Learning cloth manipulation with demonstrations

Recent advances in Deep Reinforcement learning and computational capabilities of GPUs have led to variety of research being conducted in the learning side of robotics. The main aim being that of making autonomous robots that are capable of learning how to solve a task on their own with minimal requirement for engineering on the planning, vision, or control side. Efforts have been made to learn the manipulation of rigid objects through the help of human demonstrations, specifically in the tasks such as stacking of multiple blocks on top of each other, inserting a pin into a hole, etc. These Deep RL algorithms successfully learn how to complete a task involving the manipulation of rigid objects, but autonomous manipulation of textile objects such as clothes through Deep RL algorithms is still not being studied in the community. The main objectives of this work involve, 1) implementing the state of the art Deep RL algorithms for rigid object manipulation and getting a deep understanding of the working of these various algorithms, 2) Creating an open-source simulation environment for simulating textile objects such as clothes, 3) Designing Deep RL algorithms for learning autonomous manipulation of textile objects through demonstrations.Peer ReviewedPreprin

Agile Autonomous Driving using End-to-End Deep Imitation Learning

We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost sensors. By imitating a model predictive controller equipped with advanced sensors, we train a deep neural network control policy to map raw, high-dimensional observations to continuous steering and throttle commands. Compared with recent approaches to similar tasks, our method requires neither state estimation nor on-the-fly planning to navigate the vehicle. Our approach relies on, and experimentally validates, recent imitation learning theory. Empirically, we show that policies trained with online imitation learning overcome well-known challenges related to covariate shift and generalize better than policies trained with batch imitation learning. Built on these insights, our autonomous driving system demonstrates successful high-speed off-road driving, matching the state-of-the-art performance.Comment: 13 pages, Robotics: Science and Systems (RSS) 201

Scene Understanding for Autonomous Manipulation with Deep Learning

Over the past few years, deep learning techniques have achieved tremendous success in many visual understanding tasks such as object detection, image segmentation, and caption generation. Despite this thriving in computer vision and natural language processing, deep learning has not yet shown signicant impact in robotics. Due to the gap between theory and application, there are many challenges when applying the results of deep learning to the real robotic systems. In this study, our long-term goal is to bridge the gap between computer vision and robotics by developing visual methods that can be used in real robots. In particular, this work tackles two fundamental visual problems for autonomous robotic manipulation: affordance detection and ne-grained action understanding. Theoretically, we propose dierent deep architectures to further improves the state of the art in each problem. Empirically, we show that the outcomes of our proposed methods can be applied in real robots and allow them to perform useful manipulation tasks

Design of an Autonomous Agriculture Robot for Real Time Weed Detection using CNN

Agriculture has always remained an integral part of the world. As the human population keeps on rising, the demand for food also increases, and so is the dependency on the agriculture industry. But in today's scenario, because of low yield, less rainfall, etc., a dearth of manpower is created in this agricultural sector, and people are moving to live in the cities, and villages are becoming more and more urbanized. On the other hand, the field of robotics has seen tremendous development in the past few years. The concepts like Deep Learning (DL), Artificial Intelligence (AI), and Machine Learning (ML) are being incorporated with robotics to create autonomous systems for various sectors like automotive, agriculture, assembly line management, etc. Deploying such autonomous systems in the agricultural sector help in many aspects like reducing manpower, better yield, and nutritional quality of crops. So, in this paper, the system design of an autonomous agricultural robot which primarily focuses on weed detection is described. A modified deep-learning model for the purpose of weed detection is also proposed. The primary objective of this robot is the detection of weed on a real-time basis without any human involvement, but it can also be extended to design robots in various other applications involved in farming like weed removal, plowing, harvesting, etc., in turn making the farming industry more efficient. Source code and other details can be found at https://github.com/Dhruv2012/Autonomous-Farm-RobotComment: Published at the AVES 2021 conference. Source code and other details can be found at https://github.com/Dhruv2012/Autonomous-Farm-Robo