TechTalks from event: Technical session talks from ICRA 2012

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Multi-Robot Systems II

  • Distributed Value Functions for Multi-Robot Exploration Authors: Matignon, Laetitia; Jeanpierre, Laurent; Mouaddib, Abdel-Illah
    This paper addresses the problem of exploring an unknown area with a team of autonomous robots using decentralized decision making techniques. The localization aspect is not considered and it is assumed the robots share their positions and have access to a map updated with all explored areas. A key problem is then the coordination of decentralized decision processes: each individual robot must choose appropriate exploration goals so that the team simultaneously explores different locations of the environment. We formalize this problem as a Decentralized Markov Decision Process (Dec-MDP) solved as a set of individual MDPs, where interactions between MDPs are considered in a distributed value function. Thus each robot computes locally a strategy that minimizes the interactions between the robots and maximizes the space coverage of the team. Our technique has been implemented and evaluated in real-world and simulated experiments.
  • Steiner Traveler: Relay Deployment for Remote Sensing in Heterogeneous Multi-Robot Exploration Authors: Pei, Yuanteng; Mutka, Matt
    In the multi-robot exploration task of an unknown environment, human operators often need to control the robots remotely and obtain the sensed information by real-time bandwidth-consuming multimedia streams. The task has military and civilian applications, such as reconnaissance, search and rescue missions in earthquake, radioactive, and other dangerous or hostile regions. Due to the nature of such applications, infrastructure networks or pre-deployed relays are often not available to support the stream transmission. To address this issue, we present a novel exploration scheme called Bandwidth aware Exploration with a Steiner Traveler (BEST). BEST has a heterogeneous robot team with a fixed number of frontier nodes(FNs) to sense the area iteratively. In addition, a relay-deployment node (RDN) tracks the FNs movement and places relays when necessary to support the video/audio streams aggregation to the base station. Therefore, the main problem is to find a minimum path for the relay-deployment robot to travel and the positions to deploy necessary relays to support the stream aggregation in each movement iteration. This problem inherits characteristics of both the Steiner minimum tree and traveling salesman problems. We model the novel problem as the minimum velocity Flow constrained Steiner Traveler problem (FST). Extensive simulations show BEST improves exploration efficiency by 62% on average compared to the state-of-the-art homogeneous robot exploration strategies.
  • Minimal Persistence Control on Dynamic Directed Graphs for Multi-Robot Formation Authors: Wang, Hua; Guo, Yi
    Given a multi-robot system, in order to preserve its geometric shape in a formation, the minimal persistence control addresses questions: (1) what pairwise communication connections have to be prescribed to minimize communication channels, and (2) which orientations of communication links are to be placed between robots. In this paper, we propose a minimal persistence control problem on multi-robot systems with underlying graphs, being directed and dynamically switching. We develop distributed algorithms based on the rank of the rigidity matrix and the pebble game method. The feasibility of the proposed methods is validated by simulations on the robotic simulator Webots and experiments on e-puck robot platform.
  • Distributed Formation Control of Unicycle Robots Authors: Sadowska, Anna; Kostic, Dragan; van de Wouw, Nathan; Huijberts, Henri; Nijmeijer, Hendrik
    In this paper, we consider the problem of distributed formation control for a group of unicycle robots. We propose a control algorithm that solves the formation control problem in that it ensures that robots create a desired time– varying formation shape while the formation as a whole follows a prescribed trajectory. Moreover, we show that it is also possible to obtain coordination of robots in the formation, regardless of the trajectory tracking of the formation. We illustrate the behavior of a group of robots controlled by the formation control algorithm proposed in this paper in a simulation study.
  • Multi-Level Formation Roadmaps for Collision-Free Dynamic Shape Changes with Non-holonomic Teams Authors: Krontiris, Athanasios; Louis, Sushil; Bekris, Kostas E.
    Teams of robots can utilize formations to accomplish a task, such as maximizing the observability of an environment while maintaining connectivity. In a cluttered space, however, it might be necessary to automatically change formation to avoid obstacles. This work proposes a path planning approach for non-holonomic robots, where a team dynamically switches formations to reach a goal without collisions. The method introduces a multi-level graph, which can be constructed offline. Each level corresponds to a different formation and edges between levels allow for formation transitions. All edges satisfy curvature bounds and clearance requirements from obstacles. During the online phase, the method returns a path for a virtual leader, as well as the points along the path where the team should switch formations. Individual agents can compute their controls using kinematic formation controllers that operate in curvilinear coordinates. The approach guarantees that it is feasible for the agents to follow the trajectory returned. Simulations show that the online cost of the approach is small. The method returns solutions that maximize the maintenance of a desired formation while allowing the team to rearrange its configuration in the presence of obstacles.
  • An Unscented Model Predictive Control Approach to the Formation Control of Nonholonomic Mobile Robots Authors: Farrokhsiar, Morteza; Najjaran, Homayoun
    Formation control of nonholonomic robots in dynamic unstructured environments is a challenging task yet to be met. This paper presents the unscented model predictive control (UMPC) approach to tackle the formation control of multiple nonholonomic robots in unstructured environments. In unscented predictive control, the uncertainty propagation in the nonholonomic nonlinear motion model is approximated using the unscented transform. The collision avoidance constraints have been introduced as the chance constraints to model predictive control. The UMPC approach enables us to find a closed form of the collision avoidance probabilistic constraints. The desired pose of each robot in the formation is introduced through the local objective function of UMPC of each robot. The simulation results indicate the effective and robust performance of UMPC in unstructured environment in the presence of action disturbance and communication signal noise.

