| 1. |
- Almeida, Diogo, 1991-, et al.
(författare)
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Team KTH’s Picking Solution for the Amazon Picking Challenge 2016
- 2017
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Ingår i: Warehouse Picking Automation Workshop 2017.
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Konferensbidrag (populärvet., debatt m.m.)abstract
- In this work we summarize the solution developed by Team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition simulated a warehouse automation scenario and it was divided in two tasks: a picking task where a robot picks items from a shelf and places them in a tote and a stowing task which is the inverse task where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting from a high level overview of our system and later delving into details of our perception pipeline and our strategy for manipulation and grasping. The solution was implemented using a Baxter robot equipped with additional sensors.
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| 2. |
- Almeida, Diogo, 1991-, et al.
(författare)
-
Team KTH’s Picking Solution for the Amazon Picking Challenge 2016
- 2020
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Ingår i: Advances on Robotic Item Picking: Applications in Warehousing and E-Commerce Fulfillment. - Cham : Springer Nature. ; , s. 53-62
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this chapter we summarize the solution developed by team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition, which simulated a warehouse automation scenario, was divided into two parts: a picking task, where the robot picks items from a shelf and places them into a tote, and a stowing task, where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting with a high-level overview of the system, delving later into the details of our perception pipeline and strategy for manipulation and grasping. The hardware platform used in our solution consists of a Baxter robot equipped with multiple vision sensors.
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| 3. |
- Hang, Kaiyu, 1987-, et al.
(författare)
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Team CVAP’s Mobile Picking System at the Amazon Picking Challenge 2015
- 2020
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Ingår i: Advances on Robotic Item Picking. - Cham : Springer Nature. ; , s. 1-12
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this paper we present the system we developed for the Amazon Picking Challenge 2015, and discuss some of the lessons learned that may prove useful to researchers and future teams developing autonomous robot picking systems. For the competition we used a PR2 robot, which is a dual arm robot research platform equipped with a mobile base and a variety of 2D and 3D sensors. We adopted a behavior tree to model the overall task execution, where we coordinate the different perception, localization, navigation, and manipulation activities of the system in a modular fashion. Our perception system detects and localizes the target objects in the shelf and it consisted of two components: one for detecting textured rigid objects using the SimTrack vision system, and one for detecting non-textured or nonrigid objects using RGBD features. In addition, we designed a set of grasping strategies to enable the robot to reach and grasp objects inside the confined volume of shelf bins. The competition was a unique opportunity to integrate the work of various researchers at the Robotics, Perception and Learning laboratory (formerly the Computer Vision and Active Perception Laboratory, CVAP) of KTH, and it tested the performance of our robotic system and defined the future direction of our research.
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| 4. |
- Viña Barrientos, Francisco, 1990-, et al.
(författare)
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Adaptive control for pivoting with visual and tactile feedback
- 2016
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - : Institute of Electrical and Electronics Engineers (IEEE). - 1050-4729. - 9781467380263 ; 2016-June, s. 399-406
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Konferensbidrag (refereegranskat)abstract
- In this work we present an adaptive control approach for pivoting, which is an in-hand manipulation maneuver that consists of rotating a grasped object to a desired orientation relative to the robot's hand. We perform pivoting by means of gravity, allowing the object to rotate between the fingers of a one degree of freedom gripper and controlling the gripping force to ensure that the object follows a reference trajectory and arrives at the desired angular position. We use a visual pose estimation system to track the pose of the object and force measurements from tactile sensors to control the gripping force. The adaptive controller employs an update law that accommodates for errors in the friction coefficient, which is one of the most common sources of uncertainty in manipulation. Our experiments confirm that the proposed adaptive controller successfully pivots a grasped object in the presence of uncertainty in the object's friction parameters.
