TechTalks from event: Technical session talks from ICRA 2012

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Intelligent Manipulation Grasping

  • A Generalized Framework for Opening Doors and Drawers in Kitchen Environments Authors: Ruehr, Thomas; Sturm, Jürgen; Pangercic, Dejan; Beetz, Michael; Cremers, Daniel
    In this paper, we present a generalized framework for robustly operating previously unknown cabinets in kitchen environments. Our framework consists of the following four components: (1) a module for detecting both Lambertian and non-Lambertian (i.e. specular) handles, (2) a module for opening and closing novel cabinets using impedance control and for learning their kinematic models, (3) a module for storing and retrieving information about these objects in the map, and (4) a module for reliably operating cabinets of which the kinematic model is known. The presented work is the result of a collaboration of three PR2 beta sites. We rigorously evaluated our approach on 29 cabinets in five real kitchens located at our institutions. These kitchens contained 13 drawers, 12 doors, 2 refrigerators and 2 dishwashers. We evaluated the overall performance of detecting the handle of a novel cabinet, operating it and storing its model in a semantic map. We found that our approach was successful in 51.9% of all 104 trials. With this work, we contribute a well-tested building block of open-source software for future robotic service applications.
  • FCL: A General Purpose Library for Collision and Proximity Queries Authors: Pan, Jia; Chitta, Sachin; Manocha, Dinesh
    We present a new collision and proximity library that integrates several techniques for fast and accurate collision checking and proximity computation. Our library is based on hierarchical representations and designed to perform multiple proximity queries on different model representations. The set of queries includes discrete collision detection, continuous collision detection, separation distance computation and penetration depth estimation. The input models may correspond to triangulated rigid or deformable models and articulated models. Moreover, FCL can perform probabilistic collision checking between noisy point clouds that are captured using cameras or LIDAR sensors. The main benefit of FCL lies in the fact that it provides a unified interface that can be used by various applications. Furthermore, its flexible architecture makes it easier to implement new algorithms within this framework. The runtime performance of the library is comparable to state of the art collision and proximity algorithms. We demonstrate its performance on synthetic datasets as well as motion planning and grasping computations performed using a two-armed mobile manipulation robot.
  • Learning Organizational Principles in Human Environments Authors: Schuster, Martin Johannes; Jain, Dominik; Tenorth, Moritz; Beetz, Michael
    In the context of robotic assistants in human everyday environments, pick and place tasks are beginning to be competently solved at the technical level. The question of where to place objects or where to pick them up from, among other higher-level reasoning tasks, is therefore gaining practical relevance. In this work, we consider the problem of identifying the organizational structure within an environment, i.e. the problem of determining organizational principles that would allow a robot to infer where to best place a particular, previously unseen object or where to reasonably search for a particular type of object given past observations about the allocation of objects to locations in the environment. This problem can be reasonably formulated as a classification task. We claim that organizational principles are governed by the notion of similarity and provide an empirical analysis of the importance of various features in datasets describing the organizational structure of kitchens. For the aforementioned classification tasks, we compare standard classification methods, reaching average accuracies of at least 79% in all scenarios. We thereby show that ontology-based similarity measures are well-suited as highly discriminative features. We demonstrate the use of learned models of organizational principles in a kitchen environment on a real robot system, where the robot identifies a newly acquired item, determines a suitable location and then stores the item accordingly.
  • Using Manipulation Primitives for Brick Sorting in Clutter Authors: Gupta, Megha; Sukhatme, Gaurav
    This paper explores the idea of manipulation-aided perception and grasping in the context of sorting small objects on a tabletop. We present a robust pipeline that combines perception and manipulation to accurately sort Duplo bricks by color and size. The pipeline uses two simple motion primitives to manipulate the scene in ways that help the robot to improve its perception. This results in the ability to sort cluttered piles of Duplo bricks accurately. We present experimental results on the PR2 robot comparing brick sorting without the aid of manipulation to sorting with manipulation primitives that show the benefits of the latter, particularly as the degree of clutter in the environment increases.
  • A constraint-based programming approach to physical human-robot Interaction Authors: Borghesan, Gianni; Willaert, Bert; De Schutter, Joris
    Abstract— This work aims to extend the constraint-based formalism iTaSC for scenarios where physical human-robot interaction plays a central role, which is the case for e.g. surgical robotics, rehabilitation robotics and household robotics. To really exploit the potential of robots in these scenarios, it should be possible to enforce force and geometrical constraints in an easy and flexible way. iTaSC allows to express such constraints in different frames expressed in arbitrary spaces and to obtain control setpoints in a systematic way. In previous implementations of iTaSC, industrial velocity-controlled robots were considered. This work presents an extension of the iTaSC-framework that allows to take advantage of the back-drivability of a robot thus avoiding the use of force sensors. Then, as a case-study, the iTaSC-framework is used to formulate a (position-position) teleoperation scheme. The theoretical findings are experimentally validated using a PR2 robot.

