the system remotely to feed an able-bodied person. In general, people who operated the system reported that it was easy to use, including Henry. The feeding system also incorporates corrective actions designed to be triggered either autonomously or by the user. In an offline evaluation using data collected with the feeding system, a new version of our multimodal anomaly detection system outperformed prior versions. | Towards Assistive Feeding with a General-Purpose Mobile Manipulator | 8,705 |
This paper is essentially an exercise in studying the minima of a certain least squares optimization using the second partial derivative test. The motivation is to gain insight into an optimization-based solution to the problem of tracking human limbs using IMU sensors. | Uniqueness of Minima of a Certain Least Squares Problem | 8,706 |
Optimal control approaches in combination with trajectory optimization have recently proven to be a promising control strategy for legged robots. Computationally efficient and robust algorithms were derived using simplified models of the contact interaction between robot and environment such as the linear inverted pendulum model (LIPM). However, as humanoid robots enter more complex environments, less restrictive models become increasingly important. As we leave the regime of linear models, we need to build dedicated solvers that can compute interaction forces together with consistent kinematic plans for the whole-body. In this paper, we address the problem of planning robot motion and interaction forces for legged robots given predefined contact surfaces. The motion generation process is decomposed into two alternating parts computing force and motion plans in coherence. We focus on the properties of the momentum computation leading to sparse optimal control formulations to be exploited by a dedicated solver. In our experiments, we demonstrate that our motion generation algorithm computes consistent contact forces and joint trajectories for our humanoid robot. We also demonstrate the favorable time complexity due to our formulation and composition of the momentum equations. | Structured contact force optimization for kino-dynamic motion generation | 8,707 |
There are many application fields for robotic systems including service robotics, search and rescue missions, industry and space robotics. As the scenarios in these areas grow more and more complex, there is a high demand for powerful tools to efficiently program heterogeneous robotic systems. Therefore, we created RAFCON, a graphical tool to develop robotic tasks and to be used for mission control by remotely monitoring the execution of the tasks. To define the tasks, we use state machines which support hierarchies and concurrency. Together with a library concept, even complex scenarios can be handled gracefully. RAFCON supports sophisticated debugging functionality and tightly integrates error handling and recovery mechanisms. A GUI with a powerful state machine editor makes intuitive, visual programming and fast prototyping possible. We demonstrated the capabilities of our tool in the SpaceBotCamp national robotic competition, in which our mobile robot solved all exploration and assembly challenges fully autonomously. It is therefore also a promising tool for various RoboCup leagues. | RAFCON: a Graphical Tool for Task Programming and Mission Control | 8,708 |
Hessian information speeds convergence substantially in motion optimization. The better the Hessian approximation the better the convergence. But how good is a given approximation theoretically? How much are we losing? This paper addresses that question and proves that for a particularly popular and empirically strong approximation known as the Gauss-Newton approximation, we actually lose very little--for a large class of highly expressive objective terms, the true Hessian actually limits to the Gauss-Newton Hessian quickly as the trajectory's time discretization becomes small. This result both motivates it's use and offers insight into computationally efficient design. For instance, traditional representations of kinetic energy exploit the generalized inertia matrix whose derivatives are usually difficult to compute. We introduce here a novel reformulation of rigid body kinetic energy designed explicitly for fast and accurate curvature calculation. Our theorem proves that the Gauss-Newton Hessian under this formulation efficiently captures the kinetic energy curvature, but requires only as much computation as a single evaluation of the traditional representation. Additionally, we introduce a technique that exploits these ideas implicitly using Cholesky decompositions for some cases when similar objective terms reformulations exist but may be difficult to find. Our experiments validate these findings and demonstrate their use on a real-world motion optimization system for high-dof motion generation. | On the Fundamental Importance of Gauss-Newton in Motion Optimization | 8,709 |
RoboCup soccer competitions are considered among the most challenging multi-robot adversarial environments, due to their high dynamism and the partial observability of the environment. In this paper we introduce a method based on a combination of Monte Carlo search and data aggregation (MCSDA) to adapt discrete-action soccer policies for a defender robot to the strategy of the opponent team. By exploiting a simple representation of the domain, a supervised learning algorithm is trained over an initial collection of data consisting of several simulations of human expert policies. Monte Carlo policy rollouts are then generated and aggregated to previous data to improve the learned policy over multiple epochs and games. The proposed approach has been extensively tested both on a soccer-dedicated simulator and on real robots. Using this method, our learning robot soccer team achieves an improvement in ball interceptions, as well as a reduction in the number of opponents' goals. Together with a better performance, an overall more efficient positioning of the whole team within the field is achieved. | Using Monte Carlo Search With Data Aggregation to Improve Robot Soccer
Policies | 8,710 |
In this paper, we present the probably first application of the popular \emph{Dynamic Movement Primitives (DMP)} approach to the domain of soccer-playing humanoid robots. DMPs are known for their ability to imitate previously demonstrated motions as well as to flexibly adapt to unforeseen changes to the desired trajectory with respect to speed and direction. As demonstrated in this paper, this makes them a useful approach for describing kick motions. Furthermore, we present a mathematical motor model that compensates for the NAO robot's motor control delay as well as a novel minor extension to the DMP formulation. The motor model is used in the calculation of the Zero Moment Point (ZMP), which is needed to keep the robot in balance while kicking. All approaches have been evaluated on real NAO robots. | Kick Motions for the NAO Robot using Dynamic Movement Primitives | 8,711 |
While a number of excellent review articles on military robots have appeared in existing literature, this paper focuses on a distinct sub-space of related problems: small military robots organized into moderately sized squads, operating in a ground combat environment. Specifically, we consider the following: - Command of practical small robots, comparable to current generation, small unmanned ground vehicles (e.g., PackBots) with limited computing and sensor payload, as opposed to larger vehicle-sized robots or micro-scale robots; - Utilization of moderately sized practical forces of 3-10 robots applicable to currently envisioned military ground operations; - Complex three-dimensional physical environments, such as urban areas or mountainous terrains and the inherent difficulties they impose, including limited and variable fields of observation, difficult navigation, and intermittent communication; - Adversarial environments where the active, intelligent enemy is the key consideration in determining the behavior of the robotic force; and - Purposeful, partly autonomous, coordinated behaviors that are necessary for such a robotic force to survive and complete missions; these are far more complex than, for example, formation control or field coverage behavior. | A Survey of Research on Control of Teams of Small Robots in Military
Operations | 8,712 |
To enable safe and efficient human-robot collaboration in shared workspaces it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative re-planning with the above cost function. The key to our approach is thus to learn a cost function which "explains" the motion of the human. To do this, we gather example trajectories from pairs of participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict reaching motions from the human's current configuration to a task-space goal region by iteratively re-planning a trajectory using the learned cost function. Our planning algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF human kinematic model and accounts for the presence of a moving collaborator and obstacles in the environment. Our results suggest that in most cases, our method outperforms baseline methods when predicting motions. We also show that our method outperforms baselines for predicting human motion when a human and a robot share the workspace. | Goal Set Inverse Optimal Control and Iterative Re-planning for
Predicting Human Reaching Motions in Shared Workspaces | 8,713 |
In this article, a theoretical justification of one type of skew-symmetric optimal translational motion (moving in the minimal acceptable time) of a flexible object carried by a robot from its initial to its final position of absolute quiescence with the exception of the oscillations at the end of the motion is presented. The Hamilton-Ostrogradsky principle is used as a criterion for searching an optimal control. The data of experimental verification of the control are presented using the Orthoglide robot for translational motions and several masses were attached to a flexible beam. | Optimal Motion of Flexible Objects with Oscillations Elimination at the
Final Point | 8,714 |
Service robots for the domestic environment are intended to autonomously provide support for their users. However, state-of-the-art robots still often get stuck in failure situations leading to breakdowns in the interaction flow from which the robot cannot recover alone. We performed a multi-user Wizard-of-Oz experiment in which we manipulated the robot's behavior in such a way that it appeared unexpected and malfunctioning, and asked participants to help the robot in order to restore the interaction flow. We examined how participants reacted to the robot's error, its subsequent request for help and how it changed their perception of the robot with respect to perceived intelligence, likability, and task contribution. As interaction scenario we used a game of building Lego models performed by user dyads. In total 38 participants interacted with the robot and helped in malfunctioning situations. We report two major findings: (1) in user dyads, the user who gave the last command followed by the user who is closer is more likely to help (2) malfunctions that can be actively fixed by the user seem not to negatively impact perceived intelligence and likability ratings. This work offers insights in how far user support can be a strategy for domestic service robots to recover from repeating malfunctions. | Help, Anyone? A User Study For Modeling Robotic Behavior To Mitigate
Malfunctions With The Help Of The User | 8,715 |
Semantic mapping is the incremental process of "mapping" relevant information of the world (i.e., spatial information, temporal events, agents and actions) to a formal description supported by a reasoning engine. Current research focuses on learning the semantic of environments based on their spatial location, geometry and appearance. Many methods to tackle this problem have been proposed, but the lack of a uniform representation, as well as standard benchmarking suites, prevents their direct comparison. In this paper, we propose a standardization in the representation of semantic maps, by defining an easily extensible formalism to be used on top of metric maps of the environments. Based on this, we describe the procedure to build a dataset (based on real sensor data) for benchmarking semantic mapping techniques, also hypothesizing some possible evaluation metrics. Nevertheless, by providing a tool for the construction of a semantic map ground truth, we aim at the contribution of the scientific community in acquiring data for populating the dataset. | A Proposal for Semantic Map Representation and Evaluation | 8,716 |