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| 5. |
- Viña Barrientos, Francisco, 1990-
(författare)
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Robotic Manipulation under Uncertainty and Limited Dexterity
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Robotic manipulators today are mostly constrained to perform fixed, repetitive tasks. Engineers design the robot’s workcell specifically tailoredto the task, minimizing all possible uncertainties such as the location of tools and parts that the robot manipulates. However, autonomous robots must be capable of manipulating novel objects with unknown physical properties such as their inertial parameters, friction and shape. In this thesis we address the problem of uncertainty connected to kinematic constraints and friction forces in several robotic manipulation tasks. We design adaptive controllers for opening one degree of freedom mechanisms, such as doors and drawers, under the presence of uncertainty in the kinematic parameters of the system. Furthermore, we formulate adaptive estimators for determining the location of the contact point between a tool grasped by the robot and the environment in manipulation tasks where the robot needs to exert forces with the tool on another object, as in the case of screwing or drilling. We also propose a learning framework based on Gaussian Process regression and dual arm manipulation to estimate the static friction properties of objects. The second problem we address in this thesis is related to the mechanical simplicity of most robotic grippers available in the market. Their lower cost and higher robustness compared to more mechanically advanced hands make them attractive for industrial and research robots. However, the simple mechanical design restrictsthem from performing in-hand manipulation, i.e. repositioning of objects in the robot’s hand, by using the fingers to push, slide and roll the object. Researchers have proposed thus to use extrinsic dexterity instead, i.e. to exploit resources and features of the environment, such as gravity or inertial forces, that can help the robot to perform regrasps. Given that the robot must then interact with the environment, the problem of uncertainty becomes highly relevant. We propose controllers for performing pivoting, i.e. reorienting the grasped object in the robot’s hand, using gravity and controlling the friction exerted by the fingertips by varying the grasping force.
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| 6. |
- Vina, Francisco, 1990-, et al.
(författare)
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In-hand manipulation using gravity and controlled slip
- 2015
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Ingår i: Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on. - : IEEE conference proceedings. ; 2015-December, s. 5636-5641
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Konferensbidrag (refereegranskat)abstract
- In this work we propose a sliding mode controllerfor in-hand manipulation that repositions a tool in the robot’shand by using gravity and controlling the slippage of the tool. In our approach, the robot holds the tool with a pinch graspand we model the system as a link attached to the grippervia a passive revolute joint with friction, i.e., the grasp onlyaffords rotational motions of the tool around a given axis ofrotation. The robot controls the slippage by varying the openingbetween the fingers in order to allow the tool to move tothe desired angular position following a reference trajectory.We show experimentally how the proposed controller achievesconvergence to the desired tool orientation under variations ofthe tool’s inertial parameters.
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| 7. |
- Viña, Francisco, 1990-, et al.
(författare)
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Predicting Slippage and Learning Manipulation Affordances through Gaussian Process Regression
- 2013
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Ingår i: IEEE-RAS International Conference on Humanoid Robots. - : IEEE Computer Society. - 2164-0580 .- 2164-0572. ; , s. 462-468
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Konferensbidrag (refereegranskat)abstract
- Object grasping is commonly followed by some form of object manipulation - either when using the grasped object as a tool or actively changing its position in the hand through in-hand manipulation to afford further interaction. In this process, slippage may occur due to inappropriate contact forces, various types of noise and/or due to the unexpected interaction or collision with the environment. In this paper, we study the problem of identifying continuous bounds on the forces and torques that can be applied on a grasped object before slippage occurs. We model the problem as kinesthetic rather than cutaneous learning given that the measurements originate from a wrist mounted force-torque sensor. Given the continuous output, this regression problem is solved using a Gaussian Process approach. We demonstrate a dual armed humanoid robot that can autonomously learn force and torque bounds and use these to execute actions on objects such as sliding and pushing. We show that the model can be used not only for the detection of maximum allowable forces and torques but also for potentially identifying what types of tasks, denoted as manipulation affordances, a specific grasp configuration allows. The latter can then be used to either avoid specific motions or as a simple step of achieving in-hand manipulation of objects through interaction with the environment.
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