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Robotics

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arXiv2021

A Survey of FPGA-Based Robotic Computing

Recent researches on robotics have shown significant improvement, spanning from algorithms, mechanics to hardware architectures. Robotics, including manipulators, legged robots, drones, and autonomous vehicles, are now widely applied in diverse scenarios. However, the high computation and data complexity of robotic algorithms pose great challenges to its applications. On the one hand, CPU platform is flexible to handle multiple robotic tasks. GPU platform has higher computational capacities and easy-touse development frameworks, so they have been widely adopted in several applications. On the other hand, FPGA-based robotic accelerators are becoming increasingly competitive alternatives, especially in latency-critical and power-limited scenarios. With specialized designed hardware logic and algorithm kernels, FPGA-based accelerators can surpass CPU and GPU in performance and energy efficiency. In this paper, we give an overview of previous work on FPGA-based robotic accelerators covering different stages of the robotic system pipeline. An analysis of software and hardware optimization techniques and main technical issues is presented, along with some commercial and space applications, to serve as a guide for future work.

cs.RO↗

arXiv2021

Reinforcement Learning Approaches in Social Robotics

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field.

cs.RO↗

arXiv2020

With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance

Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions as a means to cope with the lack of a central system coordinating the efforts of all robots. Especially in complex dynamic environments, the coordination boost allowed by communication is critical to avoid collisions between cooperating robots. However, the risk of collision between a pair of robots fluctuates through their motion and communication is not always needed. Additionally, constant communication makes much of the still valuable information shared in previous time steps redundant. This paper presents an efficient communication method that solves the problem of "when" and with "whom" to communicate in multi-robot collision avoidance scenarios. In this approach, every robot learns to reason about other robots' states and considers the risk of future collisions before asking for the trajectory plans of other robots. We evaluate and verify the proposed communication strategy in simulation with four quadrotors and compare it with three baseline strategies: non-communicating, broadcasting and a distance-based method broadcasting information with quadrotors within a predefined distance.

cs.RO↗

arXiv2020

Bioinspired Bipedal Locomotion Control for Humanoid Robotics Based on EACO

To construct a robot that can walk as efficiently and steadily as humans or other legged animals, we develop an enhanced elitist-mutated ant colony optimization~(EACO) algorithm with genetic and crossover operators in real-time applications to humanoid robotics or other legged robots. This work presents promoting global search capability and convergence rate of the EACO applied to humanoid robots in real-time by estimating the expected convergence rate using Markov chain. Furthermore, we put a special focus on the EACO algorithm on a wide range of problems, from ACO, real-coded GAs, GAs with neural networks~(NNs), particle swarm optimization~(PSO) to complex robotics systems including gait synthesis, dynamic modeling of parameterizable trajectories and gait optimization of humanoid robotics. The experimental results illustrate the capability of this method to discover the premature convergence probability, tackle successfully inherent stagnation, and promote the convergence rate of the EACO-based humanoid robotics systems and demonstrated the applicability and the effectiveness of our strategy for solving sophisticated optimization tasks. We found reliable and fast walking gaits with a velocity of up to 0.47m/s using the EACO optimization strategy. These findings have significant implications for understanding and tackling inherent stagnation and poor convergence rate of the EACO and provide new insight into the genetic architectures and control optimization of humanoid robotics.

cs.NE↗

arXiv2020

Motion Planning Combines Psychological Safety and Motion Prediction for a Sense Motive Robot

Human safety is the most important demand for human robot interaction and collaboration (HRIC), which not only refers to physical safety, but also includes psychological safety. Although many robots with different configurations have entered our living and working environments, the human safety problem is still an ongoing research problem in human-robot coexistence scenarios. This paper addresses the human safety issue by covering both the physical safety and psychological safety aspects. First, we introduce an adaptive robot velocity control and step size adjustment method according to human facial expressions, such that the robot can adjust its movement to keep safety when the human emotion is unusual. Second, we predict the human motion by detecting the suddenly changes of human head pose and gaze direction, such that the robot can infer whether the human attention is distracted, predict the next move of human and rebuild a repulsive force to avoid potential collision. Finally, we demonstrate our idea using a 7 DOF TIAGo robot in a dynamic HRIC environment, which shows that the robot becomes sense motive, and responds to human action and emotion changes quickly and efficiently.