The proper handling of 3D orientations is a central element in many optimization problems in engineering. Unfortunately many researchers and engineers struggle with the formulation of such problems and often fall back to suboptimal solutions. The existence of many different conventions further complicates this issue, especially when interfacing multiple differing implementations. This document discusses an alternative approach which makes use of a more abstract notion of 3D orientations. The relative orientation between two coordinate systems is primarily identified by the coordinate mapping it induces. This is combined with the standard exponential map in order to introduce representation-independent and minimal differentials, which are very convenient in optimization based methods. | A Primer on the Differential Calculus of 3D Orientations | 8,717 |
Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved? | Past, Present, and Future of Simultaneous Localization And Mapping:
Towards the Robust-Perception Age | 8,718 |
A promising approach to autonomous driving is machine learning. In such systems, training datasets are created that capture the sensory input to a vehicle as well as the desired response. A disadvantage of using a learned navigation system is that the learning process itself may require a huge number of training examples and a large amount of computing. To avoid the need to collect a large training set of driving examples, we describe a system that takes advantage of the huge number of training examples provided by ImageNet, but is able to adapt quickly using a small training set for the specific driving environment. | Fast Incremental Learning for Off-Road Robot Navigation | 8,719 |
We propose an informative path planning (IPP) algorithm for active classification using an unmanned aerial vehicle (UAV), focusing on weed detection in precision agriculture. We model the presence of weeds on farmland using an occupancy grid and generate plans according to information-theoretic objectives, enabling the UAV to gather data efficiently. We use a combination of global viewpoint selection and evolutionary optimization to refine the UAV's trajectory in continuous space while satisfying dynamic constraints. We validate our approach in simulation by comparing against standard "lawnmower" coverage, and study the effects of varying objectives and optimization strategies. We plan to evaluate our algorithm on a real platform in the immediate future. | Online Informative Path Planning for Active Classification on UAVs | 8,720 |
Behavior trees (BTs) emerged from video game development as a graphical language for modeling intelligent agent behavior. However as initially implemented, behavior trees are static plans. This paper adds to recent literature exploring the ability of BTs to adapt to their success or failure in achieving tasks. The "Selector" node of a BT tries alternative strategies (its children) and returns success only if all of its children return failure. This paper studies several means by which Selector nodes can learn from experience, in particular, learn conditional probabilities of success based on sensor information, and modify the execution order based on the learned iformation. Furthermore, a "Greedy Selector" is studied which only tries the child having the highest success probability. Simulation results indicate significantly increased task performance, especially when frequentist probability estimate is conditioned on sensor information. The Greedy selector was ineffective unless it was preceded by a period of training in which all children were exercised. | Simulation Results on Selector Adaptation in Behavior Trees | 8,721 |
Affordances have been introduced in literature as action opportunities that objects offer, and used in robotics to semantically represent their interconnection. However, when considering an environment instead of an object, the problem becomes more complex due to the dynamism of its state. To tackle this issue, we introduce the concept of Spatio-Temporal Affordances (STA) and Spatio-Temporal Affordance Map (STAM). Using this formalism, we encode action semantics related to the environment to improve task execution capabilities of an autonomous robot. We experimentally validate our approach to support the execution of robot tasks by showing that affordances encode accurate semantics of the environment. | STAM: A Framework for Spatio-Temporal Affordance Maps | 8,722 |
A dataset is crucial for model learning and evaluation. Choosing the right dataset to use or making a new dataset requires the knowledge of those that are available. In this work, we provide that knowledge, by reviewing twenty datasets that were published in the recent six years and that are directly related to object manipulation. We report on modalities, activities, and annotations for each individual dataset and give our view on its use for object manipulation. We also compare the datasets and summarize them. We conclude with our suggestion on future datasets. | Datasets on object manipulation and interaction: a survey | 8,723 |
We consider a problem called task ordering with path uncertainty (TOP-U) where multiple robots are provided with a set of task locations to visit in a bounded environment, but the length of the path between a pair of task locations is initially known only coarsely by the robots. The objective of the robots is to find the order of tasks that reduces the path length (or, energy expended) to visit the task locations in such a scenario. To solve this problem, we propose an abstraction called a task reachability graph (TRG) that integrates the task ordering with the path planning by the robots. The TRG is updated dynamically based on inter-task path costs calculated using a sampling-based motion planner, and, a Hidden Markov Model (HMM)-based technique that calculates the belief in the current path costs based on the environment perceived by the robot's sensors and task completion information received from other robots. We then describe a Markov Decision Process (MDP)-based algorithm that can select the paths that reduce the overall path length to visit the task locations and a coordination algorithm that resolves path conflicts between robots. We have shown analytically that our task selection algorithm finds the lowest cost path returned by the motion planner, and, that our proposed coordination algorithm is deadlock free. We have also evaluated our algorithm on simulated Corobot robots within different environments while varying the number of task locations, obstacle geometries and number of robots, as well as on physical Corobot robots. Our results show that the TRG-based approach can perform considerably better in planning and locomotion times, and number of re-plans, while traveling almost-similar distances as compared to a closest first, no uncertainty (CFNU) task selection algorithm. | Integrated Task and Motion Planning for Multiple Robots under Path and
Communication Uncertainties | 8,724 |
The main contribution of this paper is a high frequency, low-complexity, on-board visual-inertial odometry system for quadrotor micro air vehicles. The system consists of an extended Kalman filter (EKF) based state estimation algorithm that fuses information from a low cost MEMS inertial measurement unit acquired at 200Hz and VGA resolution images from a monocular camera at 50Hz. The dynamic model describing the quadrotor motion is employed in the estimation algorithm as a third source of information. Visual information is incorporated into the EKF by enforcing the epipolar constraint on features tracked between image pairs, avoiding the need to explicitly estimate the location of the tracked environmental features. Combined use of the dynamic model and epipolar constraints makes it possible to obtain drift free velocity and attitude estimates in the presence of both accelerometer and gyroscope biases. A strategy to deal with the unobservability that arises when the quadrotor is in hover is also provided. Experimental data from a real-time implementation of the system on a 50 gram embedded computer are presented in addition to the simulations to demonstrate the efficacy of the proposed system. | Fast, On-board, Model-aided Visual-Inertial Odometry System for
Quadrotor Micro Aerial Vehicles | 8,725 |
In this article, we propose a sampling-based motion planning algorithm equipped with an information-theoretic convergence criterion for incremental informative motion planning. The proposed approach allows dense map representations and incorporates the full state uncertainty into the planning process. The problem is formulated as a constrained maximization problem. Our approach is built on rapidly-exploring information gathering algorithms and benefits from advantages of sampling-based optimal motion planning algorithms. We propose two information functions and their variants for fast and online computations. We prove an information-theoretic convergence for an entire exploration and information gathering mission based on the least upper bound of the average map entropy. A natural automatic stopping criterion for information-driven motion control results from the convergence analysis. We demonstrate the performance of the proposed algorithms using three scenarios: comparison of the proposed information functions and sensor configuration selection, robotic exploration in unknown environments, and a wireless signal strength monitoring task in a lake from a publicly available dataset collected using an autonomous surface vehicle. | Sampling-based Incremental Information Gathering with Applications to
Robotic Exploration and Environmental Monitoring | 8,726 |
This paper addresses 6-DOF (degree-of-freedom) tactile localization, i.e. the pose estimation of tridimensional objects given tactile measurements. This estimation problem is fundamental for the operation of autonomous robots that are often required to manipulate and grasp objects whose pose is a-priori unknown. The nature of tactile measurements, the strict time requirements for real-time operation and the multimodality of the involved probability distributions pose remarkable challenges and call for advanced nonlinear filtering techniques. Following a Bayesian approach, this paper proposes a novel and effective algorithm, named Memory Unscented Particle Filter (MUPF), which solves the 6-DOF localization problem recursively in real-time by only exploiting contact point measurements. MUPF combines a modified particle filter that incorporates a sliding memory of past measurements to better handle multimodal distributions, along with the unscented Kalman filter that moves the particles towards regions of the search space that are more likely with the measurements. The performance of the proposed MUPF algorithm has been assessed both in simulation and on a real robotic system equipped with tactile sensors (i.e., the iCub humanoid robot). The experiments show that the algorithm provides accurate and reliable localization even with a low number of particles and, hence, is compatible with real-time requirements. | Memory Unscented Particle Filter for 6-DOF Tactile Localization | 8,727 |
This paper proposes a novel method for randomized bin-picking based on learning. When a two-fingered gripper tries to pick an object from the pile, a finger often contacts a neighboring object. Even if a finger contacts a neighboring object, the target object will be successfully picked depending on the configuration of neighboring objects. In our proposed method, we use the visual information on neighboring objects to train the discriminator. Corresponding to a grasping posture of an object, the discriminator predicts whether or not the pick will be successful even if a finger contacts a neighboring object. We examine two learning algorithms, the linear support vector machine (SVM) and the random forest (RF) approaches. By using both methods, we demonstrate that the picking success rate is significantly higher than with conventional methods without learning. | Initial Experiments on Learning-Based Randomized Bin-Picking Allowing
Finger Contact with Neighboring Objects | 8,728 |
This paper discusses the concept and parameter design of a Robust Stair Climbing Compliant Modular Robot, capable of tackling stairs with overhangs. Modifying the geometry of the periphery of the wheels of our robot helps in tackling overhangs. Along with establishing a concept design, robust design parameters are set to minimize performance variation. The Grey-based Taguchi Method is adopted for providing an optimal setting for the design parameters of the robot. The robot prototype is shown to have successfully scaled stairs of varying dimensions, with overhang, thus corroborating the analysis performed. | Design of a Robust Stair Climbing Compliant Modular Robot to Tackle
Overhang on Stairs | 8,729 |
