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Robotics

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arXiv2022

Investigating the Effects of Robot Proficiency Self-Assessment on Trust and Performance

Human-robot teams will soon be expected to accomplish complex tasks in high-risk and uncertain environments. Here, the human may not necessarily be a robotics expert, but will need to establish a baseline understanding of the robot's abilities in order to appropriately utilize and rely on the robot. This willingness to rely, also known as trust, is based partly on the human's belief in the robot's proficiency at a given task. If trust is too high, the human may push the robot beyond its capabilities. If trust is too low, the human may not utilize it when they otherwise could have, wasting precious resources. In this work, we develop and execute an online human-subjects study to investigate how robot proficiency self-assessment reports based on Factorized Machine Self-Confidence affect operator trust and task performance in a grid world navigation task. Additionally we present and analyze a metric for trust level assessment, which measures the allocation of control between an operator and robot when the human teammate is free to switch between teleportation and autonomous control. Our results show that an a priori robot self-assessment report aligns operator trust with robot proficiency, and leads to performance improvements and small increases in self-reported trust.

cs.RO↗

arXiv2022

Addressing Non-Intervention Challenges via Resilient Robotics utilizing a Digital Twin

Multi-robot systems face challenges in reducing human interventions as they are often deployed in dangerous environments. It is therefore necessary to include a methodology to assess robot failure rates to reduce the requirement for costly human intervention. A solution to this problem includes robots with the ability to work together to ensure mission resilience. To prevent this intervention, robots should be able to work together to ensure mission resilience. However, robotic platforms generally lack built-in interconnectivity with other platforms from different vendors. This work aims to tackle this issue by enabling the functionality through a bidirectional digital twin. The twin enables the human operator to transmit and receive information to and from the multi-robot fleet. This digital twin considers mission resilience and autonomous and human-led decision making to enable the resilience of a multi-robot fleet. This creates the cooperation, corroboration, and collaboration of diverse robots to leverage the capability of robots and support recovery of a failed robot.

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arXiv2022

Explain yourself! Effects of Explanations in Human-Robot Interaction

Recent developments in explainable artificial intelligence promise the potential to transform human-robot interaction: Explanations of robot decisions could affect user perceptions, justify their reliability, and increase trust. However, the effects on human perceptions of robots that explain their decisions have not been studied thoroughly. To analyze the effect of explainable robots, we conduct a study in which two simulated robots play a competitive board game. While one robot explains its moves, the other robot only announces them. Providing explanations for its actions was not sufficient to change the perceived competence, intelligence, likeability or safety ratings of the robot. However, the results show that the robot that explains its moves is perceived as more lively and human-like. This study demonstrates the need for and potential of explainable human-robot interaction and the wider assessment of its effects as a novel research direction.

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arXiv2023

Intelligent Trajectory Design for RIS-NOMA aided Multi-robot Communications

A novel reconfigurable intelligent surface-aided multi-robot network is proposed, where multiple mobile robots are served by an access point (AP) through non-orthogonal multiple access (NOMA). The goal is to maximize the sum-rate of whole trajectories for the multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots, phase-shift coefficients of the RIS, and the power allocation of the AP, subject to predicted initial and final positions of robots and the quality of service (QoS) of each robot. To tackle this problem, an integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network (D$^{3}$QN) algorithm. For initial and final position prediction for robots, the LSTM-ARIMA is able to overcome the problem of gradient vanishment of non-stationary and non-linear sequences of data. For jointly determining the phase shift matrix and robots' trajectories, D$^{3}$QN is invoked for solving the problem of action value overestimation. Based on the proposed scheme, each robot holds an optimal trajectory based on the maximum sum-rate of a whole trajectory, which reveals that robots pursue long-term benefits for whole trajectory design. Numerical results demonstrated that: 1) LSTM-ARIMA model provides high accuracy predicting model; 2) The proposed D$^{3}$QN algorithm can achieve fast average convergence; and 3) RIS-NOMA networks have superior network performance compared to RIS-aided orthogonal counterparts.

