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.
We consider the problem of autonomously bringing an article of clothing into a desired configuration using a general-purpose two-armed robot. We propose a hidden Markov model (HMM) for estimating the identity of the article and tracking the article's configuration throughout a specific sequence of manipulations and observations. At the end of this sequence, the article's configuration is known, though not necessarily desired. The estimated identity and configuration of the article are then used to plan a second sequence of manipulations that brings the article into the desired configuration. We propose a relaxation of a strain-limiting finite element model for cloth simulation that can be solved via convex optimization; this serves as the basis of the transition and observation models of the HMM. The observation model uses simple perceptual cues consisting of the height of the article when held by a single gripper and the silhouette of the article when held by two grippers. The model accurately estimates the identity and configuration of clothing articles, enabling our procedure to autonomously bring a variety of articles into desired configurations that are useful for other tasks, such as folding. In our research, we aim to construct a house-keeping robot system which folds laundry. And, we propose a way for folding clothes without vision sensing device. To realize this system, we prepare appropriate fixed folding motion. We analyzed cloth folding motions by human. And, we found fixed folding motion pattern for the robot. Furthermore, we generated a trajectory of folding motion which is applicable to different size of towels. We confirmed the effectiveness of our method through experiments without the use of vision sensing device. 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.