Legged Robots for Object Manipulation: A Review

Legged robots can have a unique role in manipulating objects in dynamic, human-centric, or otherwise inaccessible environments. Although most legged robotics research to date typically focuses on traversing these challenging environments, many legged platform demonstrations have also included "moving an object" as a way of doing tangible work. Legged robots can be designed to manipulate a particular type of object (e.g., a cardboard box, a soccer ball, or a larger piece of furniture), by themselves or collaboratively. The objective of this review is to collect and learn from these examples, to both organize the work done so far in the community and highlight interesting open avenues for future work. This review categorizes existing works into four main manipulation methods: object interactions without grasping, manipulation with walking legs, dedicated non-locomotive arms, and legged teams. Each method has different design and autonomy features, which are illustrated by available examples in the literature. Based on a few simplifying assumptions, we further provide quantitative comparisons for the range of possible relative sizes of the manipulated object with respect to the robot. Taken together, these examples suggest new directions for research in legged robot manipulation, such as multifunctional limbs, terrain modeling, or learning-based control, to support a number of new deployments in challenging indoor/outdoor scenarios in warehouses/construction sites, preserved natural areas, and especially for home robotics.Comment: Preprint of the paper submitted to Frontiers in Mechanical Engineerin

Identifying important sensory feedback for learning locomotion skills

Robot motor skills can be acquired by deep reinforcement learning as neural networks to refect state–action mapping. The selection of states has been demonstrated to be crucial for successful robot motor learning. However, because of the complexity of neural networks, human insights and engineering eforts are often required to select appropriate states through qualitative approaches, such as ablation studies, without a quantitative analysis of the state importance. Here we present a systematic saliency analysis that quantitatively evaluates the relative importance of diferent feedback states for motor skills learned through deep reinforcement learning. Our approach provides a guideline to identify the most essential feedback states for robot motor learning. By using only the important states including joint positions, gravity vector and base linear and angular velocities, we demonstrate that a simulated quadruped robot can learn various robust locomotion skills. We fnd that locomotion skills learned only with important states can achieve task performance comparable to the performance of those with more states. This work provides quantitative insights into the impacts of state observations on specifc types of motor skills, enabling the learning of a wide range of motor skills with minimal sensing dependencies

Identifying important sensory feedback for learning locomotion skills

Robot motor skills can be acquired by deep reinforcement learning as neural networks to reflect state–action mapping. The selection of states has been demonstrated to be crucial for successful robot motor learning. However, because of the complexity of neural networks, human insights and engineering efforts are often required to select appropriate states through qualitative approaches, such as ablation studies, without a quantitative analysis of the state importance. Here we present a systematic saliency analysis that quantitatively evaluates the relative importance of different feedback states for motor skills learned through deep reinforcement learning. Our approach provides a guideline to identify the most essential feedback states for robot motor learning. By using only the important states including joint positions, gravity vector and base linear and angular velocities, we demonstrate that a simulated quadruped robot can learn various robust locomotion skills. We find that locomotion skills learned only with important states can achieve task performance comparable to the performance of those with more states. This work provides quantitative insights into the impacts of state observations on specific types of motor skills, enabling the learning of a wide range of motor skills with minimal sensing dependencies.</p

Rede de sensores inerciais para equilíbrio de um robô humanóide

Mestrado em Engenharia MecânicaO objetivo principal desta dissertação é o desenvolvimento de uma rede de sensores inerciais composta por um conjunto de Inertial Measurement Units, constituídos por acelerómetros, giroscópios e magnetómetros (os últimos foram descartados no projeto). É pretendido que esta rede de sensores permita ao robô humanóide do PHUA - Projeto Humanoide da Universidade de Aveiro adquirir informação necessária ao seu equilíbrio. O primeiro passo deste trabalho passou pela caraterização de dois tipos de unidades inerciais, o RAZOR - SEN 10736 - 9 gdl e o POLOLU - MinIMUv2 - 9 gdl, com auxílio de um braço robótico antropomórfico. Os resultados desta avaliação são apresentados no trabalho. De seguida, foi desenvolvida a estrutura da rede inercial com recurso a uma unidade de processamento central, uma plataforma de desenvolvimento Arduino com controlo multiplexado de oito unidades assente no protocolo de comunicação I2C, acrescido de uma comunicação RS232 com a nona unidade. A rede desenvolvida suporta um máximo de nove unidades inerciais, com uma taxa de aquisição de 7 Hz. O funcionamento da rede não está dependente do número de unidades utilizadas, podendo operar com qualquer número de unidades compreendidas entre 1 e 9 unidades. A rede inercial foi instalada no robô humanóide, e testada durante a execução de movimentos simples, permitindo concluir da viabilidade da rede em diversas situações.The main objective of this dissertation is the development of a network of inertial sensors constituted by a set of Inertial Measurement Units consisting of accelerometers, gyroscopes and magnetometers (the latter were discarded in the project). It is intended that this network of sensors enables the humanoid robot PHUA - Projeto Humanoide da Universidade de Aveiro acquire information necessary for its balance. The first step of this work consisted by characterization of two types of inertial units, the RAZOR - SEN 10736-9 DOF and Pololu - MinIMUv2 - 9 DOF, with the aid of an anthropomorphic robot arm. The results of this evaluation are presented in the work. Then, the inertial network structure was developed using a central processing unit, a development platform Arduino multiplexed with eight control units based on the communication protocol I2C, plus a communication RS232 with the ninth unit. The developed network supports a maximum of nine units inertial, with an acquisition rate of 7 Hz. The network’s operation is not dependent on the number of units used and can operate with any number of units between 1 and 9 units. The network was installed in inertial humanoid robot, and tested during the execution of simple movements, allowing to conclude the feasibility of the network in various situations