cs.HC↗

arXiv2020

The RobotSlang Benchmark: Dialog-guided Robot Localization and Navigation

Autonomous robot systems for applications from search and rescue to assistive guidance should be able to engage in natural language dialog with people. To study such cooperative communication, we introduce Robot Simultaneous Localization and Mapping with Natural Language (RobotSlang), a benchmark of 169 natural language dialogs between a human Driver controlling a robot and a human Commander providing guidance towards navigation goals. In each trial, the pair first cooperates to localize the robot on a global map visible to the Commander, then the Driver follows Commander instructions to move the robot to a sequence of target objects. We introduce a Localization from Dialog History (LDH) and a Navigation from Dialog History (NDH) task where a learned agent is given dialog and visual observations from the robot platform as input and must localize in the global map or navigate towards the next target object, respectively. RobotSlang is comprised of nearly 5k utterances and over 1k minutes of robot camera and control streams. We present an initial model for the NDH task, and show that an agent trained in simulation can follow the RobotSlang dialog-based navigation instructions for controlling a physical robot platform. Code and data are available at https://umrobotslang.github.io/.

cs.RO↗

arXiv2020

HAVEN: A Unity-based Virtual Robot Environment to Showcase HRI-based Augmented Reality

Due to the COVID-19 pandemic, conducting Human-Robot Interaction (HRI) studies in person is not permissible due to social distancing practices to limit the spread of the virus. Therefore, a virtual reality (VR) simulation with a virtual robot may offer an alternative to real-life HRI studies. Like a real intelligent robot, a virtual robot can utilize the same advanced algorithms to behave autonomously. This paper introduces HAVEN (HRI-based Augmentation in a Virtual robot Environment using uNity), a VR simulation that enables users to interact with a virtual robot. The goal of this system design is to enable researchers to conduct HRI Augmented Reality studies using a virtual robot without being in a real environment. This framework also introduces two common HRI experiment designs: a hallway passing scenario and human-robot team object retrieval scenario. Both reflect HAVEN's potential as a tool for future AR-based HRI studies.

cs.RO↗

arXiv2020

Prognostic and Health Management (PHM) tool for Robot Operating System (ROS)

Nowadays, prognostics-aware systems are increasingly used in many systems and it is critical for sustaining autonomy. All engineering systems, especially robots, are not perfect. Absence of failures in a certain time is the perfect system and it is impossible practically. In all engineering works, we must try to predict or minimize/prevent failures in the system. Failures in the systems are generally unknown, so prediction of these failures and reliability of the system is made by prediction process. Reliability analysis is important for the improving the system performance, extending system lifetime, etc. Prognostic and Health Management (PHM) includes reliability, safety, predictive fault detection / isolation, advanced diagnostics / prognostics, component lifecycle tracking, health reporting and information management, etc. This study proposes an open source robot prognostic and health management tool using model-based methodology namely "Prognostics and Health Management tool for ROS". This tool is a generic tool for using with any kind of robot (mobile robot, robot arm, drone etc.) with compatible with ROS. Some features of this tool are managing / monitoring robots' health, RUL, probability of task completion (PoTC) etc. User is able to enter the necessary equations and components information (hazard rates, robot configuration etc.) to the PHM tool and the other sensory data like temperature, humidity, pressure, load etc. In addition to these, a case study is conducted for the mobile robots (OTA) using this tool.

cs.RO↗

arXiv2020

A Robotic Dating Coaching System Leveraging Online Communities Posts

Can a robot be a personal dating coach? Even with the increasing amount of conversational data on the internet, the implementation of conversational robots remains a challenge. In particular, a detailed and professional counseling log is expensive and not publicly accessible. In this paper, we develop a robot dating coaching system leveraging corpus from online communities. We examine people's perceptions of the dating coaching robot with a dialogue module. 97 participants joined to have a conversation with the robot, and 30 of them evaluated the robot. The results indicate that participants thought the robot could become a dating coach while considering the robot is entertaining rather than helpful.

cs.RO↗

arXiv2020

Trajectory and Passive Beamforming Design for IRS-aided Multi-Robot NOMA Indoor Networks

A novel intelligent reflecting surface (IRS)-aided multi-robot network is proposed, where multiple mobile wheeled robots are served by an access point (AP) through non-orthogonal multiple access (NOMA). The goal is to maximize the sum-rate of all robots by jointly optimizing trajectories and NOMA decoding orders of robots, reflecting coefficients of the IRS, and the power allocation of the AP, subject to the quality of service (QoS) of each robot. To tackle this problem, a dueling double deep Q-network (D^{3}QN) based algorithm is invoked for jointly determining the phase shift matrix and robots' trajectories. Specifically, the trajectories for robots contain a set of local optimal positions, which reveals that robots make the optimal decision at each step. Numerical results demonstrated that the proposed D^{3}QN algorithm outperforms the conventional algorithm, while the performance of IRS-NOMA network is better than the orthogonal multiple access (OMA) network.

