Bed-making is a universal home task that can be challenging for senior citizens due to reaching motions. Automating bed-making has multiple technical challenges such as perception in an unstructured environments, deformable object manipulation, obstacle avoidance and sequential decision making. We explore how DART, an LfD algorithm for learning robust policies, can be applied to automating bed making without fiducial markers with a Toyota Human Support Robot (HSR). By gathering human demonstrations for grasping the sheet and failure detection, we can learn deep neural network policies that leverage pre-trained YOLO features to automate the task. Experiments with a 1 2 scale twin bed and distractors placed on the bed, suggest policies learned on 50 demonstrations with DART achieve 96 sheet coverage, which is over 200 better than a corner detector baseline using contour detection.
Manipulation of cloth has been explored in a variety of contexts including laundry folding and surgical robotics. used an algorithm based on identifying and tensioning corners to enable a home robot to fold laundry @cite_12 . proposed a technique that used 3rd person human demonstrations to learn how to fold a towel in isolation @cite_2 . examined the problem of bringing clothing into an arbitrary position and proposed using a deformable object simulator to plan motions @cite_5 . Shibata proposed to fold a towel without using registration by examining humans performing the action and designing a robust folding strategy @cite_8 , given the known initial position of the towel.
@cite_5 @cite_8 @cite_12 @cite_2
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We consider the problem of autonomous robotic laundry folding, and propose a solution to the perception and manipulation challenges inherent to the task. At the core of our approach is a quasi-static cloth model which allows us to neglect the complex dynamics of cloth under significant parts of the state space, allowing us to reason instead in terms of simple geometry. We present an algorithm which, given a 2D cloth polygon and a desired sequence of folds, outputs a motion plan for executing the corresponding manipulations, deemed g-folds, on a minimal number of robot grippers. We define parametrized fold sequences for four clothing categories: towels, pants, short-sleeved shirts, and long-sleeved shirts, each represented as polygons. We then devise a model-based optimization approach for visually inferring the class and pose of a spread-out or folded clothing article from a single image, such that the resulting polygon provides a parse suitable for these folding primitives. We test the manipulation and perception tasks individually, and combine them to implement an autonomous folding system on the Willow Garage PR2. This enables the PR2 to identify a clothing article spread out on a table, execute the computed folding sequence, and visually track its progress over successive folds. We consider how the tedious chore of folding clothes can be performed by a robot. At the core of our approach is the definition of a cloth model that allows us to reason about the geometry rather than the physics of the cloth in significant parts of the state space. We present an algorithm that, given the geometry of the cloth, computes how many grippers are needed and what the motion of these grippers are to achieve a final configuration specified as a sequence of g − folds—folds that can be achieved while staying in the subset of the state space to which the geometric model applies. G-folds are easy to specify and are sufficiently rich to capture most common cloth folding procedures.We consider folds involving single and stacked layers of material and describe experiments folding towels, shirts, sweaters, and slacks with a Willow Garage PR2 robot. Experiments based on the planner had success rates varying between 5 9 and 9 9 for different clothing articles. We present a novel vision-based grasp point detection algorithm that can reliably detect the corners of a piece of cloth, using only geometric cues that are robust to variation in texture. Furthermore, we demonstrate the effectiveness of our algorithm in the context of folding a towel using a general-purpose two-armed mobile robotic platform without the use of specialized end-effectors or tools. The robot begins by picking up a randomly dropped towel from a table, goes through a sequence of vision-based re-grasps and manipulations—partially in the air, partially on the table—and finally stacks the folded towel in a target location. The reliability and robustness of our algorithm enables for the first time a robot with general purpose manipulators to reliably and fully-autonomously fold previously unseen towels, demonstrating success on all 50 out of 50 single-towel trials as well as on a pile of 5 towels. Research on robotic manipulation has primarily focused on grasping rigid objects using a single manipulator. It is however evident that in order to be truly pervasive, service robots will need to handle deformable objects, possibly with two arms. In this paper we tackle the problem of using cooperative manipulators to perform towel folding tasks. Differently from other approaches, our method executes what we call a momentum fold - a swinging motion that exploits the dynamics of the object being manipulated. We propose a new learning algorithm that combines imitation and reinforcement learning. Human demonstrations are used to reduce the search space of the reinforcement learning algorithm, which then quickly converges to its final solution. The strengths of the algorithm come from its efficient processing, fast learning capabilities, absence of a deformable object model, and applicability to other problems exhibiting temporally incoherent parameter spaces. A wide range of experiments were performed on a robotic platform, demonstrating the algorithm's capability and practicality.