State estimation for deformable objects by point registration and dynamic simulation

Tian, Tao; Yi, Fan; Lin, Hsien-Chung; Tomizuka, Masayoshi

doi:10.1109/iros.2017.8206058

Cited by 33 publications

(38 citation statements)

References 17 publications

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“…Furthermore, it has been demonstrated that by increasing the resolution and FPS of the depth camera, while reducing the computational load of the simulation we can reduce the mean linear error to 0.702 cm while increasing the speed of various parts of the cable to be in excess of 1 m/s. It is also worth noting that while several other works [6,23] use a detailed simulated model as part of their method, we use a simulation here purely for experimental purposes and our model as it stands requires little a priori information of the object to be tracked before it can be used.…”

Section: Discussionmentioning

confidence: 99%

“…More recently, however, there have been several attempts to bridge the gap between providing realtime support alongside structural information. Schulman et al [6] and Tang et al [23,24] both use an approach that probabilistically associates a pointcloud with a dynamic simulation of the deformable object being tracked. The main limitation of the proposed method by Schulamn et al is ambiguity of the proposed tracking system in escaping from local minima in the optimisation [6].…”

Section: Related Workmentioning

confidence: 99%

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Tracking linear deformable objects using slicing method

Rastegarpanah

Howard

2021

Robotica

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In this paper, an efficient novel method for tracking the linear deformable objects (LDOs) in real time is proposed. The method is developed based on recursively slicing a pointcloud into smaller pointclouds with sufficiently small variance. The performance of this method is investigated through a series of experiments with various camera resolutions in simulation when a robot end effector tracking an LDO using an RGBD camera, and in real word when the camera tracks a rope during a swing. The performance of the proposed method is compared with another state-of-the-art technique and the outcome is reported here.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Tracking linear deformable objects using slicing method

Rastegarpanah

Howard

2021

Robotica

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“…Morita et al (2003) described rope state topologically by listing intersections created by rope crossings. Methods that more completely describe location along the entire length of the DLO often use non-rigid registration techniques to track the rope from a known initial state (Chi and Berenson, 2019;Tang et al, 2017). Alternatively, an initial state estimate from a given point cloud can be refined to better align with the system dynamics (Javdani et al, 2011;Lui and Saxena, 2013).…”

Section: Cable/rope Manipulationmentioning

confidence: 99%

Cable manipulation with a tactile-reactive gripper

She

Wang

Dong³

et al. 2021

The International Journal of Robotics Research

128

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Cables are complex, high-dimensional, and dynamic objects. Standard approaches to manipulate them often rely on conservative strategies that involve long series of very slow and incremental deformations, or various mechanical fixtures such as clamps, pins, or rings. We are interested in manipulating freely moving cables, in real time, with a pair of robotic grippers, and with no added mechanical constraints. The main contribution of this paper is a perception and control framework that moves in that direction, and uses real-time tactile feedback to accomplish the task of following a dangling cable. The approach relies on a vision-based tactile sensor, GelSight, that estimates the pose of the cable in the grip, and the friction forces during cable sliding. We achieve the behavior by combining two tactile-based controllers: (1) cable grip controller, where a PD controller combined with a leaky integrator regulates the gripping force to maintain the frictional sliding forces close to a suitable value; and (2) cable pose controller, where an linear–quadratic regulator controller based on a learned linear model of the cable sliding dynamics keeps the cable centered and aligned on the fingertips to prevent the cable from falling from the grip. This behavior is possible with the use of reactive gripper fitted with GelSight-based high-resolution tactile sensors. The robot can follow 1 m of cable in random configurations within two to three hand regrasps, adapting to cables of different materials and thicknesses. We demonstrate a robot grasping a headphone cable, sliding the fingers to the jack connector, and inserting it. To the best of the authors’ knowledge, this is the first implementation of real-time cable following without the aid of mechanical fixtures. Videos are available at http://gelsight.csail.mit.edu/cable/

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“…[11] and [12] extended this work by considering the constraints imposed by robot's joints, obstacles in the space, and object's surface normal. In [13], the CPD algorithm is proposed to track deformable object in real time by registering the state estimation to the measured point cloud. A uniform framework for state estimation, task planning and trajectory planning for deformable object manipulation was also presented in [2] by utilizing CPD registration.…”

Section: Related Workmentioning

confidence: 99%

“…Two Kinect Model 1517 cameras are placed in different directions to collect visual data, transmitting 640×480 RGB-D image at 10Hz to Ubuntu PC, which runs a ROS [18] node. Our vision algorithm [13] abstracts the object as a set of nodes, and returns an array of Cartesian coordinates of all nodes at a slightly lower rate depending on the size of the object. The processed information, in the form of published ROS topic, is sent to Host PC, which runs the algorithm discussed above and generates the trajectory.…”

Section: A Experiments Platform Architecturementioning

confidence: 99%

Robotic manipulation of deformable objects by tangent space mapping and non-rigid registration

Tian

Liu

Chen

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Point set registration is a powerful method that enables robots to manipulate deformable objects. By mapping the point cloud of the current object to the pre-trained point cloud, a transformation function can be constructed. The manipulator's trajectory for pre-trained shapes can be warped with this transformation function, yielding a feasible trajectory for the new shape. However, usually this transformation function regards objects as discrete points, and dismisses the topological structures. Therefore, it risks over-stretching or overcompression during manipulation. To tackle this problem, this paper proposes a tangent space point set registration method. A tangent space representation of an object is constructed by defining an angle for each node on the object. Point set registration algorithm runs in this newly-constructed tangent space, yielding a tangent space trajectory. The trajectory is then converted back to Cartesian space and carried out by the robot. Compared to its counterpart in Cartesian space, tangent space point set registration is safer and more robust, succeeding in a series of experiments such as rope straightening, rope knotting, cloth folding and unfolding.

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State estimation for deformable objects by point registration and dynamic simulation

Cited by 33 publications

References 17 publications

Tracking linear deformable objects using slicing method

Tracking linear deformable objects using slicing method

Cable manipulation with a tactile-reactive gripper

Robotic manipulation of deformable objects by tangent space mapping and non-rigid registration

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