Grasping: Learning and Estimation

  • End-To-End Dexterous Manipulation with Deliberate Interactive Estimation Authors: Hudson, Nicolas; Howard, Tom; Ma, Jeremy; Jain, Abhinandan; Bajracharya, Max; Myint, Steven; Matthies, Larry; Backes, Paul; Hebert, Paul; Fuchs, Thomas; Burdick, Joel
    This paper presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation (ARM) program. The developed autonomy system uses robot, object, and environment models to identify and localize objects, and well as plan and execute required manipulation tasks. Deliberate interaction with objects and the environment increases system knowledge about the combined robot and environmental state, enabling high precision tasks such as key insertion to be performed in a consistent framework. This approach has been demonstrated across a wide range of manipulation tasks, and is the leading manipulation approach in independent DARPA testing.
  • Template-Based Learning of Grasp Selection Authors: Herzog, Alexander; Pastor, Peter; Kalakrishnan, Mrinal; Righetti, Ludovic; Asfour, Tamim; Schaal, Stefan
    The ability to grasp unknown objects is an important skill for personal robots, which has been addressed by many present and past research projects. A crucial aspect of grasping is choosing an appropriate grasp configuration, i.e. the 6d pose of the hand relative to the object and its finger configuration. Finding feasible grasp configurations for novel objects, however, is challenging because of the huge variety in shape and size of these objects and the specific kinematics of the robotic hand in use. In this paper, we introduce a new grasp selection algorithm able to find object grasp poses based on previously demonstrated grasps. Assuming that objects with similar shapes can be grasped in a similar way, we associate to each demonstrated grasp a grasp template. The template is a local shape descriptor for a possible grasp pose and is constructed using 3d information from depth sensors. For each new object to grasp, the algorithm then finds the best grasp candidate in the library of templates. The grasp selection is also able to improve over time using the information of previous grasp attempts to adapt the ranking of the templates. We tested the algorithm on two different platforms, the Willow Garage PR2 and the Barrett WAM arm which have very different hands. Our results show that the algorithm is able to find good grasp configurations for a large set of objects from a relatively small set of demonstrations, and does indeed improve its performance over time.
  • Learning Hardware Agnostic Grasps for a Universal Jamming Gripper Authors: Jiang, Yun; Amend, John; Lipson, Hod; Saxena, Ashutosh
    Grasping has been studied from various perspectives including planning, control, and learning. In this paper, we take a learning approach to predict successful grasps for a universal jamming gripper. A jamming gripper is comprised of a flexible membrane filled with granular material, and it can quickly harden or soften to grip objects of varying shape by modulating the air pressure within the membrane. Although this gripper is easy to control, it is difficult to develop a physical model of its gripping mechanism because it undergoes significant deformation during use. Thus, many grasping approaches based on physical models (such as based on form- and force-closure) would be challenging to apply to a jamming gripper. Here we instead use a supervised learning algorithm and design both visual and shape features for capturing the property of good grasps. We show that given a RGB image and a point cloud of the target object, our algorithm can predict successful grasps for the jamming gripper without requiring a physical model. It can therefore be applied to both a parallel plate gripper and a jamming gripper without modification. We demonstrate that our learning algorithm enables both grippers to pick up a wide variety of objects, and through robotic experiments we are able to define the type of objects each gripper is best suited for handling.
  • Learning Grasp Stability Authors: Dang, Hao; Allen, Peter
    We deal with the problem of blind grasping where we use tactile feedback to predict the stability of a robotic grasp given no visual or geometric information about the object being grasped. We first simulated tactile feedback using a soft finger contact model in GraspIt! and computed tactile contacts of thousands of grasps with a robotic hand using the Columbia Grasp Database. We used the K-means clustering method to learn a contact dictionary from the tactile contacts, which is a codebook that models the contact space. The feature vector for a grasp is a histogram computed based on the distribution of its contacts over the contact space defined by the dictionary. An SVM is then trained to predict the stability of a robotic grasp given this feature vector. Experiments indicate that this model which requires low-dimension feature input is useful in predicting the stability of a grasp.
  • Learning to Slide a Magnetic Card through a Card Reader Authors: Sukhoy, Vladimir; Georgiev, Veselin; Wegter, Todd; Sweidan, Ramy; Stoytchev, Alexander
    This paper describes a set of experiments in which an upper-torso humanoid robot learned to slide a card through a card reader. The small size and the flexibility of the card presented a number of manipulation challenges for the robot. First, because most of the card is occluded by the card reader and the robot's hand during the sliding process, visual feedback is useless for this task. Second, because the card bends easily, it is difficult to distinguish between bending and hitting an obstacle in order to correct the sliding trajectory. To solve these manipulation challenges this paper proposes a method for constraint detection that uses only proprioceptive data. The method uses dynamic joint torque thresholds that are calibrated using the robot's movements in free space. The experimental results show that using this method the robot can detect when the movement of the card is constrained and modify the sliding trajectory in real time, which makes solving this task possible.