In this work we present a trajectory Optimization framework for whole-body motion planning through contacts. We demonstrate how the proposed approach can be applied to automatically discover different gaits and dynamic motions on a quadruped robot. In contrast to most previous methods, we do not pre-specify contact switches, timings, points or gait patterns, but they are a direct outcome of the optimization. Furthermore, we optimize over the entire dynamics of the robot, which enables the optimizer to fully leverage the capabilities of the robot. To illustrate the spectrum of achievable motions, here we show eight different tasks, which would require very different control structures when solved with state-of-the-art methods. Using our trajectory Optimization approach, we are solving each task with a simple, high level cost function and without any changes in the control structure. Furthermore, we fully integrated our approach with the robot's control and estimation framework such that optimization can be run online. By demonstrating a rough manipulation task with multiple dynamic contact switches, we exemplarily show how optimized trajectories and control inputs can be directly applied to hardware. | Trajectory Optimization Through Contacts and Automatic Gait Discovery
for Quadrupeds | 8,730 |
We present a novel approach to perform probabilistic collision detection between a high-DOF robot and high-DOF obstacles in dynamic, uncertain environments. In dynamic environments with a high-DOF robot and moving obstacles, our approach efficiently computes accurate collision probability between the robot and obstacles with upper error bounds. Furthermore, we describe a prediction algorithm for future obstacle position and motion that accounts for both spatial and temporal uncertainties. We present a trajectory optimization algorithm for high-DOF robots in dynamic, uncertain environments based on probabilistic collision detection. We highlight motion planning performance in challenging scenarios with robot arms operating in environments with dynamically moving human obstacles. | Fast and Bounded Probabilistic Collision Detection in Dynamic
Environments for High-DOF Trajectory Planning | 8,731 |
Existing techniques for motion imitation often suffer a certain level of latency due to their computational overhead or a large set of correspondence samples to search. To achieve real-time imitation with small latency, we present a framework in this paper to reconstruct motion on humanoids based on sparsely sampled correspondence. The imitation problem is formulated as finding the projection of a point from the configuration space of a human's poses into the configuration space of a humanoid. An optimal projection is defined as the one that minimizes a back-projected deviation among a group of candidates, which can be determined in a very efficient way. Benefited from this formulation, effective projections can be obtained by using sparse correspondence. Methods for generating these sparse correspondence samples have also been introduced. Our method is evaluated by applying the human's motion captured by a RGB-D sensor to a humanoid in real-time. Continuous motion can be realized and used in the example application of tele-operation. | Motion Imitation Based on Sparsely Sampled Correspondence | 8,732 |
While social robots are developed to provide assistance to users through social interactions, their behaviors are dominantly pre-programmed and remote-controlled. Despite the numerous robot control architectures being developed, very few offer reutilization opportunities in various therapeutic contexts. To bridge this gap, we propose a robot control architecture to be applied in different scenarios taking into account requirements from both therapeutic and robotic perspectives. As robot behaviors are kept at an abstract level and afterward mapped with the robot's morphology, the proposed architecture accommodates its applicability to a variety of social robot platforms. | A platform-independent robot control architecture for multiple
therapeutic scenarios | 8,733 |
Environment perception is a crucial ability for robot's interaction into an environment. One of the first steps in this direction is the combined problem of simultaneous localization and mapping (SLAM). A new method, called G-SLAM, is proposed, where the map is considered as a set of scattered points in the continuous space followed by a probability that states the existence of an obstacle in the subsequent point in space. A probabilistic approach with particle filters for the robot's pose estimation and an adaptive recursive algorithm for the map's probability distribution estimation is presented. Key feature of the G-SLAM method is the adaptive repositioning of the scattered points and their convergence around obstacles. In this paper the goal is to estimate the best robot trajectory along with the probability distribution of the obstacles in space. For experimental purposes a four wheel rear drive car kinematic model is used and results derived from real case scenarios are discussed. | Generative Simultaneous Localization and Mapping (G-SLAM) | 8,734 |
We address the problem where a mobile search agent seeks to find an unknown number of stationary objects distributed in a bounded search domain, and the search mission is subject to time/distance constraint. Our work accounts for false positives, false negatives and environmental uncertainty. We consider the case that the performance of a search sensor is dependent on the environment (e.g., clutter density), and therefore sensor performance is better in some locations than in others. We specifically consider applications where environmental information can be acquired either by a separate vehicle or by the same vehicle that performs the search task. Our main contribution in this study is to formally derive a decision-theoretic cost function to compute the locations where the environmental information should be acquired. For the cases where computing the optimal locations to sample the environment is computationally expensive, we offer an approximation approach that yields provable near-optimal paths. We show that our decision-theoretic cost function outperforms the information-maximization approach, which is often employed in similar applications. | Environmental Information Improves Robotic Search Performance | 8,735 |
The Cloud-based Advanced Robotics Laboratory (CARL) integrates a whole body controller and web-based teleoperation to enable any device with a web browser to access and control a humanoid robot. By integrating humanoid robots with the cloud, they are accessible from any Internet-connected device. Increased accessibility is important because few people have access to state-of-the-art humanoid robots limiting their rate of development. CARL's implementation is based on modern software libraries, frameworks, and middleware including Node.js, Socket.IO, ZMQ, ROS, Robot Web Tools, and ControlIt! Feasibility is demonstrated by having inexperienced human operators use a smartphone's web-browser to control Dreamer, a torque-controlled humanoid robot based on series elastic actuators, and make it perform a dual-arm manipulation task. The implementation serves as a proof-of-concept and foundation upon which many advanced humanoid robot technologies can be researched and developed. | Web Based Teleoperation of a Humanoid Robot | 8,736 |
Field Programmable Gate Arrays(FPGA) exceed the computing power of software based implementations by breaking the paradigm of sequential execution and accomplishing more per clock cycle by enabling hardware level parallelization at an architectural level. Introducing parallel architectures for a computationally intensive algorithm like Rapidly Exploring Random Trees(RRT) will result in an exploration that is fast, dense and uniform. Through a cost function delineated in later sections, FPGA based combinatorial architecture delivers superlative speed-up but consumes very high power while hierarchical architecture delivers relatively lower speed-up with acceptable power consumption levels. To combine the qualities of both, a hybrid architecture, that encompasses both combinatorial and hierarchical architecture, is designed. To determine the number of RRT nodes to be allotted to the combinatorial and hierarchical blocks of the hybrid architecture, a cost function, comprised of fundamentally inversely related speed-up and power parameters, is formulated. This maximization of cost function, with its associated constraints,is then mathematically solved using a modified branch and bound, that leads to optimal allocation of RRT modules to both blocks. It is observed that this hybrid architecture delivers the highest performance-per-watt out of the three architectures for differential, quad-copter and fixed wing kinematics. | FPGA based hybrid architecture for parallelizing RRT | 8,737 |
Computing globally optimal motion plans for a robot is challenging in part because it requires analyzing a robot's configuration space simultaneously from both a macroscopic viewpoint (i.e., considering paths in multiple homotopic classes) and a microscopic viewpoint (i.e., locally optimizing path quality). We introduce Interleaved Optimization with Sampling-based Motion Planning (IOS-MP), a new method that effectively combines global exploration and local optimization to quickly compute high quality motion plans. Our approach combines two paradigms: (1) asymptotically-optimal sampling-based motion planning, which is effective at global exploration but relatively slow at locally refining paths, and (2) optimization-based motion planning, which locally optimizes paths quickly but lacks a global view of the configuration space. IOS-MP iteratively alternates between global exploration and local optimization, sharing information between the two, to improve motion planning efficiency. We evaluate IOS-MP as it scales with respect to dimensionality and complexity, as well as demonstrate its effectiveness on a 7-DOF manipulator for tasks specified using goal configurations and workspace goal regions. | Interleaving Optimization with Sampling-Based Motion Planning (IOS-MP):
Combining Local Optimization with Global Exploration | 8,738 |
This technical report presents an introduction to different aspects of multi-fingered robot grasping. After having introduced relevant mathematical background for modeling, form and force closure are discussed. Next, we present an overview of various grasp planning algorithms with the objective of illustrating different approaches to solve this problem. Finally, we discuss grasp performance benchmarking. | Multi-Fingered Robotic Grasping: A Primer | 8,739 |
Real-time parking occupancy information is valuable for guiding drivers' searching for parking spaces. Recently many parking detection systems using range-based on-vehicle sensors are invented, but they disregard the practical difficulty of obtaining access to raw sensory data which are required for any feature-based algorithm. In this paper, we focus on a system using short-range radars (SRR) embedded in Advanced Driver Assistance System (ADAS) to collect occupancy information, and broadcast it through a connected vehicle network. The challenge that the data transmitted through ADAS unit has been encoded to sparse points is overcome by a statistical method instead of feature extractions. We propose a two-step classification algorithm combining Mean-Shift clustering and Support Vector Machine to analyze SRR-GPS data, and evaluate it through field experiments. The results show that the average Type I error rate for off-street parking is $15.23 \%$ and for on-street parking is $32.62\%$. In both cased the Type II error rates are less than $20 \%$. Bayesian updating can recursively improve the mapping results. This paper can provide a comprehensive method to elevate automotive sensors for the parking detection function. | A Statistical Method for Parking Spaces Occupancy Detection via
Automotive Radars | 8,740 |
In this paper, we propose a novel inverse Dynamic Reachability Map (iDRM) that allows a floating base system to find valid end-poses in complex and dynamically changing environments in real-time. End-pose planning for valid stance pose and collision-free configuration is an essential problem for humanoid applications, such as providing goal states for walking and motion planners. However, this is non-trivial in complex environments, where standing locations and reaching postures are restricted by obstacles. Our proposed iDRM customizes the robot-to-workspace occupation list and uses an online update algorithm to enable efficient reconstruction of the reachability map to guarantee that the selected end-poses are always collision-free. The iDRM was evaluated in a variety of reaching tasks using the 38 degree-of-freedom (DoF) humanoid robot Valkyrie. Our results show that the approach is capable of finding valid end-poses in a fraction of a second. Significantly, we also demonstrate that motion planning algorithms integrating our end-pose planning method are more efficient than those not utilizing this technique. | iDRM: Humanoid Motion Planning with Real-Time End-Pose Selection in
Complex Environments | 8,741 |