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arXiv2022

Design and Simulation of an Autonomous Quantum Flying Robot Vehicle: An IBM Quantum Experience

The application of quantum computation and information in robotics has caught the attention of researchers off late. The field of robotics has always put its effort on the minimization of the space occupied by the robot, and on making the robot `smarter. `The smartness of a robot is its sensitivity to its surroundings and the user input and its ability to react upon them desirably. Quantum phenomena in robotics make sure that the robots occupy less space and the ability of quantum computation to process the huge amount of information effectively, consequently making the robot smarter. Braitenberg vehicle is a simple circuited robot that moves according to the input that its sensors receive. Building upon that, we propose a quantum robot vehicle that is `smart' enough to understand the complex situations more than that of a simple Braitenberg vehicle and navigate itself as per the obstacles present. It can detect an obstacle-free path and can navigate itself accordingly. It also takes input from the user when there is more than one free path available. When left with no option on the ground, it can airlift itself off the ground. As these vehicles sort of `react to the surrounding conditions, this idea can be used to build artificial life and genetic algorithms, space exploration and deep-earth exploration probes, and a handy tool in defense and intelligence services.

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arXiv2022

Safe Human-Robot Collaborative Transportation via Trust-Driven Role Adaptation

We study a human-robot collaborative transportation task in presence of obstacles. The task for each agent is to carry a rigid object to a common target position, while safely avoiding obstacles and satisfying the compliance and actuation constraints of the other agent. Human and robot do not share the local view of the environment. The human policy either assists the robot when they deem the robot actions safe based on their perception of the environment, or actively leads the task. Using estimated human inputs, the robot plans a trajectory for the transported object by solving a constrained finite time optimal control problem. Sensors on the robot measure the inputs applied by the human. The robot then appropriately applies a weighted combination of the human's applied and its own planned inputs, where the weights are chosen based on the robot's trust value on its estimates of the human's inputs. This allows for a dynamic leader-follower role adaptation of the robot throughout the task. Furthermore, under a low value of trust, if the robot approaches any obstacle potentially unknown to the human, it triggers a safe stopping policy, maintaining safety of the system and signaling a required change in the human's intent. With experimental results, we demonstrate the efficacy of the proposed approach.

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arXiv2022

Printable Flexible Robots for Remote Learning

The COVID-19 pandemic has revealed the importance of digital fabrication to enable online learning, which remains a challenge for robotics courses. We introduce a teaching methodology that allows students to participate remotely in a hands-on robotics course involving the design and fabrication of robots. Our methodology employs 3D printing techniques with flexible filaments to create innovative soft robots; robots are made from flexible, as opposed to rigid, materials. Students design flexible robotic components such as actuators, sensors, and controllers using CAD software, upload their designs to a remote 3D printing station, monitor the print with a web camera, and inspect the components with lab staff before being mailed for testing and assembly. At the end of the course, students will have iterated through several designs and created fluidically-driven soft robots. Our remote teaching methodology enables educators to utilize 3D printing resources to teach soft robotics and cultivate creativity among students to design novel and innovative robots. Our methodology seeks to democratize robotics engineering by decoupling hands-on learning experiences from expensive equipment in the learning environment.

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arXiv2022

NMPC-LBF: Nonlinear MPC with Learned Barrier Function for Decentralized Safe Navigation of Multiple Robots in Unknown Environments