Formal Methods

  • Temporal Logic Motion Control Using Actor-Critic Methods Authors: Ding, Xu Chu; Wang, Jing; Lahijanian, Morteza; Paschalidis, Yannis; Belta, Calin
    In this paper, we consider the problem of deploying a robot from a specification given as a temporal logic statement about some properties satisfied by the regions of a large, partitioned environment. We assume that the robot has noisy sensors and actuators and model its motion through the regions of the environment as a Markov Decision Process (MDP). The robot control problem becomes finding the control policy maximizing the probability of satisfying the temporal logic task on the MDP. For a large environment, obtaining transition probabilities for each state-action pair, as well as solving the necessary optimization problem for the optimal policy are usually not computationally feasible. To address these issues, we propose an approximate dynamic programming framework based on a least-square temporal difference learning method of the actor-critic type. This framework operates on sample paths of the robot and optimizes a randomized control policy with respect to a small set of parameters. The transition probabilities are obtained only when needed. Hardware-in-the-loop simulations confirm that convergence of the parameters translates to an approximately optimal policy.
  • Robust Multi-Robot Optimal Path Planning with Temporal Logic Constraints Authors: Ulusoy, Alphan; Smith, Stephen L.; Ding, Xu Chu; Belta, Calin
    In this paper we present a method for automatically planning robust optimal paths for a group of robots that satisfy a common high level mission specification. Each robot's motion in the environment is modeled as a weighted transition system, and the mission is given as a Linear Temporal Logic (LTL) formula over a set of propositions satisfied by the regions of the environment. In addition, an optimizing proposition must repeatedly be satisfied. The goal is to minimize the maximum time between satisfying instances of the optimizing proposition while ensuring that the LTL formula is satisfied even with uncertainty in the robots' traveling times. We characterize a class of LTL formulas that are robust to robot timing errors, for which we generate optimal paths if no timing errors are present, and we present bounds on the deviation from the optimal values in the presence of errors. We implement and experimentally evaluate our method considering a persistent monitoring task in a road network environment.
  • Stunt Driving via Policy Search Authors: Lau, Tak Kit; Liu, Yunhui
    To explore or exploit? In this paper, we discuss the long-standing exploration-exploration dilemma in context of designing a learning controller for stunt-style driving with scarce samples. By making an efficient use of a single demonstration by an expert, our algorithm leverages our intuitive understanding of driving to extract a coarse dynamics model from the collected driving data, then formulate the policy search in a setting of gradient update with a specially designed cost function. Both theoretical and empirical results are detailed and discussed.
  • Probabilistic Control from Time-Bounded Temporal Logic Specifications in Dynamic Environments Authors: Medina Ayala, Ana Ivonne; Andersson, Sean; Belta, Calin
    The increasing need for real time robotic systems capable of performing tasks in changing and constrained environments demands the development of reliable and adaptable motion planning and control algorithms. This paper considers a mobile robot whose performance is measured by the completion of temporal logic tasks within a certain period of time. In addition to such time constraints, the planning algorithm must also deal with changes in the robot’s workspace during task execution. In our case, the robot is deployed in a partitioned environment subjected to structural changes in which doors shift from open to closed and vice-versa. The motion of the robot is modeled as a Continuous Time Markov Decision Process and the robot’s mission is expressed as a Continuous Stochastic Logic (CSL) temporal logic specification. An approximate solution to find a control strategy that satisfies such specifications is derived for a subset of probabilistic CSL formulae. Simulation and experimental results are provided to illustrate the method.
  • Non-Gaussian Belief Space Planning: Correctness and Complexity Authors: Platt, Robert; Tedrake, Russ; Kaelbling, Leslie; Lozano-Perez, Tomas
    We consider the partially observable control problem where it is potentially necessary to perform complex information-gathering operations in order to localize state. One approach to solving these problems is to create plans in {em belief-space}, the space of probability distributions over the underlying state of the system. The belief-space plan encodes a strategy for performing a task while gaining information as necessary. Unlike most approaches in the literature which rely upon representing belief state as a Gaussian distribution, we have recently proposed an approach to non-Gaussian belief space planning based on solving a non-linear optimization problem defined in terms of a set of state samples~cite{platt_isrr2011}. In this paper, we show that even though our approach makes optimistic assumptions about the content of future observations for planning purposes, all low-cost plans are guaranteed to gain information in a specific way under certain conditions. We show that eventually, the algorithm is guaranteed to localize the true state of the system and to reach a goal region with high probability. Although the computational complexity of the algorithm is dominated by the number of samples used to define the optimization problem, our convergence guarantee holds with as few as two samples. Moreover, we show empirically that it is unnecessary to use large numbers of samples in order to obtain good performance.
  • Proving the Correctness of Concurrent Robot Software Authors: Kazanzides, Peter; Kouskoulas, Yanni; Deguet, Anton; Shao, Zhong
    Component-based software has been proposed as a methodology for improving software reuse and has increasingly been adopted by robot software developers. At the same time, robot systems typically have real-time performance requirements and performance gains can often be obtained by multi-threading. It is challenging, however, to create correct multi-threaded software, especially when standard mutual exclusion primitives, such as mutexes and semaphores, are eschewed in favor of more efficient, lock-free mechanisms. It is even more difficult to find these errors, as they can remain dormant for years until triggered by just the &quot;right&quot; conditions. Our approach, therefore, is to apply Formal Methods to reason about the correctness of these mechanisms. As a first step, we adopted a recently-developed program logic called History for Local Rely/Guarantee (HLRG) and applied it to prove the correctness (after first finding and fixing an error) of one such mechanism in the open source <i>cisst</i> software package. This strategy is not specific to <i>cisst</i> and can be applied to other packages.