Grasping and Manipulation

  • Movement-Aware Action Control - Integrating Symbolic and Control-Theoretic Action Execution Authors: Kresse, Ingo; Beetz, Michael
    In this paper we propose a bridge between a symbolic reasoning system and a task function based controller. We suggest to use modular position- and force constraints, which are represented as action-object-object triples on the symbolic side and as task function parameters on the controller side. This description is a considerably more fine-grained interface than what has been seen in high-level robot control systems before. It can preserve the 'null space' of the task and make it available to the control level. We demonstrate how a symbolic description can be translated to a control-level description that is executable on the robot. We describe the relation to existing robot knowledge bases and indicate information sources for generating constraints on the symbolic side. On the control side we then show how our approach outperforms a traditional controller, by exploiting the task's null space, leading to a significantly extended work space.
  • Physically-Based Grasp Quality Evaluation under Uncertainty Authors: Kim, Junggon; Pollard, Nancy S
    In this paper new grasp quality measures considering both object dynamics and pose uncertainty are proposed. Dynamics of the object is incorporated into our grasping simulation to capture the change of its pose and contact points during grasping. Pose uncertainty is considered by running multiple simulations starting from slightly different initial poses sampled from a probability distribution model. A simple robotic grasping strategy is simulated and the quality score of the resulting grasp is evaluated from the simulation result. The effectiveness of the new quality measures on predicting the actual grasp success rate is shown through a real robot experiment.
  • Bimanual Regrasping from Unimanual Machine Learning Authors: Balaguer, Benjamin; Carpin, Stefano
    While unimanual regrasping has been studied extensively, either by regrasping in-hand or by placing the object on a surface, bimanual regrasping has seen little attention. The recent popularity of simple end-effectors and dual-manipulator platforms makes bimanual regrasping an important behavior for service robots to possess. We solve the challenge of bimanual regrasping by casting it as an optimization problem, where the objective is to minimize execution time. The optimization problem is supplemented by image processing and a unimanual grasping algorithm based on machine learning that jointly identify two good grasping points on the object and the proper orientations for each end-effector. The optimization algorithm exploits this data by finding the proper regrasp location and orientation to minimize execution time. Influenced by human bimanual manipulation, the algorithm only requires a single stereo image as input. The efficacy of the method we propose is demonstrated on a dual manipulator torso equipped with Barrett WAM arms and Barrett Hands.
  • Planar, Bimanual, Whole-Arm Grasping Authors: Seo, Jungwon; Kim, Soonkyum; Kumar, Vijay
    We address the problem of synthesizing planar, bimanual, whole-arm grasps by developing the abstraction of an open chain gripper, an open, planar chain of rigid links and revolute joints contacting a planar, polygonal object, and introducing the concept of a generalized contact. Since two generalized contacts suffice for planar grasps, we leverage previous work on caging and immobilization for two contact grasps to construct an algorithm which synthesizes contact configurations for stable grasping. Simulations show that our methodology can be applied to grasp a wide range of planar objects without relying on special-purpose end-effectors. Representative experiments with the PR2 humanoid robot illustrate that this approach is practical.
  • Identification of Contact Formations: Resolving Ambiguous Force Torque Information Authors: Hertkorn, Katharina; Preusche, Carsten; Roa, Maximo A.
    This paper presents the identification of contact formations using force torque information. As force torque measurements do not map uniquely to their corresponding contact formations, three steps are performed: A contact formation graph is augmented with a similarity index that reflects the similarity of contact formations with respect to their spanned wrench spaces. Prior to that, the wrench space for each contact formation is computed automatically. A particle filter is used to represent the likeliness of a contact formation given a force torque measurement. Finally, this probability distribution is resolved taking the similarity index, the transitions of the contact formation graph and the history of identified contact formations into account. This allows to recognize the order of demonstrated contact formations by a measured set of forces and torques. The approach is verified by experiments.