Robots that Learn and Plan — Unifying Robot Learning and Motion Planning for Generalized Task Execution

Robots have the potential to assist people with a variety of everyday tasks, but to achieve that potential robots require software capable of planning and executing motions in cluttered environments. To address this, over the past few decades, roboticists have developed numerous methods for planning motions to avoid obstacles with increasingly stronger guarantees, from probabilistic completeness to asymptotic optimality. Some of these methods have even considered the types of constraints that must be satisfied to perform useful tasks, but these constraints must generally be manually specified. In recent years, there has been a resurgence of methods for automatic learning of tasks from human-provided demonstrations. Unfortunately, these two fields, task learning and motion planning, have evolved largely separate from one another, and the learned models are often not usable by motion planners. In this thesis, we aim to bridge the gap between robot task learning and motion planning by employing a learned task model that can subsequently be leveraged by an asymptotically-optimal motion planner to autonomously execute the task. First, we show that application of a motion planner enables task performance while avoiding novel obstacles and extend this to dynamic environments by replanning at reactive rates. Second, we generalize the method to accommodate time-invariant model parameters, allowing more information to be gleaned from the demonstrations. Third, we describe a more principled approach to temporal registration for such learning methods that mirrors the ultimate integration with a motion planner and often reduces the number of demonstrations required. Finally, we extend this framework to the domain of mobile manipulation. We empirically evaluate each of these contributions on multiple household tasks using the Aldebaran Nao, Rethink Robotics Baxter, and Fetch mobile manipulator robots to show that these approaches improve task execution success rates and reduce the amount of human-provided information required.Doctor of Philosoph

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

Deep Reinforcement Learning for Robotic Tasks: Manipulation and Sensor Odometry

Research in robotics has frequently focused on artificial intelligence (AI). To increase the effectiveness of the learning process for the robot, numerous studies have been carried out. To be more effective, robots must be able to learn effectively in a shorter amount of time and with fewer resources. It has been established that reinforcement learning (RL) is efficient for aiding a robot's learning. In this dissertation, we proposed and optimized RL algorithms to ensure that our robots learn well. Research into driverless or self-driving automobiles has exploded in the last few years. A precise estimation of the vehicle's motion is crucial for higher levels of autonomous driving functionality. Recent research has been done on the development of sensors to improve the localization accuracy of these vehicles. Recent sensor odometry research suggests that Lidar Monocular Visual Odometry (LIMO) can be beneficial for determining odometry. However, the LIMO algorithm has a considerable number of errors when compared to ground truth, which motivates us to investigate ways to make it far more accurate. We intend to use a Genetic Algorithm (GA) in our dissertation to improve LIMO's performance. Robotic manipulator research has also been popular and has room for development, which piqued our interest. As a result, we researched robotic manipulators and applied GA to Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) (GA+DDPG+HER). Finally, we kept researching DDPG and created an algorithm named AACHER. AACHER uses HER and many independent instances of actors and critics from the DDPG to increase a robot's learning effectiveness. AACHER is used to evaluate the results in both custom and existing robot environments.In the first part of our research, we discuss the LIMO algorithm, an odometry estimation technique that employs a camera and a Lidar for visual localization by tracking features from their measurements. LIMO can estimate sensor motion via Bundle Adjustment based on reliable keyframes. LIMO employs weights of the vegetative landmarks and semantic labeling to reject outliers. LIMO, like many other odometry estimating methods, has the issue of having a lot of hyperparameters that need to be manually modified in response to dynamic changes in the environment to reduce translational errors. The GA has been proven to be useful in determining near-optimal values of learning hyperparameters. In our study, we present and propose the application of the GA to maximize the performance of LIMO's localization and motion estimates by optimizing its hyperparameters. We test our approach using the well-known KITTI dataset and demonstrate how the GA supports LIMO to lower translation errors in various datasets. Our second contribution includes the use of RL. Robots using RL can select actions based on a reward function. On the other hand, the choice of values for the learning algorithm's hyperparameters could have a big impact on the entire learning process. We used GA to find the hyperparameters for the Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER). We proposed the algorithm GA+DDPG+HER to optimize learning hyperparameters and applied it to the robotic manipulation tasks of FetchReach, FetchSlide, FetchPush, FetchPick\&Place, and DoorOpening. With only a few modifications, our proposed GA+DDPG+HER was also used in the AuboReach environment. Compared to the original algorithm (DDPG+HER), our experiments show that our approach (GA+DDPG+HER) yields noticeably better results and is substantially faster. In the final part of our dissertation, we were motivated to use and improve DDPG. Many simulated continuous control problems have shown promising results for the DDPG, a unique Deep Reinforcement Learning (DRL) technique. DDPG has two parts: Actor learning and Critic learning. The performance of the DDPG technique is therefore relatively sensitive and unstable because actor and critic learning is a considerable contributor to the robot’s total learning. Our dissertation suggests a multi-actor-critic DDPG for reliable actor-critic learning as an improved DDPG to further enhance the performance and stability of DDPG. This multi-actor-critic DDPG is further combined with HER, called AACHER. The average value of numerous actors/critics is used to replace the single actor/critic in the traditional DDPG approach for improved resistance when one actor/critic performs poorly. Numerous independent actors and critics can also learn from the environment in general. In all the actor/critic number combinations that are evaluated, AACHER performs better than DDPG+HER

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

An Object SLAM Framework for Association, Mapping, and High-Level Tasks

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this paper, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance.Comment: Accepted by IEEE Transactions on Robotics(T-RO

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Motion Mappings for Continuous Bilateral Teleoperation

Mapping operator motions to a robot is a key problem in teleoperation. Due to differences between workspaces, such as object locations, it is particularly challenging to derive smooth motion mappings that fulfill different goals (e.g. picking objects with different poses on the two sides or passing through key points). Indeed, most state-of-the-art methods rely on mode switches, leading to a discontinuous, low-transparency experience. In this paper, we propose a unified formulation for position, orientation and velocity mappings based on the poses of objects of interest in the operator and robot workspaces. We apply it in the context of bilateral teleoperation. Two possible implementations to achieve the proposed mappings are studied: an iterative approach based on locally-weighted translations and rotations, and a neural network approach. Evaluations are conducted both in simulation and using two torque-controlled Franka Emika Panda robots. Our results show that, despite longer training times, the neural network approach provides faster mapping evaluations and lower interaction forces for the operator, which are crucial for continuous, real-time teleoperation.Comment: Accepted for publication at the IEEE Robotics and Automation Letters (RA-L