Sensor Networks

  • Distributed Coverage with Mobile Robots on a Graph: Locational Optimization Authors: Yun, Seung-kook; Rus, Daniela
    This paper presents a decentralized algorithm for coverage with mobile robots on a graph. Coverage is an important capability of multi-robot systems engaged in a number of different applications, including placement for environmental modeling, deployment for maximal quality surveillance, and even coordinated construction.We use distributed vertex substitution for locational optimization, and the controllers minimize the corresponding cost function. We prove that the proposed controller with two-hop communication guarantees convergence to the locally optimal configuration. We evaluate the algorithms in simulations and compare them to the coverage algorithm in a continuous domain.
  • An Approach to Multi-Agent Area Protection Using Bayes Risk Authors: Bays, Matthew; Shende, Apoorva; Stilwell, Daniel
    We introduce a novel approach to controlling the motion of a team of agents so that they jointly minimize a cost function utilizing Bayes risk. We use a particle-based approach and approximations that allow us to express the optimization problem as a mixed-integer linear program. We illustrate this approach with an area protection problem in which a team of mobile agents must intercept mobile targets before the targets enter a specified area. Bayes risk is a useful measure of performance for applications where agents must perform a classification task. By minimizing Bayes risk, agents are able to explicitly account for the cost of incorrect classification. In our application, a team of mobile agents must classify potential mobile targets as threat or safe based on the likelihood the targets will enter the specified area. The agents must also maneuver to intercept targets that are classified as threat.
  • On Coordination in Practical Multi-Robot Patrol Authors: Agmon, Noa; Fok, Chien-Liang; Elmaliah, Yehuda; Stone, Peter; Julien, Christine; Vishwanath, Sriram
    Multi-robot patrol is a fundamental application of multi-robot systems. While much theoretical work exists providing an understanding of the optimal patrol strategy for teams of coordinated, homogeneous robots, little work exists on building and evaluating the performance of such systems in the real world. In this paper, we evaluate the performance of multi-robot patrol in a practical outdoor robotic system, and evaluate the effect of different coordination schemes on the performance of the robotic team, which is influenced by their communication capabilities and degree of heterogeneity. We specifically focus on frequency-based multi-robot patrol along a cyclic route specified by a set of GPS-waypoints. The multi-robot patrol algorithms evaluated vary in the level of coordination of the robots: no coordination, loose coordination, and strong coordination. In addition, we evaluate versions of these algorithms that distribute state information---either individual state, or state of the entire team (global state). Our experiments show that while strong coordination was theoretically proven to be optimal, in practice uncoordinated patrol performed better in terms of average waypoint visitation frequency. Furthermore, loosely coordinated patrol that shares only individual state outperformed all other coordination schemes in terms of worst-case frequency, and it performed significantly better than a loosely coordinated algorithm based on sharing global-view state. We respond t
  • Adaptive Sampling Using Mobile Sensor Networks Authors: Huang, Shuo; Tan, Jindong
    This paper presents an adaptive sparse sampling approach and the corresponding real-time scalar field reconstruction method using mobile sensor networks. Traditionally, the sampling methods collect measurements without considering possible distributions of target signals. A feedback driven algorithm is discussed in this paper, where new measurements are determined based on the analysis of existing observations. The information amount of each potential measurement is evaluated under a sparse domain based on compressive sensing framework given all existing information shared among networked mobile sensors, and the most informative one is selected. The efficiency of this information-driven method falls into the information maximization for each individual measurement. The simulation results show the efficacy and efficiency of this approach, where a scalar field is recovered.
  • Coverage Control of Mobile Sensors for Adaptive Search of Unknown Number of Targets Authors: Surana, Amit; Mathew, George; Kannan, Suresh
    We present a multiscale adaptive search algorithm for efficiently searching an unknown number of stationary targets using a team of multiple mobile sensors. We first derive a Spectral Multiscale Coverage (SMC) control law for a Dubins vehicle model. Given a search prior, the SMC control gives rise to uniform coverage dynamics for the mobile sensors such that the amount of time spent observing a region is proportional to finding a target in it. In order to make the search robust to sensor uncertainties and Automatic Target Detection algorithm errors (i.e. false alarm, missed detections), we combine the SMC control with decision and estimation theoretic techniques. As new targets are discovered we use the Sequential Ratio Probability Test and Recursive Least Squares estimation to quantify the current uncertainty in target detection and location, respectively. This uncertainty is used to update the search prior so as to balance exploitation (reduce uncertainty in state of already discovered potential targets) and exploration (discover new targets). We demonstrate this adaptive search methodology in a high fidelity simulation environment and show an improved performance over lawnmower type search.