In this paper we present the PUMP (Parallel Uncertainty-aware Multiobjective Planning) algorithm for addressing the stochastic kinodynamic motion planning problem, whereby one seeks a low-cost, dynamically-feasible motion plan subject to a constraint on collision probability (CP). To ensure exhaustive evaluation of candidate motion plans (as needed to tradeoff the competing objectives of performance and safety), PUMP incrementally builds the Pareto front of the problem, accounting for the optimization objective and an approximation of CP. This is performed by a massively parallel multiobjective search, here implemented with a focus on GPUs. Upon termination of the exploration phase, PUMP searches the Pareto set of motion plans to identify the lowest cost solution that is certified to satisfy the CP constraint (according to an asymptotically exact estimator). We introduce a novel particle-based CP approximation scheme, designed for efficient GPU implementation, which accounts for dependencies over the history of a trajectory execution. We present numerical experiments for quadrotor planning wherein PUMP identifies solutions in ~100 ms, evaluating over one hundred thousand partial plans through the course of its exploration phase. The results show that this multiobjective search achieves a lower motion plan cost, for the same CP constraint, compared to a safety buffer-based search heuristic and repeated RRT trials. | Real-Time Stochastic Kinodynamic Motion Planning via Multiobjective
Search on GPUs | 8,742 |
A resolution complete optimal kinodynamic motion planning algorithm is presented and described as a generalized label correcting (GLC) method. In contrast to related algorithms, the GLC method does not require a local planning subroutine and benefits from a simple implementation. The key contributions of this paper are the construction and analysis of the GLC conditions which are the basis of the proposed algorithm. Numerical experiments demonstrate the running time of the GLC method to be less than the related SST algorithm. | A Generalized Label Correcting Method for Optimal Kinodynamic Motion
Planning | 8,743 |
Planning balanced and collision-free motion for humanoid robots is non-trivial, especially when they are operated in complex environments, such as reaching targets behind obstacles or through narrow passages. We propose a method that allows us to apply existing sampling--based algorithms to plan trajectories for humanoids by utilizing a customized state space representation, biased sampling strategies, and a steering function based on a robust inverse kinematics solver. Our approach requires no prior offline computation, thus one can easily transfer the work to new robot platforms. We tested the proposed method solving practical reaching tasks on a 38 degrees-of-freedom humanoid robot, NASA Valkyrie, showing that our method is able to generate valid motion plans that can be executed on advanced full-size humanoid robots. We also present a benchmark between different motion planning algorithms evaluated on a variety of reaching motion problems. This allows us to find suitable algorithms for solving humanoid motion planning problems, and to identify the limitations of these algorithms. | Scaling Sampling-based Motion Planning to Humanoid Robots | 8,744 |
We present a novel approach for collision-free global navigation for continuous-time multi-agent systems with general linear dynamics. Our approach is general and can be used to perform collision-free navigation in 2D and 3D workspaces with narrow passages and crowded regions. As part of pre-computation, we compute multiple bridges in the narrow or tight regions in the workspace using kinodynamic RRT algorithms. Our bridge has certain geometric characteristics, that en- able us to calculate a collision-free trajectory for each agent using simple interpolation at runtime. Moreover, we combine interpolated bridge trajectories with local multi-agent navigation algorithms to compute global collision-free paths for each agent. The overall approach combines the performance benefits of coupled multi-agent algorithms with the pre- computed trajectories of the bridges to handle challenging scenarios. In practice, our approach can handle tens to hundreds of agents in real-time on a single CPU core in 2D and 3D workspaces. | Efficient Multi-Agent Global Navigation Using Interpolating Bridges | 8,745 |
The function of protein, RNA, and DNA is modulated by fast, dynamic exchanges between three-dimensional conformations. Conformational sampling of biomolecules with exact and nullspace inverse kinematics, using rotatable bonds as revolute joints and non-covalent interactions as holonomic constraints, can accurately characterize these native ensembles. However, sampling biomolecules remains challenging owing to their ultra-high dimensional configuration spaces, and the requirement to avoid (self-) collisions, which results in low acceptance rates. Here, we present two novel mechanisms to overcome these limitations. First, we introduced temporary constraints between near-colliding links. The resulting constraint varieties instantaneously redirect the search for collision-free conformations, and couple motions between distant parts of the linkage. Second, we adapted a randomized Poisson-disk motion planner, which prevents local oversampling and widens the search, to ultra-high dimensions. We evaluated our algorithm on several model systems. Our contributions apply to general high-dimensional motion planning problems in static and dynamic environments with obstacles. | Collision-Free Poisson Motion Planning in Ultra High-Dimensional
Molecular Conformation Spaces | 8,746 |
Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. This paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot. | Learning to Prevent Monocular SLAM Failure using Reinforcement Learning | 8,747 |
We present a method for humanoid robot walking on partial footholds such as small stepping stones and rocks with sharp surfaces. Our algorithm does not rely on prior knowledge of the foothold, but information about an expected foothold can be used to improve the stepping performance. After a step is taken, the robot explores the new contact surface by attempting to shift the center of pressure around the foot. The available foothold is inferred by the way in which the foot rotates about contact edges and/or by the achieved center of pressure locations on the foot during exploration. This estimated contact area is then used by a whole body momentum-based control algorithm. To walk and balance on partial footholds, we combine fast, dynamic stepping with the use of upper body angular momentum to regain balance. We applied this method to the Atlas humanoid designed by Boston Dynamics to walk over small contact surfaces, such as line and point contacts. We present experimental results and discuss performance limitations. | Walking on Partial Footholds Including Line Contacts with the Humanoid
Robot Atlas | 8,748 |
Robotic ultrasound has the potential to assist and guide physicians during interventions. In this work, we present a set of methods and a workflow to enable autonomous MRI-guided ultrasound acquisitions. Our approach uses a structured-light 3D scanner for patient-to-robot and image-to-patient calibration, which in turn is used to plan 3D ultrasound trajectories. These MRI-based trajectories are followed autonomously by the robot and are further refined online using automatic MRI/US registration. Despite the low spatial resolution of structured light scanners, the initial planned acquisition path can be followed with an accuracy of 2.46 +/- 0.96 mm. This leads to a good initialization of the MRI/US registration: the 3D-scan-based alignment for planning and acquisition shows an accuracy (distance between planned ultrasound and MRI) of 4.47 mm, and 0.97 mm after an online-update of the calibration based on a closed loop registration. | Towards MRI-Based Autonomous Robotic US Acquisitions: A First
Feasibility Study | 8,749 |
The humanoid robot iCub is a research platform of the Fondazione Istituto Italiano di Tecnologia (IIT), spread among different institutes around the world. In the most recent version of iCub, the robot is equipped with stronger legs and bigger feet, allowing it to perform balancing and walking motions that were not possible with the first generations. Despite the new legs hardware, walking has been rarely performed on the iCub robot. In this work the objective is to implement walking motions on the robot, from which we want to analyze its walking capabilities. We developed software modules based on extensions of classic techniques such as the ZMP based pattern generator and position control to identify which are the characteristics as well as limitations of the robot against different walking tasks in order to give the users a reference of the performance of the robot. Most of the experiments have been performed with HeiCub, a reduced version of iCub without arms and head. | Walking of the iCub humanoid robot in different scenarios:
implementation and performance analysis | 8,750 |
Linear models for control and motion generation of humanoid robots have received significant attention in the past years, not only due to their well known theoretical guarantees, but also because of practical computational advantages. However, to tackle more challenging tasks and scenarios such as locomotion on uneven terrain, a more expressive model is required. In this paper, we are interested in contact interaction-centered motion optimization based on the momentum dynamics model. This model is non-linear and non-convex; however, we find a relaxation of the problem that allows us to formulate it as a single convex quadratically-constrained quadratic program (QCQP) that can be very efficiently optimized. Furthermore, experimental results suggest that this relaxation is tight and therefore useful for multi-contact planning. This convex model is then coupled to the optimization of end-effector contacts location using a mixed integer program, which can be solved in realtime. This becomes relevant e.g. to recover from external pushes, where a predefined stepping plan is likely to fail and an online adaptation of the contact location is needed. The performance of our algorithm is demonstrated in several multi-contact scenarios for humanoid robot. | A Convex Model of Momentum Dynamics for Multi-Contact Motion Generation | 8,751 |
We present a multi-contact walking pattern generator based on preview-control of the 3D acceleration of the center of mass (COM). A key point in the design of our algorithm is the calculation of contact-stability constraints. Thanks to a mathematical observation on the algebraic nature of the frictional wrench cone, we show that the 3D volume of feasible COM accelerations is a always a downward-pointing cone. We reduce its computation to a convex hull of (dual) 2D points, for which optimal O(n log n) algorithms are readily available. This reformulation brings a significant speedup compared to previous methods, which allows us to compute time-varying contact-stability criteria fast enough for the control loop. Next, we propose a conservative trajectory-wide contact-stability criterion, which can be derived from COM-acceleration volumes at marginal cost and directly applied in a model-predictive controller. We finally implement this pipeline and exemplify it with the HRP-4 humanoid model in multi-contact dynamically walking scenarios. | Multi-contact Walking Pattern Generation based on Model Preview Control
of 3D COM Accelerations | 8,752 |
It has long been hoped that model-based control will improve tracking performance while maintaining or increasing compliance. This hope hinges on having or being able to estimate an accurate inverse dynamics model. As a result, substantial effort has gone into modeling and estimating dynamics (error) models. Most recent research has focused on learning the true inverse dynamics using data points mapping observed accelerations to the torques used to generate them. Unfortunately, if the initial tracking error is bad, such learning processes may train substantially off-distribution to predict well on actual observed acceleration rather then the desired accelerations. This work takes a different approach. We define a class of gradient-based online learning algorithms we term Direct Online Optimization for Modeling Errors in Dynamics (DOOMED) that directly minimize an objective measuring the divergence between actual and desired accelerations. Our objective is defined in terms of the true system's unknown dynamics and is therefore impossible to evaluate. However, we show that its gradient is measurable online from system data. We develop a novel adaptive control approach based on running online learning to directly correct (inverse) dynamics errors in real time using the data stream from the robot to accurately achieve desired accelerations during execution. | DOOMED: Direct Online Optimization of Modeling Errors in Dynamics | 8,753 |
