427 research outputs found
Fast Sensing and Adaptive Actuation for Robust Legged Locomotion
Robust legged locomotion in complex terrain demands fast perturbation detection and reaction. In animals, due to the neural transmission delays, the high-level control loop involving the brain is absent from mitigating the initial disturbance. Instead, the low-level compliant behavior embedded in mechanics and the mid-level controllers in the spinal cord are believed to provide quick response during fast locomotion. Still, it remains unclear how these low- and mid-level components facilitate robust locomotion.
This thesis aims to identify and characterize the underlining elements responsible for fast sensing and actuation. To test individual elements and their interplay, several robotic systems were implemented. The implementations include active and passive mechanisms as a combination of elasticities and dampers in multi-segment robot legs, central pattern generators inspired by intraspinal controllers, and a synthetic robotic version of an intraspinal sensor.
The first contribution establishes the notion of effective damping. Effective damping is defined as the total energy dissipation during one step, which allows quantifying how much ground perturbation is mitigated. Using this framework, the optimal damper is identified as viscous and tunable. This study paves the way for integrating effective dampers to legged designs for robust locomotion.
The second contribution introduces a novel series elastic actuation system. The proposed system tackles the issue of power transmission over multiple joints, while featuring intrinsic series elasticity. The design is tested on a hopper with two more elastic elements, demonstrating energy recuperation and enhanced dynamic performance.
The third contribution proposes a novel tunable damper and reveals its influence on legged hopping. A bio-inspired slack tendon mechanism is implemented in parallel with a spring. The tunable damping is rigorously quantified on a central-pattern-generator-driven hopping robot, which reveals the trade-off between locomotion robustness and efficiency.
The last contribution explores the intraspinal sensing hypothesis of birds. We speculate that the observed intraspinal structure functions as an accelerometer. This accelerometer could provide fast state feedback directly to the adjacent central pattern generator circuits, contributing to birdsâ running robustness. A biophysical simulation framework is established, which provides new perspectives on the sensing mechanics of the system, including the influence of morphologies and material properties.
Giving an overview of the hierarchical control architecture, this thesis investigates the fast sensing and actuation mechanisms in several control layers, including the low-level mechanical response and the mid-level intraspinal controllers. The contributions of this work provide new insight into animal loco-motion robustness and lays the foundation for future legged robot design
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 robotâs 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
An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application
With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper
Industrial, Collaborative and Mobile Robotics in Latin America: Review of Mechatronic Technologies for Advanced Automation
Mechatronics and Robotics (MaR) have recently gained importance in product development and manufacturing settings and applications. Therefore, the Center for Space Emerging Technologies (C-SET) has managed an international multi-disciplinary study to present, historically, the first Latin American general review of industrial, collaborative, and mobile robotics, with the support of North American and European researchers and institutions. The methodology is developed by considering literature extracted from Scopus, Web of Science, and Aerospace Research Central and adding reports written by companies and government organizations. This describes the state-of-the-art of MaR until the year 2023 in the 3 Sub-Regions: North America, Central America, and South America, having achieved important results related to the academy, industry, government, and entrepreneurship; thus, the statistics shown in this manuscript are unique. Also, this article explores the potential for further work and advantages described by robotic companies such as ABB, KUKA, and Mecademic and the use of the Robot Operating System (ROS) in order to promote research, development, and innovation. In addition, the integration with industry 4.0 and digital manufacturing, architecture and construction, aerospace, smart agriculture, artificial intelligence, and computational social science (human-robot interaction) is analyzed to show the promising features of these growing tech areas, considering the improvements to increase production, manufacturing, and education in the Region. Finally, regarding the information presented, Latin America is considered an important location for investments to increase production and product development, taking into account the further proposal for the creation of the LATAM Consortium for Advanced Robotics and Mechatronics, which could support and work on roboethics and education/R+D+I law and regulations in the Region. Doi: 10.28991/ESJ-2023-07-04-025 Full Text: PD
Structured machine learning models for robustness against different factors of variability in robot control
An important feature of human sensorimotor skill is our ability to learn to reuse them across different environmental contexts, in part due to our understanding of attributes of variability in these environments. This thesis explores how the structure of models used within learning for robot control could similarly help autonomous robots cope with variability, hence achieving skill generalisation. The overarching approach is to develop modular architectures that judiciously combine different forms of inductive bias for learning. In particular, we consider how models and policies should be structured in order to achieve robust behaviour in the face of different factors of variation - in the environment, in objects and in other internal parameters of a policy - with the end goal of more robust, accurate and data-efficient skill acquisition and adaptation.
