Tactile Estimation of Extrinsic Contact Patch for Stable Placement

Precise perception of contact interactions is essential for the fine-grained manipulation skills for robots. In this paper, we present the design of feedback skills for robots that must learn to stack complex-shaped objects on top of each other. To design such a system, a robot should be able to reason about the stability of placement from very gentle contact interactions. Our results demonstrate that it is possible to infer the stability of object placement based on tactile readings during contact formation between the object and its environment. In particular, we estimate the contact patch between a grasped object and its environment using force and tactile observations to estimate the stability of the object during a contact formation. The contact patch could be used to estimate the stability of the object upon the release of the grasp. The proposed method is demonstrated on various pairs of objects that are used in a very popular board game.Comment: Under submissio

Model Adaptive Blended Curriculum (ABC) sebagai Inovasi Kurikulum dalam Upaya Mendukung Pemerataan Pendidikan

Indonesia merupakan negara yang luas baik dari segi geografis maupun budaya. Dengan melihat perbedaan tersebut, tentunya tiap-tiap sumber daya manusia pada tiap-tiap wilayah menjadi lebih beragam. Oleh karena itu, kajian ini bertujuan untuk mengajukan suatu inovasi model kurikulum yang dianggap tepat untuk diterapkan. Kurikulum yang baik di era sekarang tentunya tidak mengedepankan teacher centered learning, namun memprioritaskan student centered learning. Kurikulum secara adaptif sangat berpotensi untuk mewujudkan student centered learning. Maka dari itu, model semacam ini sangat tepat untuk diterapkan karena memenuhi unsur prinsip-prinsip dalam kurikulum, terutama prinsip relevansi dengan perkembangan zaman. Adaptive Blended Curriculum (ABC) merupakan suatu model inovasi kurikulum yang menekankan pada kurikulum secara blended (campuran) antara langsung dengan tidak langsung. Hasil dari kajian ini diharapkan dapat menjadi suatu referensi alternatif dalam penerapan kurikulum yang layak di Indonesia.   Indonesia is a vast country both geographically and culturally. By looking at these differences, of course, it makes each potential human resource in each region more diverse. So, this study aims to propose an innovative curriculum model that is considered appropriate to be applied. A good curriculum in the current era certainly does not prioritize teacher centered learning, but prioritizes student centered learning. Adaptive curriculum has the potential to realize student centered learning. Therefore, this kind of model is very appropriate to be applied because it fulfills the elements of the principles in the curriculum, especially the principle of relevance to the times. Adaptive Blended Curriculum (ABC) is a curriculum innovation model that emphasizes the curriculum blended direct and indirect. The results of this study are expected to become an alternative reference in implementing a proper curriculum in Indonesia

A Workflow for Training Robotic End-to-End Visuomotor Policies in Simulation

The explicit programming methods which control most industrial robotic manipulators is a great option for precisely defined environments like factories and warehouses. These spaces are intentionally designed so robots can follow commands complete a task with limited or no awareness of their environment. But the real-world does not adhere to such strict rules; it is noisy, dynamic, and interactive. For these robots to work alongside humans in the real-world a new approach that can adapt to this randomness is needed. Research has turned to machine learning, specifically neural networks (NN), for this. Instead of programming exactly what the robot should do in every possible scenario, these methods let a NN control the robot. The NN is trained to control the robot and learns a general approach that it can adapt to whatever conditions it encounters. I focus specifically on end-to-end methods which take an observation of the environment and directly map this to a decision. These NN are trained on a specific task and run continuously. By using proprioceptive information about the robot's state and depth images from a camera in front of the robot as inputs these NN learn a visuomotor policy, akin to hand-eye coordination in humans. I share a workflow for creating these NN through behavior cloning and compare the performance of different network structures and training parameters. The workflow I present includes tools for generating demonstrations of a task, training the network, and evaluating the network. This process is designed to be adapted for different robots, tasks, or training methodologies. I show how recursive neural network structures and the training on domain randomized data both improve performance of the NN. I also describe issues where the NN do not learn the intended task and identify changes that may correct the learning process.No embargoAcademic Major: Mechanical Engineerin