This paper proposes a iterative visual recognition system for learning based randomized bin-picking. Since the configuration on randomly stacked objects while executing the current picking trial is just partially different from the configuration while executing the previous picking trial, we consider detecting the poses of objects just by using a part of visual image taken at the current picking trial where it is different from the visual image taken at the previous picking trial. By using this method, we do not need to try to detect the poses of all objects included in the pile at every picking trial. Assuming the 3D vision sensor attached at the wrist of a manipulator, we first explain a method to determine the pose of a 3D vision sensor maximizing the visibility of randomly stacked objects. Then, we explain a method for detecting the poses of randomly stacked objects. Effectiveness of our proposed approach is confirmed by experiments using a dual-arm manipulator where a 3D vision sensor and the two-fingered hand attached at the right and the left wrists, respectively. | Iterative Visual Recognition for Learning Based Randomized Bin-Picking | 8,754 |
This paper considers the problem of cooperative localization (CL) using inter-robot measurements for a group of networked robots with limited on-board resources. We propose a novel recursive algorithm in which each robot localizes itself in a global coordinate frame by local dead reckoning, and opportunistically corrects its pose estimate whenever it receives a relative measurement update message from a server. The computation and storage cost per robot in terms of the size of the team is of order O(1), and the robots are only required to transmit information when they are involved in a relative measurement. The server also only needs to compute and transmit update messages when it receives an inter-robot measurement. We show that under perfect communication, our algorithm is an alternative but exact implementation of a joint CL for the entire team via Extended Kalman Filter (EKF). The perfect communication however is not a hard requirement. In fact, we show that our algorithm is intrinsically robust with respect to communication failures, with formal guarantees that the updated estimates of the robots receiving the update message are of minimum variance in a first-order approximate sense at that given timestep. We demonstrate the performance of the algorithm in simulation and experiments. | Server assisted distributed cooperative localization over unreliable
communication links | 8,755 |
Mobile manipulation problems involving many objects are challenging to solve due to the high dimensionality and multi-modality of their hybrid configuration spaces. Planners that perform a purely geometric search are prohibitively slow for solving these problems because they are unable to factor the configuration space. Symbolic task planners can efficiently construct plans involving many variables but cannot represent the geometric and kinematic constraints required in manipulation. We present the FFRob algorithm for solving task and motion planning problems. First, we introduce Extended Action Specification (EAS) as a general purpose planning representation that supports arbitrary predicates as conditions. We adapt existing heuristic search ideas for solving \proc{strips} planning problems, particularly delete-relaxations, to solve EAS problem instances. We then apply the EAS representation and planners to manipulation problems resulting in FFRob. FFRob iteratively discretizes task and motion planning problems using batch sampling of manipulation primitives and a multi-query roadmap structure that can be conditionalized to evaluate reachability under different placements of movable objects. This structure enables the EAS planner to efficiently compute heuristics that incorporate geometric and kinematic planning constraints to give a tight estimate of the distance to the goal. Additionally, we show FFRob is probabilistically complete and has finite expected runtime. Finally, we empirically demonstrate FFRob's effectiveness on complex and diverse task and motion planning tasks including rearrangement planning and navigation among movable objects. | FFRob: Leveraging Symbolic Planning for Efficient Task and Motion
Planning | 8,756 |
The motion of robots and objects in our world is often highly dependent upon contact. When contact is expected but does not occur or when contact is not expected but does occur, robot behavior diverges from plan, often disastrously. This paper describes an approach that uses simulation to detect possible such behavioral divergences on real robots. This approach, and others like it, could be applied to validation of robot behaviors, mechanism design, and even online planning. The particle trace approach samples robot modeling parameters, sensory readings, and state estimates to evaluate a robot's behavior statistically over a range of conditions. We demonstrate that combining even coarse estimates of state and modeling parameters with fast multibody simulation can be sufficient to detect divergent robot behavior and characterize robot performance in the real world. Correspondingly, this approach could be used to assess risk and find and analyze likely failures, given the extensive data that such simulations can generate. We assess this approach on actuated, high degree-of-freedom robot locomotion examples, a picking task with a fixed-base manipulator, and an unpowered passive dynamic walker. This research works toward understanding how multi-rigid body simulations can better characterize the behavior of robots without significantly compliant elements. | Particle Traces for Detecting Divergent Robot Behavior | 8,757 |
Planning of any motion starts by planning the trajectory of the CoM. It is of the highest importance to ensure that the robot will be able to perform planned trajectory. With increasing capabilities of the humanoid robots, the case when contacts are spatially distributed should be considered. In this paper, it is shown that there are some contact configurations in which any acceleration of the center of mass (CoM) is feasible. The procedure for identifying such a configurations is presented, as well as its physical meaning. On the other hand, for the configurations in which the constraint on CoM movement exists, it will be shown how to find that linear constraint, which defines the space of feasible motion. The proposed algorithm has a low complexity and to speed up the procedure even further, it will be shown that the whole procedure needs to be run only once when contact configuration changes. As the CoM moves, the new constraints can be calculated from the initial one, thus yielding significant computation speedup. The methods are illustrated in two simulated scenarios. | Increased Mobility in Presence of Multiple Contacts - Identifying
Contact Configurations that Enable Arbitrary Acceleration of CoM | 8,758 |
An effective paradigm for simulating the dynamics of robots that locomote and manipulate is multi-rigid body simulation with rigid contact. This paradigm provides reasonable tradeoffs between accuracy, running time, and simplicity of parameter selection and identification. The Stewart-Trinkle/Anitescu-Potra time stepping approach is the basis of many existing implementations. It successfully treats inconsistent (Painleve-type) contact configurations, efficiently handles many contact events occurring in short time intervals, and provably converges to the solution of the continuous time differential algebraic equations (DAEs) as the integration step size tends to zero. However, there is currently no means to determine when the solution has largely converged, i.e., when smaller integration steps would result in only small increases in accuracy. The present work describes an approach that computes the event times (when the set of active equations in a DAE changes) of all contact/impact events for a multi-body simulation, toward using integration techniques with error control to compute a solution with desired accuracy. We also describe a first-order, variable integration approach that ensures that rigid bodies with convex polytopic geometries never interpenetrate. This approach permits taking large steps when possible and takes small steps when contact is complex. | True Rigidity: Interpenetration-free Multi-Body Simulation with
Polytopic Contact | 8,759 |
Maximum likelihood estimation (MLE) is a well-known estimation method used in many robotic and computer vision applications. Under Gaussian assumption, the MLE converts to a nonlinear least squares (NLS) problem. Efficient solutions to NLS exist and they are based on iteratively solving sparse linear systems until convergence. In general, the existing solutions provide only an estimation of the mean state vector, the resulting covariance being computationally too expensive to recover. Nevertheless, in many simultaneous localisation and mapping (SLAM) applications, knowing only the mean vector is not enough. Data association, obtaining reduced state representations, active decisions and next best view are only a few of the applications that require fast state covariance recovery. Furthermore, computer vision and robotic applications are in general performed online. In this case, the state is updated and recomputed every step and its size is continuously growing, therefore, the estimation process may become highly computationally demanding. This paper introduces a general framework for incremental MLE called SLAM++, which fully benefits from the incremental nature of the online applications, and provides efficient estimation of both the mean and the covariance of the estimate. Based on that, we propose a strategy for maintaining a sparse and scalable state representation for large scale mapping, which uses information theory measures to integrate only informative and non-redundant contributions to the state representation. SLAM++ differs from existing implementations by performing all the matrix operations by blocks. This led to extremely fast matrix manipulation and arithmetic operations. Even though this paper tests SLAM++ efficiency on SLAM problems, its applicability remains general. | Highly Efficient Compact Pose SLAM with SLAM++ | 8,760 |
This paper presents a mid-level planning system for object reorientation. It includes a grasp planner, a placement planner, and a regrasp sequence solver. Given the initial and goal poses of an object, the mid-level planning system finds a sequence of hand configurations that reorient the object from the initial to the goal. This mid-level planning system is open to low-level motion planning algorithm by providing two end-effector poses as the input. It is also open to high-level symbolic planners by providing interface functions like placing an object to a given position at a given rotation. The planning system is demonstrated with several simulation examples and real-robot executions using a Kawada Hiro robot and Robotiq 85 grippers. | A Mid-level Planning System for Object Reorientation | 8,761 |
This paper exhibits a short-run correspondence method appropriate for swarm versatile robots application. Infrared is utilized for transmitting and accepting information and obstruction location. The infrared correspondence code based swarm signaling is utilized for an independent versatile robot communication system in this research. A code based signaling system is developed for transmitting information between different entities of robot. The reflected infrared sign is additionally utilized for separation estimation for obstruction evasion. Investigation of robot demonstrates the possibility of utilizing infrared signs to get a solid nearby correspondence between swarm portable robots. This paper exhibits a basic decentralized control for swarm of self-collecting robots. Every robot in the code based swarm signaling is completely self-governing and controlled utilizing a conduct based methodology with just infrared-based nearby detecting and correspondences. The viability of the methodology has been checked with simulation, for a set of swarm robots. | An approach of IR-Based short-range correspondence systems for swarm
robot balanced requisitions and communications | 8,762 |
Occupancy grids are the most common framework when it comes to creating a map of the environment using a robot. This paper studies occupancy grids from the motion planning perspective and proposes a mapping method that provides richer data (map) for the purpose of planning and collision avoidance. Typically, in occupancy grid mapping, each cell contains a single number representing the probability of cell being occupied. This leads to conflicts in the map, and more importantly inconsistency between the map error and reported confidence values. Such inconsistencies pose challenges for the planner that relies on the generated map for planning motions. In this work, we store a richer data at each voxel including an accurate estimate of the variance of occupancy. We show that in addition to achieving maps that are often more accurate than tradition methods, the proposed filtering scheme demonstrates a much higher level of consistency between its error and its reported confidence. This allows the planner to reason about acquisition of the future sensory information. Such planning can lead to active perception maneuvers that while guiding the robot toward the goal aims at increasing the confidence in parts of the map that are relevant to accomplishing the task. | SMAP: Simultaneous Mapping and Planning on Occupancy Grids | 8,763 |
