337 research outputs found
Comparative Study on Electrochemical Corrosion and Natural Corrosion of Reinforced Concrete Components
Based on the study of the corrosion characteristics of reinforced concrete members under electrochemical corrosion and natural corrosion, such as the corrosion principle, product formation, morphology and microstructure of steel corrosion pits, the differences and similarities of the basic mechanical properties of steel bars and the mechanical properties of concrete members under two corrosion states are analyzed, and the applicability of accelerated corrosion of steel bars in concrete is discussed. In the study of the mechanical properties of corroded steel bars, the non-uniform electrochemical corrosion can be used to replace the natural corrosion. When the research object needs to consider the corrosion expansion force and the corrosion current density model, the difference between the two cannot be ignored
Flipbot: Learning Continuous Paper Flipping via Coarse-to-Fine Exteroceptive-Proprioceptive Exploration
This paper tackles the task of singulating and grasping paper-like deformable
objects. We refer to such tasks as paper-flipping. In contrast to manipulating
deformable objects that lack compression strength (such as shirts and ropes),
minor variations in the physical properties of the paper-like deformable
objects significantly impact the results, making manipulation highly
challenging. Here, we present Flipbot, a novel solution for flipping paper-like
deformable objects. Flipbot allows the robot to capture object physical
properties by integrating exteroceptive and proprioceptive perceptions that are
indispensable for manipulating deformable objects. Furthermore, by
incorporating a proposed coarse-to-fine exploration process, the system is
capable of learning the optimal control parameters for effective paper-flipping
through proprioceptive and exteroceptive inputs. We deploy our method on a
real-world robot with a soft gripper and learn in a self-supervised manner. The
resulting policy demonstrates the effectiveness of Flipbot on paper-flipping
tasks with various settings beyond the reach of prior studies, including but
not limited to flipping pages throughout a book and emptying paper sheets in a
box.Comment: Accepted to International Conference on Robotics and Automation
(ICRA) 202
Learn to Grasp via Intention Discovery and its Application to Challenging Clutter
Humans excel in grasping objects through diverse and robust policies, many of
which are so probabilistically rare that exploration-based learning methods
hardly observe and learn. Inspired by the human learning process, we propose a
method to extract and exploit latent intents from demonstrations, and then
learn diverse and robust grasping policies through self-exploration. The
resulting policy can grasp challenging objects in various environments with an
off-the-shelf parallel gripper. The key component is a learned intention
estimator, which maps gripper pose and visual sensory to a set of sub-intents
covering important phases of the grasping movement. Sub-intents can be used to
build an intrinsic reward to guide policy learning. The learned policy
demonstrates remarkable zero-shot generalization from simulation to the real
world while retaining its robustness against states that have never been
encountered during training, novel objects such as protractors and user
manuals, and environments such as the cluttered conveyor.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L
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