In this paper, we present a decentralized control approach based on a Nonlinear Model Predictive Control (NMPC) method that employs barrier certificates for safe navigation of multiple nonholonomic wheeled mobile robots in unknown environments with static and/or dynamic obstacles. This method incorporates a Learned Barrier Function (LBF) into the NMPC design in order to guarantee safe robot navigation, i.e., prevent robot collisions with other robots and the obstacles. We refer to our proposed control approach as NMPC-LBF. Since each robot does not have a priori knowledge about the obstacles and other robots, we use a Deep Neural Network (DeepNN) running in real-time on each robot to learn the Barrier Function (BF) only from the robot's LiDAR and odometry measurements. The DeepNN is trained to learn the BF that separates safe and unsafe regions. We implemented our proposed method on simulated and actual Turtlebot3 Burger robot(s) in different scenarios. The implementation results show the effectiveness of the NMPC-LBF method at ensuring safe navigation of the robots.

cs.RO↗

arXiv2022

GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots

Recent years have seen a surge in commercially-available and affordable quadrupedal robots, with many of these platforms being actively used in research and industry. As the availability of legged robots grows, so does the need for controllers that enable these robots to perform useful skills. However, most learning-based frameworks for controller development focus on training robot-specific controllers, a process that needs to be repeated for every new robot. In this work, we introduce a framework for training generalized locomotion (GenLoco) controllers for quadrupedal robots. Our framework synthesizes general-purpose locomotion controllers that can be deployed on a large variety of quadrupedal robots with similar morphologies. We present a simple but effective morphology randomization method that procedurally generates a diverse set of simulated robots for training. We show that by training a controller on this large set of simulated robots, our models acquire more general control strategies that can be directly transferred to novel simulated and real-world robots with diverse morphologies, which were not observed during training.

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arXiv2023

Proactive Opinion-Driven Robot Navigation around Human Movers

We propose, analyze, and experimentally verify a new proactive approach for robot social navigation driven by the robot's "opinion" for which way and by how much to pass human movers crossing its path. The robot forms an opinion over time according to nonlinear dynamics that depend on the robot's observations of human movers and its level of attention to these social cues. For these dynamics, it is guaranteed that when the robot's attention is greater than a critical value, deadlock in decision making is broken, and the robot rapidly forms a strong opinion, passing each human mover even if the robot has no bias nor evidence for which way to pass. We enable proactive rapid and reliable social navigation by having the robot grow its attention across the critical value when a human mover approaches. With human-robot experiments we demonstrate the flexibility of our approach and validate our analytical results on deadlock-breaking. We also show that a single design parameter can tune the trade-off between efficiency and reliability in human-robot passing. The new approach has the additional advantage that it does not rely on a predictive model of human behavior.

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arXiv2022

FogROS G: Enabling Secure, Connected and Mobile Fog Robotics with Global Addressability

Fog Robotics renders networked robots with greater mobility, on-demand compute capabilities and better energy efficiency by offloading heavy robotics workloads to nearby Edge and distant Cloud data centers. However, as the de-facto standard for implementing fog robotics applications, Robot Operating System (ROS) and its successor ROS2 fail to provide fog robots with a mobile-friendly and secure communication infrastructure. In this work, we present FogROS G, a secure routing framework that connects robotics software components from different physical locations, networks, Data Distribution Service (DDS) and ROS distributions. FogROS G indexes networked robots with globally unique 256-bit names that remains constant even if the robot roams between multiple administrative network domains. FogROS G leverages Global Data Plane, a global and secure peer-to-peer routing infrastructure between the names, guaranteeing that only authenticated party can send to or receive from the robot. FogROS G adopts a proxy-based design that connect nodes from ROS1 and ROS2 with mainstream DDS vendors; this can be done without any changes to the application code.