We present a motion planning algorithm to compute collision-free and smooth trajectories for high-DOF robots interacting with humans in a shared workspace. Our approach uses offline learning of human actions along with temporal coherence to predict the human actions. Our intention-aware online planning algorithm uses the learned database to compute a reliable trajectory based on the predicted actions. We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. We also describe novel techniques to account for noise in human motion prediction. We highlight the performance of our planning algorithm in complex simulated scenarios and real world benchmarks with 7-DOF robot arms operating in a workspace with a human performing complex tasks. We demonstrate the benefits of our intention-aware planner in terms of computing safe trajectories in such uncertain environments. | I-Planner: Intention-Aware Motion Planning Using Learning Based Human
Motion Prediction | 8,764 |
This paper presents an extension of the OpenAI Gym for robotics using the Robot Operating System (ROS) and the Gazebo simulator. The content discusses the software architecture proposed and the results obtained by using two Reinforcement Learning techniques: Q-Learning and Sarsa. Ultimately, the output of this work presents a benchmarking system for robotics that allows different techniques and algorithms to be compared using the same virtual conditions. | Extending the OpenAI Gym for robotics: a toolkit for reinforcement
learning using ROS and Gazebo | 8,765 |
We present Probabilistic Reciprocal Velocity Obstacle or PRVO as a general algorithm for navigating multiple robots under perception and motion uncertainty. PRVO is defined as the space of velocities that ensures dynamic collision avoidance between a pair of robots with a specified probability. Our approach is based on defining chance constraints over the inequalities defined by the deterministic Reciprocal Velocity Obstacle (RVO). The computational complexity of the proposed probabilistic RVO is comparable to the deterministic counterpart. This is achieved by a series of reformulations where we first substitute the computationally intractable chance constraints with a family of surrogate constraints and then adopt a time scaling based solution methodology to efficiently characterize their solution space. Further, we also show that the solution space of each member of the family of surrogate constraints can be mapped in closed form to the probability with which the original chance constraints are satisfied and thus consequently to probability of collision avoidance. We validate our formulations through numerical simulations where we highlight the importance of incorporating the effect of motion uncertainty and the advantages of PRVO over existing formulations which handles the effect of uncertainty by using conservative bounding volumes. | Chance constraint based multi agent navigation under uncertainty | 8,766 |
This paper demonstrates the ability of the harmonic potential field, HPF, planning method to generate a well-behaved constrained path for a robot with second order dynamics in a cluttered environment. It is shown that HPF-based controllers may be developed for holonomic as well as nonholonomic robots to effectively suppress the effect of inertial forces on the robot trajectory while maintaining all the attractive features of a purely kinematic HPF planner. The capabilities of the suggested navigation controller are demonstrated using simulation results. Comparisons are also supplied with other approaches used for converting the guidance signal from a purely kinematic HPF planner into a navigation control signal. | Managing The Dynamics Of A Harmonic Potential Field-Guided Robot In A
Cluttered Environment | 8,767 |
This paper proposes control laws ensuring the stabilization of a time-varying desired joint trajectory, as well as joint limit avoidance, in the case of fully-actuated manipulators. The key idea is to perform a parametrization of the feasible joint space in terms of exogenous states. It follows that the control of these states allows for joint limit avoidance. One of the main outcomes of this paper is that position terms in control laws are replaced by parametrized terms, where joint limits must be avoided. Stability and convergence of time-varying reference trajectories obtained with the proposed method are demonstrated to be in the sense of Lyapunov. The introduced control laws are verified by carrying out experiments on two degrees-of-freedom of the humanoid robot iCub. | On-line Joint Limit Avoidance for Torque Controlled Robots by Joint
Space Parametrization | 8,768 |
We study the problem of simultaneously reconstructing a polygonal room and a trajectory of a device equipped with a (nearly) collocated omnidirectional source and receiver. The device measures arrival times of echoes of pulses emitted by the source and picked up by the receiver. No prior knowledge about the device's trajectory is required. Most existing approaches addressing this problem assume multiple sources or receivers, or they assume that some of these are static, serving as beacons. Unlike earlier approaches, we take into account the measurement noise and various constraints on the geometry by formulating the solution as a minimizer of a cost function similar to \emph{stress} in multidimensional scaling. We study uniqueness of the reconstruction from first-order echoes, and we show that in addition to the usual invariance to rigid motions, new ambiguities arise for important classes of rooms and trajectories. We support our theoretical developments with a number of numerical experiments. | Look, no Beacons! Optimal All-in-One EchoSLAM | 8,769 |
If autonomous vehicles are to be widely accepted, we need to ensure their safe operation. For this reason, verification and validation (V&V) approaches must be developed that are suitable for this domain. Model checking is a formal technique which allows us to exhaustively explore the paths of an abstract model of a system. Using a probabilistic model checker such as PRISM, we may determine properties such as the expected time for a mission, or the probability that a specific mission failure occurs. However, model checking of complex systems is difficult due to the loss of information during abstraction. This is especially so when considering systems such as autonomous vehicles which are subject to external influences. An alternative solution is the use of Monte Carlo simulation to explore the results of a continuous-time model of the system. The main disadvantage of this approach is that the approach is not exhaustive as not all executions of the system are analysed. We are therefore interested in developing a framework for formal verification of autonomous vehicles, using Monte Carlo simulation to inform and validate our symbolic models during the initial stages of development. In this paper, we present a continuous-time model of a quadrotor unmanned aircraft undertaking an autonomous mission. We employ this model in Monte Carlo simulation to obtain specific mission properties which will inform the symbolic models employed in formal verification. | A Continuous-Time Model of an Autonomous Aerial Vehicle to Inform and
Validate Formal Verification Methods | 8,770 |
Consider a swarm of particles controlled by global inputs. This paper presents algorithms for shaping such swarms in 2D using boundary walls. The range of configurations created by conforming a swarm to a boundary wall is limited. We describe the set of stable configurations of a swarm in two canonical workspaces, a circle and a square. To increase the diversity of configurations, we add boundary interaction to our model. We provide algorithms using friction with walls to place two robots at arbitrary locations in a rectangular workspace. Next, we extend this algorithm to place $n$ agents at desired locations. We conclude with efficient techniques to control the covariance of a swarm not possible without wall-friction. Simulations and hardware implementations with 100 robots validate these results. These methods may have particular relevance for current micro- and nano-robots controlled by global inputs. | Algorithms For Shaping a Particle Swarm With a Shared Control Input
Using Boundary Interaction | 8,771 |
A new path planning method for Mobile Robots (MR) has been developed and implemented. On the one hand, based on the shortest path from the start point to the goal point, this path planner can choose the best moving directions of the MR, which helps to reach the target point as soon as possible. On the other hand, with an intelligent obstacle avoidance, our method can find the target point with the near-shortest path length while avoiding some infinite loop traps of several obstacles in unknown environments. The combination of two approaches helps the MR to reach the target point with a very reliable algorithm. Moreover, by continuous updates of the on-board sensors information, this approach can generate the MRs trajectory both in static and dynamic environments. A large number of simulations in some similar studies environments demonstrate the power of the proposed path planning algorithm. | Path planning and Obstacle avoidance approaches for Mobile robot | 8,772 |
In the classical context of robotic mapping and localization, map matching is typically defined as the task of finding a rigid transformation (i.e., 3DOF rotation/translation on the 2D moving plane) that aligns the query and reference maps built by mobile robots. This definition is valid in loop-rich trajectories that enable a mapper robot to close many loops, for which precise maps can be assumed. The same cannot be said about the newly emerging autonomous navigation and driving systems, which typically operate in loop-less trajectories that have no large loop (e.g., straight paths). In this paper, we propose a solution that overcomes this limitation by merging the two maps. Our study is motivated by the observation that even when there is no large loop in either the query or reference map, many loops can often be obtained in the merged map. We add two new aspects to map matching: (1) image retrieval with discriminative deep convolutional neural network (DCNN) features, which efficiently generates a small number of good initial alignment hypotheses; and (2) map merge, which jointly deforms the two maps to minimize differences in shape between them. To realize practical computation time, we also present a preemption scheme that avoids excessive evaluation of useless map-matching hypotheses. To verify our approach experimentally, we created a novel collection of uncertain loop-less maps by utilizing the recently published North Campus Long-Term (NCLT) dataset and its ground-truth GPS data. The results obtained using these map collections confirm that our approach improves on previous map-matching approaches. | Deformable Map Matching for Uncertain Loop-Less Maps | 8,773 |
In order to deal with the scaling problem of volumetric map representations we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance fields. We show that these compressed maps can be used as meaningful descriptors for selective decompression in scenarios relevant to robotic applications. As compression methods, we compare using PCA-derived low-dimensional bases to non-linear auto-encoder networks and novel mixed architectures that combine both. Selecting two application-oriented performance metrics, we evaluate the impact of different compression rates on reconstruction fidelity as well as to the task of map-aided ego-motion estimation. It is demonstrated that lossily compressed distance fields used as cost functions for ego-motion estimation, can outperform their uncompressed counterparts in challenging scenarios from standard RGB-D data-sets. | An Eigenshapes Approach to Compressed Signed Distance Fields and Their
Utility in Robot Mapping | 8,774 |
We present an efficient variational integrator for multibody systems. Variational integrators reformulate the equations of motion for multibody systems as discrete Euler-Lagrange (DEL) equations, transforming forward integration into a root-finding problem for the DEL equations. Variational integrators have been shown to be more robust and accurate in preserving fundamental properties of systems, such as momentum and energy, than many frequently used numerical integrators. However, state-of-the-art algorithms suffer from $O(n^3)$ complexity, which is prohibitive for articulated multibody systems with a large number of degrees of freedom, $n$, in generalized coordinates. Our key contribution is to derive a recursive algorithm that evaluates DEL equations in $O(n)$, which scales up well for complex multibody systems such as humanoid robots. Inspired by recursive Newton-Euler algorithm, our key insight is to formulate DEL equation individually for each body rather than for the entire system. Furthermore, we introduce a new quasi-Newton method that exploits the impulse-based dynamics algorithm, which is also $O(n)$, to avoid the expensive Jacobian inversion in solving DEL equations. We demonstrate scalability and efficiency, as well as extensibility to holonomic constraints through several case studies. | A Linear-Time Variational Integrator for Multibody Systems | 8,775 |