cs.RO↗

arXiv2021

Robotic Communications for 5G and Beyond: Challenges and Research Opportunities

The ongoing surge in applications of robotics brings both opportunities and challenges for the fifth-generation (5G) and beyond (B5G) of communication networks. This article focuses on 5G/B5G-enabled terrestrial robotic communications with an emphasis on distinct characteristics of such communications. Firstly, signal and spatial modeling for robotic communications are presented. To elaborate further, both the benefits and challenges derived from robots' mobility are discussed. As a further advance, a novel simultaneous localization and radio mapping (SLARM) framework is proposed for integrating localization and communications into robotic networks. Furthermore, dynamic trajectory design and resource allocation for both indoor and outdoor robots are provided to verify the performance of robotic communications in the context of typical robotic application scenarios.

cs.RO↗

arXiv2020

Crowd Vetting: Rejecting Adversaries via Collaboration--with Application to Multi-Robot Flocking

We characterize the advantage of using a robot's neighborhood to find and eliminate adversarial robots in the presence of a Sybil attack. We show that by leveraging the opinions of its neighbors on the trustworthiness of transmitted data, robots can detect adversaries with high probability. We characterize a number of communication rounds required to achieve this result to be a function of the communication quality and the proportion of legitimate to malicious robots. This result enables increased resiliency of many multi-robot algorithms. Because our results are finite time and not asymptotic, they are particularly well-suited for problems with a time critical nature. We develop two algorithms, \emph{FindSpoofedRobots} that determines trusted neighbors with high probability, and \emph{FindResilientAdjacencyMatrix} that enables distributed computation of graph properties in an adversarial setting. We apply our methods to a flocking problem where a team of robots must track a moving target in the presence of adversarial robots. We show that by using our algorithms, the team of robots are able to maintain tracking ability of the dynamic target.

cs.RO↗

arXiv2020

Uniform Circle Formation By Oblivious Swarm Robots

In this paper, we study the circle formation problem by multiple autonomous and homogeneous disc-shaped robots (also known as fat robots). The goal of the robots is to place themselves on the periphery of a circle. Circle formation has many real-world applications, such as boundary surveillance. This paper addresses one variant of such problem { uniform circle formation, where the robots have to be equidistant apart. The robots operate by executing cycles of the states wait-look-compute-move. They are oblivious, indistinguishable, anonymous, and do not communicate via message passing. First, we solve the uniform circle formation problem while assuming the robots to be transparent. Next, we address an even weaker model, where the robots are non-transparent and have limited visibility. We propose novel distributed algorithms to solve these variants. Our presented algorithms in this paper are proved to be correct and guarantee to prevent collision and deadlock among the swarm of robots.

cs.RO↗

arXiv2020

Deliberative and Conceptual Inference in Service Robots

Service robots need to reason to support people in daily life situations. Reasoning is an expensive resource that should be used on demand whenever the expectations of the robot do not match the situation of the world and the execution of the task is broken down; in such scenarios the robot must perform the common sense daily life inference cycle consisting on diagnosing what happened, deciding what to do about it, and inducing and executing a plan, recurring in such behavior until the service task can be resumed. Here we examine two strategies to implement this cycle: (1) a pipe-line strategy involving abduction, decision-making and planning, which we call deliberative inference and (2) the use of the knowledge and preferences stored in the robot's knowledge-base, which we call conceptual inference. The former involves an explicit definition of a problem space that is explored through heuristic search, and the latter is based on conceptual knowledge including the human user preferences, and its representation requires a non-monotonic knowledge-based system. We compare the strengths and limitations of both approaches. We also describe a service robot conceptual model and architecture capable of supporting the daily life inference cycle during the execution of a robotics service task. The model is centered in the declarative specification and interpretation of robot's communication and task structure. We also show the implementation of this framework in the fully autonomous robot Golem-III. The framework is illustrated with two demonstration scenarios.

cs.RO↗

arXiv2022

QoS Aware Robot Trajectory Optimization with IRS-Assisted Millimeter-Wave Communications