At a high level, variability in skill is determined by variations in constraints presented by the external environment, and in task-specific perturbations that affect the specification of optimal action. A typical example of environmental perturbation would be variation in lighting and illumination, affecting the noise characteristics of perception. An example of task perturbations would be variation in object geometry, mass or friction, and in the specification of costs associated with speed or smoothness of execution. We counteract these factors of variation by exploring three forms of structuring: utilising separate data sets curated according to the relevant factor of variation, building neural network models that incorporate this factorisation into the very structure of the networks, and learning structured loss functions. The thesis is comprised of four projects exploring this theme within robotics planning and prediction tasks.
Firstly, in the setting of trajectory prediction in crowded scenes, we explore a modular architecture for learning static and dynamic environmental structure. We show that factorising the prediction problem from the individual representations allows for robust and label efficient forward modelling, and relaxes the need for full model re-training in new environments. This modularity explicitly allows for a more flexible and interpretable adaptation of trajectory prediction models to using
pre-trained state of the art models. We show that this results in more efficient motion prediction and allows for performance comparable to the state-of-the-art supervised 2D trajectory prediction.
Next, in the domain of contact-rich robotic manipulation, we consider a modular architecture that combines model-free learning from demonstration, in particular dynamic movement primitives (DMP), with modern model-free reinforcement learning (RL), using both on-policy and off-policy approaches. We show that factorising the skill learning problem to skill acquisition and error correction through policy adaptation strategies such as residual learning can help improve the overall performance of policies in the context of contact-rich manipulation. Our empirical evaluation demonstrates how to best do this with DMPs and propose âresidual Learning from Demonstrationâ (rLfD), a framework that combines DMPs with RL to learn a residual correction policy. Our evaluations, performed both in simulation and on a physical system, suggest that applying residual learning directly in task space and operating on the full pose of the robot can significantly improve the overall performance of DMPs. We show that rLfD offers a gentle to the joints solution that improves the task success and generalisation of DMPs. Last but not least, our study shows that the extracted correction policies can be transferred to different geometries and frictions through few-shot task adaptation.
Third, we employ meta learning to learn time-invariant reward functions, wherein both the objectives of a task (i.e., the reward functions) and the policy for performing that task optimally are learnt simultaneously. We propose a novel inverse reinforcement learning (IRL) formulation that allows us to 1) vary the length of execution by learning time-invariant costs, and 2) relax the temporal alignment requirements for learning from demonstration. We apply our method to two different types of cost formulations and evaluate their performance in the context of learning reward functions for simulated placement and peg in hole tasks executed on a 7DoF Kuka IIWA arm. Our results show that our approach enables learning temporally invariant rewards from misaligned demonstration that can also generalise spatially to out of distribution tasks.
Finally, we employ our observations to evaluate adversarial robustness in the context of transfer learning from a source trained on CIFAR 100 to a target network trained on CIFAR 10. Specifically, we study the effects of using robust optimisation in the source and target networks. This allows us to identify transfer learning strategies under which adversarial defences are successfully retained, in addition to revealing potential vulnerabilities. We study the extent to which adversarially robust features can preserve their defence properties against black and white-box attacks under three different transfer learning strategies. Our empirical evaluations give insights on how well adversarial robustness under transfer learning can generalise.