Robots are becoming ever more autonomous. This expanding ability to take unsupervised decisions renders it imperative that mechanisms are in place to guarantee the safety of behaviours executed by the robot. Moreover, smart autonomous robots should be more than safe; they should also be explicitly ethical -- able to both choose and justify actions that prevent harm. Indeed, as the cognitive, perceptual and motor capabilities of robots expand, they will be expected to have an improved capacity for making moral judgements. We present a control architecture that supplements existing robot controllers. This so-called Ethical Layer ensures robots behave according to a predetermined set of ethical rules by predicting the outcomes of possible actions and evaluating the predicted outcomes against those rules. To validate the proposed architecture, we implement it on a humanoid robot so that it behaves according to Asimov's laws of robotics. In a series of four experiments, using a second humanoid robot as a proxy for the human, we demonstrate that the proposed Ethical Layer enables the robot to prevent the human from coming to harm. | An architecture for ethical robots | 8,776 |
Guided policy search is a method for reinforcement learning that trains a general policy for accomplishing a given task by guiding the learning of the policy with multiple guiding distributions. Guided policy search relies on learning an underlying dynamical model of the environment and then, at each iteration of the algorithm, using that model to gradually improve the policy. This model, though, often makes the assumption that the environment dynamics are markovian, e.g., depend only on the current state and control signal. In this paper we apply guided policy search to a problem with non-markovian dynamics. Specifically, we apply it to the problem of pouring a precise amount of liquid from a cup into a bowl, where many of the sensor measurements experience non-trivial amounts of delay. We show that, with relatively simple state augmentation, guided policy search can be extended to non-markovian dynamical systems, where the non-markovianess is caused by delayed sensor readings. | Guided Policy Search with Delayed Sensor Measurements | 8,777 |
This work contributes to a compositional theory of "co-design" that allows to optimally design a robotic platform. In this framework, the user describes each subsystem as a monotone relation between "functionality" provided and "resources" required. These models can be easily composed to express the co-design constraints among different subsystems. The user then queries the model, to obtain the design with minimal resources usage, subject to a lower bound on the provided functionality. This paper concerns the introduction of uncertainty in the framework. Uncertainty has two roles: first, it allows to deal with limited knowledge of the models; second, it also can be used to generate consistent relaxations of a problem, as the computation requirements can be lowered, should the user accept some uncertainty in the answer. | Uncertainty in Monotone Co-Design Problems | 8,778 |
This paper develops a planner to find an optimal assembly sequence to assemble several objects. The input to the planner is the mesh models of the objects, the relative poses between the objects in the assembly, and the final pose of the assembly. The output is an optimal assembly sequence, namely (1) in which order should one assemble the objects, (2) from which directions should the objects be dropped, and (3) candidate grasps of each object. The proposed planner finds the optimal solution by automatically permuting, evaluating, and searching the possible assembly sequences considering stability, graspability, and assemblability qualities. It is expected to guide robots to do assembly using translational motion. The output provides initial and goal configurations to motion planning algorithms. It is ready to be used by robots and is demonstrated using several simulations and real-world executions. | Assembly Sequence Planning for Motion Planning | 8,779 |
We present a novel motion planning algorithm for transferring a liquid body from a source to a target container. Our approach uses a receding-horizon optimization strategy that takes into account fluid constraints and avoids collisions. In order to efficiently handle the high-dimensional configuration space of a liquid body, we use system identification to learn its dynamics characteristics using a neural network. We generate the training dataset using stochastic optimization in a transfer-problem-specific search space. The runtime feedback motion planner is used for real-time planning and we observe high success rate in our simulated 2D and 3D fluid transfer benchmarks. | Feedback Motion Planning for Liquid Transfer using Supervised Learning | 8,780 |
The necessity of maintaining a robust antiterrorist task force has become imperative in recent times with resurgence of rogue element in the society. A well equipped combat force warrants the safety and security of citizens and the integrity of the sovereign state. In this paper we propose a novel teleoperating robot which can play a major role in combat, rescue and reconnaissance missions by substantially reducing loss of human soldiers in such hostile environments. The proposed robotic solution consists of an unmanned ground vehicle equipped with an IP camera visual system broadcasting real-time video data to a remote cloud server. With the advancement in machine learning algorithms in the field of computer vision, we incorporate state of the art deep convolutional neural networks to identify and predict individuals with malevolent intent. The classification is performed on every frame of the video stream by the trained network in the cloud server. The predicted output of the network is overlaid on the video stream with specific colour marks and prediction percentage. Finally the data is resized into half-side by side format and streamed to the head mount display worn by the human controller which facilitates first person view of the scenario. The ground vehicle is also coupled with an unmanned aerial vehicle for aerial surveillance. The proposed scheme is an assistive system and the final decision evidently lies with the human handler. | HMD Vision-based Teleoperating UGV and UAV for Hostile Environment using
Deep Learning | 8,781 |
We propose a new parallel framework for fast computation of inverse and forward dynamics of articulated robots based on prefix sums (scans). We re-investigate the well-known recursive Newton-Euler formulation of robot dynamics and show that the forward-backward propagation process for robot inverse dynamics is equivalent to two scan operations on certain semigroups. We show that the state-of-the-art forward dynamics algorithms may almost completely be cast into a sequence of scan operations, with unscannable parts clearly identified. This suggests a serial-parallel hybrid approach for systems with a moderate number of links. We implement our scan based algorithms on Nvidia CUDA platform with performance compared with multithreading CPU-based recursive algorithms; a significant level of acceleration is demonstrated. | Parallel Dynamics Computation using Prefix Sum Operations | 8,782 |
We present a model predictive controller (MPC) for multi-contact locomotion where predictive optimizations are realized by time-optimal path parameterization (TOPP). A key feature of this solution is that, contrary to existing planners where step timings are provided as inputs, here the timing between contact switches is computed as output of a fast nonlinear optimization. This is particularly appealing to multi-contact locomotion, where proper timings depend on terrain topology and suitable heuristics are unknown. We show how to formulate legged locomotion as a TOPP problem and demonstrate the behavior of the resulting TOPP-MPC controller in simulations with a model of the HRP-4 humanoid robot. | When to make a step? Tackling the timing problem in multi-contact
locomotion by TOPP-MPC | 8,783 |
This paper describes the Robotarium -- a remotely accessible, multi-robot research facility. The impetus behind the Robotarium is that multi-robot testbeds constitute an integral and essential part of the multi-robot research cycle, yet they are expensive, complex, and time-consuming to develop, operate, and maintain. These resource constraints, in turn, limit access for large groups of researchers and students, which is what the Robotarium is remedying by providing users with remote access to a state-of-the-art multi-robot test facility. This paper details the design and operation of the Robotarium and discusses the considerations one must take when making complex hardware remotely accessible. In particular, safety must be built into the system already at the design phase without overly constraining what coordinated control programs users can upload and execute, which calls for minimally invasive safety routines with provable performance guarantees. | The Robotarium: A remotely accessible swarm robotics research testbed | 8,784 |
In this paper we present a novel dataset for a critical aspect of autonomous driving, the joint attention that must occur between drivers and of pedestrians, cyclists or other drivers. This dataset is produced with the intention of demonstrating the behavioral variability of traffic participants. We also show how visual complexity of the behaviors and scene understanding is affected by various factors such as different weather conditions, geographical locations, traffic and demographics of the people involved. The ground truth data conveys information regarding the location of participants (bounding boxes), the physical conditions (e.g. lighting and speed) and the behavior of the parties involved. | Joint Attention in Autonomous Driving (JAAD) | 8,785 |
microMVP is an affordable, portable, and open source micro-scale mobile robot platform designed for robotics research and education. As a complete and unique multi-vehicle platform enabled by 3D printing and the maker culture, microMVP can be easily reproduced and requires little maintenance: a set of six micro vehicles, each measuring $8\times 5\times 6$ cubic centimeters and weighing under $100$ grams, and the accompanying tracking platform can be fully assembled in under two hours, all from readily available components. In this paper, we describe microMVP's hardware and software architecture, and the design thoughts that go into the making of the platform. The capabilities of microMVP APIs are then demonstrated with several single- and multi-robot path and motion planning algorithms. microMVP supports all common operation systems. | A Portable, 3D-Printing Enabled Multi-Vehicle Platform for Robotics
Research and Education | 8,786 |
Localization in a global map is critical to success in many autonomous robot missions. This is particularly challenging for multi-robot operations in unknown and adverse environments. Here, we are concerned with providing a small unmanned ground vehicle (UGV) the ability to localize itself within a 2.5D aerial map generated from imagery captured by a low-flying unmanned aerial vehicle (UAV). We consider the scenario where GPS is unavailable and appearance-based scene changes may have occurred between the UAV's flight and the start of the UGV's mission. We present a GPS-free solution to this localization problem that is robust to appearance shifts by exploiting high-level, semantic representations of image and depth data. Using data gathered at an urban test site, we empirically demonstrate that our technique yields results within five meters of a GPS-based approach. | Semantics for UGV Registration in GPS-denied Environments | 8,787 |
The design of mobile autonomous robots is challenging due to the limited on-board resources such as processing power and energy. A promising approach is to generate intelligent schedules that reduce the resource consumption while maintaining best performance, or more interestingly, to trade off reduced resource consumption for a slightly lower but still acceptable level of performance. In this paper, we provide a framework to aid designers in exploring such resource-performance trade-offs and finding schedules for mobile robots, guided by questions such as "what is the minimum resource budget required to achieve a given level of performance?" The framework is based on a quantitative multi-objective verification technique which, for a collection of possibly conflicting objectives, produces the Pareto front that contains all the optimal trade-offs that are achievable. The designer then selects a specific Pareto point based on the resource constraints and desired performance level, and a correct-by-construction schedule that meets those constraints is automatically generated. We demonstrate the efficacy of this framework on several robotic scenarios in both simulations and experiments with encouraging results. | Resource-Performance Trade-off Analysis for Mobile Robot Design | 8,788 |