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arXiv2022

HeRoSwarm: Fully-Capable Miniature Swarm Robot Hardware Design With Open-Source ROS Support

Experiments using large numbers of miniature swarm robots are desirable to teach, study, and test multi-robot and swarm intelligence algorithms and their applications. To realize the full potential of a swarm robot, it should be capable of not only motion but also sensing, computing, communication, and power management modules with multiple options. Current swarm robot platforms developed for commercial and academic research purposes lack several of these critical attributes by focusing only on a few of these aspects. Therefore, in this paper, we propose the HeRoSwarm, a fully-capable swarm robot platform with open-source hardware and software support. The proposed robot hardware is a low-cost design with commercial off-the-shelf components that uniquely integrates multiple sensing, communication, and computing modalities with various power management capabilities into a tiny footprint. Moreover, our swarm robot with odometry capability with Robot Operating Systems (ROS) support is unique in its kind. This simple yet powerful swarm robot design has been extensively verified with different prototyping variants and multi-robot experimental demonstrations.

cs.RO↗

arXiv2022

On Terrain-Aware Locomotion for Legged Robots

(Simplified Abstract) To accomplish breakthroughs in dynamic whole-body locomotion, legged robots have to be terrain aware. Terrain-Aware Locomotion (TAL) implies that the robot can perceive the terrain with its sensors, and can take decisions based on this information. This thesis presents TAL strategies both from a proprioceptive and an exteroceptive perspective. The strategies are implemented at the level of locomotion planning, control, and state estimation, and using optimization and learning techniques. The first part is on TAL strategies at the Whole-Body Control (WBC) level. We introduce a passive WBC (pWBC) framework that allows the robot to stabilize and walk over challenging terrain while taking into account the terrain geometry (inclination) and friction properties. The pWBC relies on rigid contact assumptions which makes it suitable only for stiff terrain. As a consequence, we introduce Soft Terrain Adaptation aNd Compliance Estimation (STANCE) which is a soft terrain adaptation algorithm that generalizes beyond rigid terrain. The second part of the thesis focuses on vision-based TAL strategies. We present Vision-Based Terrain-Aware Locomotion (ViTAL) which is an online planning strategy that selects the footholds based on the robot capabilities, and the robot pose that maximizes the chances of the robot succeeding in reaching these footholds. ViTAL relies on a set of robot skills that characterizes the capabilities of the robot and its legs. The skills include the robot's ability to assess the terrain's geometry, avoid leg collisions, and avoid reaching kinematic limits. Our strategies are based on optimization and learning methods and are validated on HyQ and HyQReal in simulation and experiment. We show that with the help of these strategies, we can push dynamic legged robots one step closer to being fully autonomous and terrain aware.

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arXiv2022

A Survey of Multi-Agent Human-Robot Interaction Systems

This article presents a survey of literature in the area of Human-Robot Interaction (HRI), specifically on systems containing more than two agents (i.e., having multiple humans and/or multiple robots). We identify three core aspects of ``Multi-agent" HRI systems that are useful for understanding how these systems differ from dyadic systems and from one another. These are the Team structure, Interaction style among agents, and the system's Computational characteristics. Under these core aspects, we present five attributes of HRI systems, namely Team size, Team composition, Interaction model, Communication modalities, and Robot control. These attributes are used to characterize and distinguish one system from another. We populate resulting categories with examples from recent literature along with a brief discussion of their applications and analyze how these attributes differ from the case of dyadic human-robot systems. We summarize key observations from the current literature, and identify challenges and promising areas for future research in this domain. In order to realize the vision of robots being part of the society and interacting seamlessly with humans, there is a need to expand research on multi-human -- multi-robot systems. Not only do these systems require coordination among several agents, they also involve multi-agent and indirect interactions which are absent from dyadic HRI systems. Adding multiple agents in HRI systems requires advanced interaction schemes, behavior understanding and control methods to allow natural interactions among humans and robots. In addition, research on human behavioral understanding in mixed human-robot teams also requires more attention. This will help formulate and implement effective robot control policies in HRI systems with large numbers of heterogeneous robots and humans; a team composition reflecting many real-world scenarios.