In this paper, we consider the motion energy minimization problem for a robot that uses millimeter-wave (mm-wave) communications assisted by an intelligent reflective surface (IRS). The robot must perform tasks within given deadlines and it is subject to uplink quality of service (QoS) constraints. This problem is crucial for fully automated factories that are governed by the binomial of autonomous robots and new generations of mobile communications, i.e., 5G and 6G. In this new context, robot energy efficiency and communication reliability remain fundamental problems that couple in optimizing robot trajectory and communication QoS. More precisely, to account for the mutual dependency between robot position and communication QoS, robot trajectory and beamforming at the IRS and access point all need to be optimized. We present a solution that can decouple the two problems by exploiting mm-wave channel characteristics. Then, a closed-form solution is obtained for the beamforming optimization problem, whereas the trajectory is optimized by a novel successive-convex optimization-based algorithm that can deal with abrupt line-of-sight (LOS) to non-line-of-sight (NLOS) transitions. Specifically, the algorithm uses a radio map to avoid collisions with obstacles and poorly covered areas. We prove that the algorithm can converge to a solution satisfying the Karush-Kuhn-Tucker conditions. The simulation results show a fast convergence rate of the algorithm and a dramatic reduction of the motion energy consumption with respect to methods that aim to find maximum-rate trajectories. Moreover, we show that the use of passive IRSs represents a powerful solution to improve the radio coverage and motion energy efficiency of robots.

cs.NI↗

arXiv2021

Transparency in Multi-Human Multi-Robot Interaction

Transparency is a key factor in improving the performance of human-robot interaction. A transparent interface allows humans to be aware of the state of a robot and to assess the progress of the tasks at hand. When multi-robot systems are involved, transparency is an even greater challenge, due to the larger number of variables affecting the behavior of the robots as a whole. Significant effort has been devoted to studying transparency when single operators interact with multiple robots. However, studies on transparency that focus on multiple human operators interacting with a multi-robot systems are limited. This paper aims to fill this gap by presenting a human-swarm interaction interface with graphical elements that can be enabled and disabled. Through this interface, we study which graphical elements are contribute to transparency by comparing four "transparency modes": (i) no transparency (no operator receives information from the robots), (ii) central transparency (the operators receive information only relevant to their personal task), (iii) peripheral transparency (the operators share information on each others' tasks), and (iv) mixed transparency (both central and peripheral). We report the results in terms of awareness, trust, and workload of a user study involving 18 participants engaged in a complex multi-robot task.

cs.RO↗

arXiv2021

Distributed Control of Multi-Robot Systems in the Presence of Deception and Denial of Service Attacks

This research proposes a distributed switching control to secure multi-robot systems in the presence of cyberattacks. Two major types of cyberattack are considered: deception attack and denial of service (DoS) attack, which compromise the integrity and availability of resources, respectively. First, a residual-based attack detection scheme is introduced to identify the type of attacks. Then, a switching control is designed to neutralize the effect of the identified attacks, satisfying the performance guarantees required for state consensus among robots. For the type of a deception attack, coordination-free consensus protocols are designed to tune the weights of each robot in a way that uncompromised robots gain more weight than compromised robots. For the type of a DoS attack, leader-follower protocols that reconfigure the communication topology are utilized to transform the compromised robots into sub-robots following the leaders. The performance of the proposed approach is evaluated on the Robotarium multi-robot testbed. A full demonstration with extensive cases is available at https://youtu.be/eSj0XS2pdxI.

cs.RO↗

arXiv2021

Where is my hand? Deep hand segmentation for visual self-recognition in humanoid robots

The ability to distinguish between the self and the background is of paramount importance for robotic tasks. The particular case of hands, as the end effectors of a robotic system that more often enter into contact with other elements of the environment, must be perceived and tracked with precision to execute the intended tasks with dexterity and without colliding with obstacles. They are fundamental for several applications, from Human-Robot Interaction tasks to object manipulation. Modern humanoid robots are characterized by high number of degrees of freedom which makes their forward kinematics models very sensitive to uncertainty. Thus, resorting to vision sensing can be the only solution to endow these robots with a good perception of the self, being able to localize their body parts with precision. In this paper, we propose the use of a Convolution Neural Network (CNN) to segment the robot hand from an image in an egocentric view. It is known that CNNs require a huge amount of data to be trained. To overcome the challenge of labeling real-world images, we propose the use of simulated datasets exploiting domain randomization techniques. We fine-tuned the Mask-RCNN network for the specific task of segmenting the hand of the humanoid robot Vizzy. We focus our attention on developing a methodology that requires low amounts of data to achieve reasonable performance while giving detailed insight on how to properly generate variability in the training dataset. Moreover, we analyze the fine-tuning process within the complex model of Mask-RCNN, understanding which weights should be transferred to the new task of segmenting robot hands. Our final model was trained solely on synthetic images and achieves an average IoU of 82% on synthetic validation data and 56.3% on real test data. These results were achieved with only 1000 training images and 3 hours of training time using a single GPU.

cs.RO↗