Uncertainty is a major difficulty in endowing robots with autonomy. Robots often fail due to unexpected events. In robot contact tasks are often design to empirically look for force thresholds to define state transitions in a Markov chain or finite state machines. Such design is prone to failure in unstructured environments, when due to external disturbances or erroneous models, such thresholds are met, and lead to state transitions that are false-positives. The focus of this paper is to perform high-level state estimation of robot behaviors and task output for robot contact tasks. Our approach encodes raw low-level 3D cartesian trajectories and converts them into a high level (HL) action grammars. Cartesian trajectories can be segmented and encoded in a way that their dynamic properties, or "texture" are preserved. Once an action grammar is generated, a classifier is trained to detect current behaviors and ultimately the task output. The system executed HL state estimation for task output verification with an accuracy of 86%, and behavior monitoring with an average accuracy of: 72%. The significance of the work is the transformation of difficult-to-use raw low-level data to HL data that enables robust behavior and task monitoring. Monitoring is useful for failure correction or other deliberation in high-level planning, programming by demonstration, and human-robot interaction to name a few. | Robot Contact Task State Estimation via Action Grammars | 8,789 |
Robotic failure is all too common in unstructured robot tasks. Despite well designed controllers, robots often fail due to unexpected events. How do robots measure unexpected events? Many do not. Most robots are driven by the senseplan- act paradigm, however more recently robots are working with a sense-plan-act-verify paradigm. In this work we present a principled methodology to bootstrap robot introspection for contact tasks. In effect, we are trying to answer the question, what did the robot do? To this end, we hypothesize that all noisy wrench data inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is generated by meaningfully segmenting the data and then encoding it. When the wrench information represents a sequence of sub-tasks, we can think of the vocabulary forming sets of words or sentences, such that each subtask is uniquely represented by a word set. Such sets can be classified using statistical or machine learning techniques. We use SVMs and Mondrian Forests to classify contacts tasks both in simulation and in real robots for one and dual arm scenarios showing the general robustness of the approach. The contribution of our work is the presentation of a simple but generalizable semantic scheme that enables a robot to understand its high level state. This verification mechanism can provide feedback for high-level planners or reasoning systems that use semantic descriptors as well. The code, data, and other supporting documentation can be found at: http://www.juanrojas.net/2017icra_wrench_introspection. | Robot Introspection via Wrench-based Action Grammars | 8,790 |
This paper considers the problem of safe mission planning of dynamic systems operating under uncertain environments. Much of the prior work on achieving robust and safe control requires solving second-order cone programs (SOCP). Unfortunately, existing general purpose SOCP methods are often infeasible for real-time robotic tasks due to high memory and computational requirements imposed by existing general optimization methods. The key contribution of this paper is a fast and memory-efficient algorithm for SOCP that would enable robust and safe mission planning on-board robots in real-time. Our algorithm does not have any external dependency, can efficiently utilize warm start provided in safe planning settings, and in fact leads to significant speed up over standard optimization packages (like SDPT3) for even standard SOCP problems. For example, for a standard quadrotor problem, our method leads to speedup of 1000x over SDPT3 without any deterioration in the solution quality. Our method is based on two insights: a) SOCPs can be interpreted as optimizing a function over a polytope with infinite sides, b) a linear function can be efficiently optimized over this polytope. We combine the above observations with a novel utilization of Wolfe's algorithm to obtain an efficient optimization method that can be easily implemented on small embedded devices. In addition to the above mentioned algorithm, we also design a two-level sensing method based on Gaussian Process for complex obstacles with non-linear boundaries such as a cylinder. | Fast Second-order Cone Programming for Safe Mission Planning | 8,791 |
In grasp detection, the robot estimates the position and orientation of potential grasp configurations directly from sensor data. This paper explores the relationship between viewpoint and grasp detection performance. Specifically, we consider the scenario where the approximate position and orientation of a desired grasp is known in advance and we want to select a viewpoint that will enable a grasp detection algorithm to localize it more precisely and with higher confidence. Our main findings are that the right viewpoint can dramatically increase the number of detected grasps and the classification accuracy of the top-n detections. We use this insight to create a viewpoint selection algorithm and compare it against a random viewpoint selection strategy and a strategy that views the desired grasp head-on. We find that the head-on strategy and our proposed viewpoint selection strategy can improve grasp success rates on a real robot by 8% and 4%, respectively. Moreover, we find that the combination of the two methods can improve grasp success rates by as much as 12%. | Viewpoint Selection for Grasp Detection | 8,792 |
Many people with motor disabilities are unable to complete activities of daily living (ADLs) without assistance. This paper describes a complete robotic system developed to provide mobile grasping assistance for ADLs. The system is comprised of a robot arm from a Rethink Robotics Baxter robot mounted to an assistive mobility device, a control system for that arm, and a user interface with a variety of access methods for selecting desired objects. The system uses grasp detection to allow previously unseen objects to be picked up by the system. The grasp detection algorithms also allow for objects to be grasped in cluttered environments. We evaluate our system in a number of experiments on a large variety of objects. Overall, we achieve an object selection success rate of 88% and a grasp detection success rate of 90% in a non-mobile scenario, and success rates of 89% and 72% in a mobile scenario. | Open World Assistive Grasping Using Laser Selection | 8,793 |
Finding the Time-Optimal Parameterization of a Path (TOPP) subject to second-order constraints (e.g. acceleration, torque, contact stability, etc.) is an important and well-studied problem in robotics. In comparison, TOPP subject to third-order constraints (e.g. jerk, torque rate, etc.) has received far less attention and remains largely open. In this paper, we investigate the structure of the TOPP problem with third-order constraints. In particular, we identify two major difficulties: (i) how to smoothly connect optimal profiles, and (ii) how to address singularities, which stop profile integration prematurely. We propose a new algorithm, TOPP3, which addresses these two difficulties and thereby constitutes an important milestone towards an efficient computational solution to TOPP with third-order constraints. | On the Structure of the Time-Optimal Path Parameterization Problem with
Third-Order Constraints | 8,794 |
This paper presents a tool for addressing a key component in many algorithms for planning robot trajectories under uncertainty: evaluation of the safety of a robot whose actions are governed by a closed-loop feedback policy near a nominal planned trajectory. We describe an adaptive importance sampling Monte Carlo framework that enables the evaluation of a given control policy for satisfaction of a probabilistic collision avoidance constraint which also provides an associated certificate of accuracy (in the form of a confidence interval). In particular this adaptive technique is well-suited to addressing the complexities of rigid-body collision checking applied to non-linear robot dynamics. As a Monte Carlo method it is amenable to parallelization for computational tractability, and is generally applicable to a wide gamut of simulatable systems, including alternative noise models. Numerical experiments demonstrating the effectiveness of the adaptive importance sampling procedure are presented and discussed. | Evaluating Trajectory Collision Probability through Adaptive Importance
Sampling for Safe Motion Planning | 8,795 |
We have been developing a paradigm, which we refer to as Learning-from-observation, for a robot to automatically acquire what-to-do through observation of human performance. Since a simple mimicking method to repeat exact joint angles does not work due to the kinematic and dynamic difference between a human and a robot, the method introduces an intermediate symbolic representation, task models, to conceptually represent what-to-do through observation. Then, these task models are mapped appropriate robot motions depending on each robot hardware. This paper presents task models, designed based on the Labanotation, for upper body movements of humanoid robots. Given a human motion sequence, we first analyze the motions of the upper body, and extract certain fixed poses at certain key frames. These key poses are translated into states represented by Labanotation symbols. Then, task models, identified from the state transitions, are mapped to robot movements on a particular robot hardware. Since the task models based on Labanotation are independent from different robot hardware, we can share the same observation module; we only need task mapping modules depending on different robot hardware. The system was implemented and demonstrated that three different robots can automatically mimic human upper body motions with satisfactory level of resemblance. | Describing upper body motions based on the Labanotation for
learning-from-observation robots | 8,796 |
We study simultaneous localization and mapping with a device that uses reflections to measure its distance from walls. Such a device can be realized acoustically with a synchronized collocated source and receiver; it behaves like a bat with no capacity for directional hearing or vocalizing. In this paper we generalize our previous work in 2D, and show that the 3D case is not just a simple extension, but rather a fundamentally different inverse problem. While generically the 2D problem has a unique solution, in 3D uniqueness is always absent in rooms with fewer than nine walls. In addition to the complete characterization of ambiguities which arise due to this non-uniqueness, we propose a robust solution for inexact measurements similar to analogous results for Euclidean Distance Matrices. Our theoretical results have important consequences for the design of collocated range-only SLAM systems, and we support them with an array of computer experiments. | Omnidirectional Bats, Point-to-Plane Distances, and the Price of
Uniqueness | 8,797 |
The generalized label correcting method is an efficient search-based approach to trajectory optimization. It relies on a finite set of control primitives that are concatenated into candidate control signals. This paper investigates the principled selection of this set of control primitives. Emphasis is placed on a particularly challenging input space geometry, the $n$-dimensional sphere. We propose using controls which minimize a generalized energy function and discuss the optimization technique used to obtain these control primitives. A numerical experiment is presented showing a factor of two improvement in running time when using the optimized control primitives over a random sampling strategy. | Selection of Input Primitives for the Generalized Label Correcting
Method | 8,798 |
How does one obtain an admissible heuristic for a kinodynamic motion planning problem? This paper develops the analytical tools and techniques to answer this question. A sufficient condition for the admissibility of a heuristic is presented which can be checked directly from the problem data. This condition is also used to formulate a concave program to optimize an admissible heuristic. This optimization is then approximated and solved in polynomial time using sum-of-squares programming techniques. A number of examples are provided to demonstrate these concepts. | Design of Admissible Heuristics for Kinodynamic Motion Planning via
Sum-of-Squares Programming | 8,799 |
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