Bimanual robotic cloth manipulation for laundry folding

Bersch, Christian; Pitzer, Benjamin; Kammel, Sören

doi:10.1109/iros.2011.6095109

Cited by 47 publications

(24 citation statements)

References 12 publications

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“…3). Grasping crumpled cloth at a desired grasping point has been attempted many times in literature by localizing corners or edges [9,14] or more specific parts [15][16][17]. In contrast, starting from a folded configuration was rarely considered, despite it being a common cloth state in domestic environments.…”

Section: The Benchmarksmentioning

confidence: 99%

Benchmarking Bimanual Cloth Manipulation

Garcia-Camacho

Alenyà

Kragić

et al. 2020

IEEE Robot. Autom. Lett.

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Cloth manipulation is a challenging task that, despite its importance, has received relatively little attention compared to rigid object manipulation. In this paper, we provide three benchmarks for evaluation and comparison of different approaches towards three basic tasks in cloth manipulation: spreading a tablecloth over a table, folding a towel, and dressing. The tasks can be executed on any bimanual robotic platform and the objects involved in the tasks are standardized and easy to acquire. We provide several complexity levels for each task, and describe the quality measures to evaluate task execution. Furthermore, we provide baseline solutions for all the tasks and evaluate them according to the proposed metrics.

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Section: The Benchmarksmentioning

confidence: 99%

Benchmarking Bimanual Cloth Manipulation

Garcia-Camacho

Alenyà

Kragić

et al. 2020

IEEE Robot. Autom. Lett.

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“…First, we assume that the robot is able to grasp and re-grasp the cloth, which we achieve during experiments through human intervention and special tools. We do not explore dexterous manipulation and grasping of cloth by the robot, as this problem has its own challenges and is being addressed by others (Fahantidis et al 1997;Osawa et al 2007;Maitin-Shepard et al 2010;Miller et al 2012;Bersch et al 2011;Berenson 2013). Second, we assume that the participant receiving assistance is collaborative.…”

Section: Assumptionsmentioning

confidence: 99%

Personalized collaborative plans for robot-assisted dressing via optimization and simulation

et al. 2019

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Robots could be a valuable tool for helping with dressing but determining how a robot and a person with disabilities can collaborate to complete the task is challenging. We present task optimization of robot-assisted dressing (TOORAD), a method for generating a plan that consists of actions for both the robot and the person. TOORAD uses a multilevel optimization framework with heterogeneous simulations. The simulations model the physical interactions between the garment and the person being dressed, as well as the geometry and kinematics of the robot, human, and environment. Notably, the models for the human are personalized for an individual's geometry and physical capabilities. TOORAD searches over a constrained action space that interleaves the motions of the person and the robot with the person remaining still when the robot moves and vice versa. In order to adapt to real-world variation, TOORAD incorporates a measure of robot dexterity in its optimization, and the robot senses the person's body with a capacitive sensor to adapt its planned end effector trajectories. To evaluate TOORAD and gain insight into robot-assisted dressing, we conducted a study with six participants with physical disabilities who have difficulty dressing themselves. In the first session, we created models of the participants and surveyed their needs, capabilities, and views on robot-assisted dressing. TOORAD then found personalized plans and generated instructional visualizations for four of the participants, who returned for a second session during which they successfully put on both sleeves of a hospital gown with assistance from the robot. Overall, our work demonstrates the feasibility of generating personalized plans for robot-assisted dressing via optimization and physics-based simulation.

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“…Previous work has also proposed force-based methods for tying knots using hand-engineered manipulation strategies [20]. Cloth manipulation, including laundry folding, has similarly been addressed largely by task-specific methods, often focusing on accurate perception [21], [22] and grasp planning [23]. Unlike these methods, our approach is not specific to rope or cloth, and learns the manipulation strategy entirely from demonstrations.…”

Section: Related Workmentioning

confidence: 99%

Learning force-based manipulation of deformable objects from multiple demonstrations

Lee

Gupta

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

111

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Manipulation of deformable objects often requires a robot to apply specific forces to bring the object into the desired configuration. For instance, tightening a knot requires pulling on the ends, flattening an article of clothing requires smoothing out wrinkles, and erasing a whiteboard requires applying downward pressure. We present a method for learning force-based manipulation skills from demonstrations. Our approach uses non-rigid registration to compute a warping function that transforms both the end-effector poses and forces in each demonstration into the current scene, based on the configuration of the object. Our method then uses the variation between the demonstrations to extract a single trajectory, along with time-varying feedback gains that determine how much to match poses or forces. This results in a learned variableimpedance control strategy that trades off force and position errors, providing for the right level of compliance that applies the necessary forces at each stage of the motion. We evaluate our approach by tying knots in rope, flattening towels, and erasing a whiteboard.

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Bimanual robotic cloth manipulation for laundry folding

Cited by 47 publications

References 12 publications

Benchmarking Bimanual Cloth Manipulation

Benchmarking Bimanual Cloth Manipulation

Personalized collaborative plans for robot-assisted dressing via optimization and simulation

Learning force-based manipulation of deformable objects from multiple demonstrations

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