cs.RO↗

arXiv2022

Evaluating Multimodal Interaction of Robots Assisting Older Adults

We outline our work on evaluating robots that assist older adults by engaging with them through multiple modalities that include physical interaction. Our thesis is that to increase the effectiveness of assistive robots: 1) robots need to understand and effect multimodal actions, 2) robots should not only react to the human, they need to take the initiative and lead the task when it is necessary. We start by briefly introducing our proposed framework for multimodal interaction and then describe two different experiments with the actual robots. In the first experiment, a Baxter robot helps a human find and locate an object using the Multimodal Interaction Manager (MIM) framework. In the second experiment, a NAO robot is used in the same task, however, the roles of the robot and the human are reversed. We discuss the evaluation methods that were used in these experiments, including different metrics employed to characterize the performance of the robot in each case. We conclude by providing our perspective on the challenges and opportunities for the evaluation of assistive robots for older adults in realistic settings.

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arXiv2022

Influence of collaborative customer service by service robots and clerks in bakery stores

In recent years, various service robots have been introduced in stores as recommendation systems. Previous studies attempted to increase the influence of these robots by improving their social acceptance and trust. However, when such service robots recommend a product to customers in real environments, the effect on the customers is influenced not only by the robot itself, but also by the social influence of the surrounding people such as store clerks. Therefore, leveraging the social influence of the clerks may increase the influence of the robots on the customers. Hence, we compared the influence of robots with and without collaborative customer service between the robots and clerks in two bakery stores. The experimental results showed that collaborative customer service increased the purchase rate of the recommended bread and improved the impression regarding the robot and store experience of the customers. Because the results also showed that the workload required for the clerks to collaborate with the robot was not high, this study suggests that all stores with service robots may show high effectiveness in introducing collaborative customer service.

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arXiv2023

Lifetime-configurable soft robots via photodegradable silicone elastomer composites

Developing soft robots that can control their own life-cycle and degrade on-demand while maintaining hyper-elasticity is a significant research challenge. On-demand degradable soft robots, which conserve their original functionality during operation and rapidly degrade under specific external stimulation, present the opportunity to self-direct the disappearance of temporary robots. This study proposes soft robots and materials that exhibit excellent mechanical stretchability and can degrade under ultraviolet (UV) light by mixing a fluoride-generating diphenyliodonium hexafluorophosphate (DPI-HFP) with a silicone resin. Spectroscopic analysis revealed the mechanism of Si-O-Si backbone cleavage using fluoride ion (F-), which was generated from UV exposed DPI-HFP. Furthermore, photo-differential scanning calorimetry (DSC) based thermal analysis indicated increased decomposition kinetics at increased temperatures. Additionally, we demonstrated a robotics application of this composite by fabricating a gaiting robot. The integration of soft electronics, including strain sensors, temperature sensors, and photodetectors, expanded the robotic functionalities. This study provides a simple yet novel strategy for designing lifecycle mimicking soft robotics that can be applied to reduce soft robotics waste, explore hazardous areas where retrieval of robots is impossible, and ensure hardware security with on-demand destructive material platforms.

cs.RO↗

arXiv2023

On Designing a Learning Robot: Improving Morphology for Enhanced Task Performance and Learning

As robots become more prevalent, optimizing their design for better performance and efficiency is becoming increasingly important. However, current robot design practices overlook the impact of perception and design choices on a robot's learning capabilities. To address this gap, we propose a comprehensive methodology that accounts for the interplay between the robot's perception, hardware characteristics, and task requirements. Our approach optimizes the robot's morphology holistically, leading to improved learning and task execution proficiency. To achieve this, we introduce a Morphology-AGnostIc Controller (MAGIC), which helps with the rapid assessment of different robot designs. The MAGIC policy is efficiently trained through a novel PRIvileged Single-stage learning via latent alignMent (PRISM) framework, which also encourages behaviors that are typical of robot onboard observation. Our simulation-based results demonstrate that morphologies optimized holistically improve the robot performance by 15-20% on various manipulation tasks, and require 25x less data to match human-expert made morphology performance. In summary, our work contributes to the growing trend of learning-based approaches in robotics and emphasizes the potential in designing robots that facilitate better learning.

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