Learning Motion Skills for a Humanoid Robot
This thesis investigates the learning of motion skills for humanoid robots. As groundwork, a humanoid robot with integrated fall management was developed as an experimental platform. Then, two different approaches for creating motion skills were investigated. First, one that is based on Cartesian quintic splines with optimized parameters.
Second, a reinforcement learning-based approach that utilizes the first approach as a reference motion to guide the learning. Both approaches were tested on the developed robot and on further simulated robots to show their generalization. A special focus was set on the locomotion skill, but a standing-up and kick skill are also discussed.
Diese Dissertation beschĂ€ftigt sich mit dem Lernen von BewegungsfĂ€higkeiten fĂŒr humanoide Roboter. Als Grundlage wurde zunĂ€chst ein humanoider Roboter mit integriertem Fall Management entwickelt, welcher als Experimentalplatform dient. Dann wurden zwei verschiedene AnsĂ€tze fĂŒr die Erstellung von BewegungsfĂ€higkeiten untersucht. Zu erst einer der kartesische quintische Splines mit optimierten Parametern nutzt.
Danach wurde ein Ansatz basierend auf bestÀrkendem Lernen untersucht, welcher den ersten Ansatz als Referenzbewegung benutzt. Beide AnsÀtze wurden sowohl auf der entwickelten Roboterplatform, als auch auf weiteren simulierten Robotern getestet um die Generalisierbarkeit zu zeigen. Ein besonderer Fokus wurde auf die FÀhigkeit des Gehens gelegt, aber auch Aufsteh- und SchussfÀhigkeiten werden diskutiert
More Than Machines?
We know that robots are just machines. Why then do we often talk about them as if they were alive? Laura Voss explores this fascinating phenomenon, providing a rich insight into practices of animacy (and inanimacy) attribution to robot technology: from science-fiction to robotics R&D, from science communication to media discourse, and from the theoretical perspectives of STS to the cognitive sciences. Taking an interdisciplinary perspective, and backed by a wealth of empirical material, Voss shows how scientists, engineers, journalists - and everyone else - can face the challenge of robot technology appearing »a little bit alive« with a reflexive and yet pragmatic stance
Optimality, Objectives, and Trade-Offs in Motor Control under Uncertainty
Biological motor control involves multiple objectives and constraints. In this thesis, I investigated the influence of uncertainty on biological sensorimotor control and decision-making, considering various objectives. In the first study, I used a simple biped walking model simulation to study the control of a rhythmic movement under uncertainty. Uncertainty necessitates a more sophisticated form of motor control involving internal model and sensing, and their effective integration. The optimality of the neural pattern generator incorporating sensory information was shown to be dependent on the relative amount of physical disturbance and sensor noise. When the controller was optimized for state estimation, other objectives of improved energy efficiency, reduced variability, and reduced number of falls were also satisfied. In the second study, human participants performed regression and classification tasks on visually presented scatterplot data. The tasks involved a trade-off between acting on small but prevalent errors and acting on big but scarce errors. We used inverse optimization to characterize the loss function used by humans in these regression and classification tasks, and found that these loss functions change systematically as the data sparsity changed. Despite being highly variable, there were overall shifts towards compensating for prevalent small errors more when the sparsity of the visual data decreased. In the third study, I extended the pattern recognition tasks to include visually mediated force tracking. When participants tracked force targets with visual noise, we observed a slight yet consistent force tracking bias. This bias, which increased with noise, was not explained by commonly hypothesized objectives such as a tendency to reduce effort while regulating error. Additional experiments revealed that a model balancing error reduction and transition reduction tendencies effectively explained and predicted experimental data. Transition reduction tendency was further separated into recency bias and central tendency bias. Notably, this bias disappeared when the task became purely visual, suggesting that such biases could be task-dependent. These findings across the three studies provide useful insights into understanding how uncertainty changes objectives and their trade-offs in biological motor control, and in turn, results in a different control strategy and behaviors
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