Deep learning for gait prediction: an application to exoskeletons for children with neurological disorders

Cerebral Palsy, a non-progressive neurological disorder, is a lifelong condition. While it has no cure, clinical intervention aims to minimise the impact of the disability on individuals' lives. Wearable robotic devices, like exoskeletons, have been rapidly advancing and proving to be effective in rehabilitating individuals with gait pathologies. The utilization of artificial intelligence (AI) algorithms in controlling exoskeletons, particularly at the supervisory level, has emerged as a valuable approach. These algorithms rely on input from onboard sensors to predict gait phase, user intention, or joint kinematics. Using AI to improve the control of robotic devices not only enhances human-robot interaction but also has the potential to improve user comfort and functional outcomes of rehabilitation, and reduce accidents and injuries. In this research study, a comprehensive systematic literature review is conducted, exploring the various applications of AI in lower-limb robotic control. This review focuses on methodological parameters such as sensor usage, training demographics, sample size, and types of models while identifying gaps in the existing literature. Building on the findings of the review, subsequent research leveraged the power of deep learning to predict gait trajectories for the application of rehabilitative exoskeleton control. This study addresses a gap in the existing literature by focusing on predicting pathological gait trajectories, which exhibit higher inter- and intra-subject variability compared to the gait of healthy individuals. The research focused on the gait of children with neurological disorders, particularly Cerebral Palsy, as they stand to benefit greatly from rehabilitative exoskeletons. State-of-the-art deep learning algorithms, including transformers, fully connected neural networks, convolutional neural networks, and long short-term memory networks, were implemented for gait trajectory prediction. This research presents findings on the performance of these models for short-term and long-term recursive predictions, the impact of varying input and output window sizes on prediction errors, the effect of adding variable levels of Gaussian noise, and the robustness of the models in predicting gait at speeds within and outside the speed range of the training set. Moreover, the research outlines a methodology for optimising the stability of long-term forecasts and provides a comparative analysis of gait trajectory forecasting for typically developing children and children with Cerebral Palsy. A novel approach to generating adaptive trajectories for children with Cerebral Palsy, which can serve as reference trajectories for position-controlled exoskeletons, is also presented

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance

Shape formation by self-disassembly in programmable matter systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 225-236).Programmable matter systems are composed of small, intelligent modules able to form a variety of macroscale objects with specific material properties in response to external commands or stimuli. While many programmable matter systems have been proposed in fiction, (Barbapapa, Changelings from Star Trek, the Terminator, and Transformers), and academia, a lack of suitable hardware and accompanying algorithms prevents their full realization. With this thesis research, we aim to create a system of miniature modules that can form arbitrary structures on demand. We develop autonomous 12mm cubic modules capable of bonding to, and communicating with, four of their immediate neighbors. These modules are among the smallest autonomous modular robots capable of sensing, communication, computation, and actuation. The modules employ unique electropermanent magnet connectors. The four connectors in each module enable the modules to communicate and share power with their nearest neighbors. These solid-state connectors are strong enough for a single inter-module connection to support the weight of 80 other modules. The connectors only consume power when switching on or off; they have no static power consumption. We implement a number of low-level communication and control algorithms which manage information transfer between neighboring modules. These algorithms ensure that messages are delivered reliably despite challenging conditions. They monitor the state of all communication links and are able to reroute messages around broken communication links to ensure that they reach their intended destinations. In order to accomplish our long-standing goal of programmatic shape formation, we also develop a suite of provably-correct distributed algorithms that allow complex shape formation. The distributed duplication algorithm that we present allows the system to duplicate any passive object that is submerged in a collection of programmable matter modules. The algorithm runs on the processors inside the modules and requires no external intervention. It requires 0(1) storage and O(n) inter-module messages per module, where n is the number of modules in the system. The algorithm can both magnify and produce multiple copies of the submerged object. A programmable matter system is a large network of autonomous processors, so these algorithms have applicability in a variety of routing, sensor network, and distributed computing applications. While our hardware system provides a 50-module test-bed for the algorithms, we show, by using a unique simulator, that the algorithms are capable of operating in much larger environments. Finally, we perform hundreds of experiments using both the simulator and hardware to show how the algorithms and hardware operate in practice.by Kyle William Gilpin.Ph.D

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered