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Robotics

Explore arXiv robotics papers and cs.RO metadata. Search for robot learning, motion planning and control, then check experiments in the source manuscript.

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arXiv2024

A Survey of Behavior Learning Applications in Robotics -- State of the Art and Perspectives

Recent success of machine learning in many domains has been overwhelming, which often leads to false expectations regarding the capabilities of behavior learning in robotics. In this survey, we analyze the current state of machine learning for robotic behaviors. We will give a broad overview of behaviors that have been learned and used on real robots. Our focus is on kinematically or sensorially complex robots. That includes humanoid robots or parts of humanoid robots, for example, legged robots or robotic arms. We will classify presented behaviors according to various categories and we will draw conclusions about what can be learned and what should be learned. Furthermore, we will give an outlook on problems that are challenging today but might be solved by machine learning in the future and argue that classical robotics and other approaches from artificial intelligence should be integrated more with machine learning to form complete, autonomous systems.

cs.RO↗

arXiv2019

Raven: Open Surgical Robotic Platforms

The Raven I and the Raven II surgical robots, as open research platforms, have been serving the robotic surgery research community for ten years. The paper 1) briefly presents the Raven I and the Raven II robots, 2) reviews the recent publications that are built upon the Raven robots, aim to be applied to the Raven robots, or are directly compared with the Raven robots, and 3) uses the Raven robots as a case study to discuss the popular research problems in the research community and the trend of robotic surgery study. Instead of being a thorough literature review, this work only reviews the works formally published in the past three years and uses these recent publications to analyze the research interests, the popular open research problems, and opportunities in the topic of robotic surgery.

cs.RO↗

arXiv2019

Multi-Robot Path Deconfliction through Prioritization by Path Prospects

This work deals with the problem of planning conflict-free paths for mobile robots in cluttered environments. Since centralized, coupled planning algorithms are computationally intractable for large numbers of robots, we consider decoupled planning, in which robots plan their paths sequentially in order of priority. Choosing how to prioritize the robots is a key consideration. State-of-the-art prioritization heuristics, however, do not model the coupling between a robot's mobility and its environment. In this paper, we propose a prioritization rule that can be computed online by each robot independently, and that provides consistent, conflict-free path plans. Our innovation is to formalize a robot's path prospects to reach its goal from its current location. To this end, we consider the number of homology classes of trajectories, and use this as a prioritization rule in our decentralized path planning algorithm, whenever any robots enter negotiation to deconflict path plans. This prioritization rule guarantees a partial ordering over the robot set. We perform simulations that compare our method to five benchmarks, and show that it reaches the highest success rate (w.r.t. completeness), and that it strikes the best balance between makespan and flowtime objectives.

cs.RO↗

arXiv2019

Functional advantages of an adaptive Theory of Mind for robotics: a review of current architectures

Great advancements have been achieved in the field of robotics, however, main challenges remain, including building robots with an adaptive Theory of Mind (ToM). In the present paper, seven current robotic architectures for human-robot interactions were described as well as four main functional advantages of equipping robots with an adaptive ToM. The aim of the present paper was to determine in which way and how often ToM features are integrated in the architectures analyzed, and if they provide robots with the associated functional advantages. Our assessment shows that different methods are used to implement ToM features in robotic architectures. Furthermore, while a ToM for false-belief understanding and tracking is often built in social robotic architectures, a ToM for proactivity, active perception and learning is less common. Nonetheless, progresses towards better adaptive ToM features in robots are warranted to provide them with full access to the advantages of having a ToM resembling that of humans.

cs.RO↗

arXiv2019

Automatic Failure Recovery for End-User Programs on Service Mobile Robots

For service mobile robots to be most effective, it must be possible for non-experts and even end-users to program them to do new tasks. Regardless of the programming method (e.g., by demonstration or traditional programming), robot task programs are challenging to write, because they rely on multiple actions to succeed, including human-robot interactions. Unfortunately, interactions are prone to fail, because a human may perform the wrong action (e.g., if the robot's request is not clear). Moreover, when the robot cannot directly observe the human action, it may not detect the failure until several steps after it occurs. Therefore, writing fault-tolerant robot tasks is beyond the ability of non-experts. This paper presents a principled approach to detect and recover from a broad class of failures that occur in end-user programs on service mobile robots. We present a two-tiered Robot Task Programming Language (RTPL): 1) an expert roboticist uses a specification language to write a probabilistic model of the robot's actions and interactions, and 2) a non-expert then writes an ordinary sequential program for a particular task. The RTPL runtime system executes the task program sequentially, while using the probabilistic model to build a Bayesian network that tracks possible, unobserved failures. If an error is observed, RTPL uses Bayesian inference to find the likely root cause of the error, and then attempts to re-execute a portion of the program for recovery. Our empirical results show that RTPL 1) allows complex tasks to be written concisely, 2) correctly identifies the root cause of failure, and 3) allows multiple tasks to recover from a variety of errors, without task-specific error-recovery code.

cs.RO↗

arXiv2019

Trust and Cognitive Load During Human-Robot Interaction

This paper presents an exploratory study to understand the relationship between a humans' cognitive load, trust, and anthropomorphism during human-robot interaction. To understand the relationship, we created a \say{Matching the Pair} game that participants could play collaboratively with one of two robot types, Husky or Pepper. The goal was to understand if humans would trust the robot as a teammate while being in the game-playing situation that demanded a high level of cognitive load. Using a humanoid vs. a technical robot, we also investigated the impact of physical anthropomorphism and we furthermore tested the impact of robot error rate on subsequent judgments and behavior. Our results showed that there was an inversely proportional relationship between trust and cognitive load, suggesting that as the amount of cognitive load increased in the participants, their ratings of trust decreased. We also found a triple interaction impact between robot-type, error-rate and participant's ratings of trust. We found that participants perceived Pepper to be more trustworthy in comparison with the Husky robot after playing the game with both robots under high error-rate condition. On the contrary, Husky was perceived as more trustworthy than Pepper when it was depicted as featuring a low error-rate. Our results are interesting and call further investigation of the impact of physical anthropomorphism in combination with variable error-rates of the robot.

cs.HC↗

arXiv2019

Omnidirectional Tractable Three Module Robot

This paper introduces the Omnidirectional Tractable Three Module Robot for traversing inside complex pipe networks. The robot consists of three omnidirectional modules fixed 120° apart circumferentially which can rotate about their own axis allowing holonomic motion of the robot. The holonomic motion enables the robot to overcome motion singularity when negotiating T-junctions and further allows the robot to arrive in a preferred orientation while taking turns inside a pipe. We have developed a closed-form kinematic model for the robot in the paper and propose the Motion Singularity Region that the robot needs to avoid while negotiating T-junction. The design and motion capabilities of the robot are demonstrated both by conducting simulations in MSC ADAMS on a simplified lumped-model of the robot and with experiments on its physical embodiment.

cs.RO↗

arXiv2019

A Review of Soft Robots

Traditional robots have rigid links and structures that limit their ability to interact with the dynamics of their immediate environment. For example, conventional robot manipulators with rigid links can only manipulate objects using specific end effectors. These robots often encounter difficulties operating in unstructured and highly congested environments. A variety of biological organisms exhibit complex movement with soft structures devoid of rigid components. Inspired by biology, researchers have been able to design and build soft robots. With a soft structure and redundant degrees of freedom, these robots can be used for delicate tasks in unstructured environments. This review discusses the motivation for soft robots, their design processes as well as their applications and limitations. Soft robots have the ability to operate in unstructured environment due to their inherent potential to exploit morphological computation to adapt to, and interact with, the world in a way that is difficult with rigid systems. Soft robots could be used for operations, ranging from search and rescue operations in a natural disaster relief effort, and of emerging interest is in the field of medical care as seen in personal robots.

cs.RO↗

arXiv2019

Towards Robotic Things in Society

Emerging are so-called smart things embedded with computational, sensing, networking and actuation capabilities, from smart bins to smart park benches, as well as the proliferation of autonomous vehicles and robots in an increasingly wide range of applications. This is not only an increased in automation affecting and hopefully improving daily life, but also calls for thinking about what a society saturated with such robotic things (i.e., smart things and robots) might look like. This paper discusses five aspects of a vision of Internet connected robotic things (or Internet of Robotic Things (IoRT)) occupying and operating in public spaces, from streets, parks to shopping malls. We discuss, highlighting issues, with the notion of an entourage of drones and robots accompanying people in public places, the idea of creating environments or envelopes suitable for robot function, the idea of societies of robotic things, and governance for robotic things in public spaces.

cs.CY↗

arXiv2019

Human Whole-Body Dynamics Estimation for Enhancing Physical Human-Robot Interaction

In the last two decades the scientific community has shown a great interest in understanding and shaping the interaction mechanisms between humans and robots. The interaction implies communication between two dyadic agents and, if the type of interaction is physical, the communication is represented by the set of forces exchanged during the interaction. Within this context, the role of quantifying these forces becomes of pivotal importance for understanding the interaction mechanisms. At the current scientific stage, classical robots are built to act for humans, but the scientific demand is going towards the direction of robots that will have to collaborate with humans. This will be possible by providing the robots with sufficient pieces of information of the agent they are interacting with (i.e., kinematic and dynamic model of the human). In a modern age where humans need the help of robots, apparently in an opposite trend, this thesis attempts to answer the following questions: Do robots need humans? Should robots know their human partners? A tentative answer is provided here in the form of a novel framework for the simultaneous human whole-body motion tracking and dynamics estimation, in a real-time scenario. The framework encompasses a set of body-mounted sensors and a probabilistic algorithm able of estimating physical quantities that in humans are not directly measurable (i.e., torques and internal forces). This thesis is mainly focussed on the paramount role in retrieving the human dynamics estimation but straightforwardly it leaves the door open to the next development step: passing the human dynamics feedback to the robot controllers. This step will enable the robot with the capability to observe and understand the human partner by generating an enhanced (intentional) interaction.

cs.RO↗

arXiv2020

A Versatile Multi-Robot Monte Carlo Tree Search Planner for On-Line Coverage Path Planning

Mobile robots hold great promise in reducing the need for humans to perform jobs such as vacuuming, seeding,harvesting, painting, search and rescue, and inspection. In practice, these tasks must often be done without an exact map of the area and could be completed more quickly through the use of multiple robots working together. The task of simultaneously covering and mapping an area with multiple robots is known as multi-robot on-line coverage and is a growing area of research. Many multi-robot on-line coverage path planning algorithms have been developed as extensions of well established off-line coverage algorithms. In this work we introduce a novel approach to multi-robot on-line coverage path planning based on a method borrowed from game theory and machine learning- Monte Carlo Tree Search. We implement a Monte Carlo Tree Search planner and compare completion times against a Boustrophedon-based on-line multi-robot planner. The MCTS planner is shown to perform on par with the conventional Boustrophedon algorithm in simulations varying the number of robots and the density of obstacles in the map. The versatility of the MCTS planner is demonstrated by incorporating secondary objectives such as turn minimization while performing the same coverage task. The versatility of the MCTS planner suggests it is well suited to many multi-objective tasks that arise in mobile robotics.

cs.MA↗

arXiv2020

Enabling Human-Robot Collaboration via Holistic Human Perception and Partner-Aware Control

As robotic technology advances, the barriers to the coexistence of humans and robots are slowly coming down. Application domains like elderly care, collaborative manufacturing, collaborative manipulation, etc., are considered the need of the hour, and progress in robotics holds the potential to address many societal challenges. The future socio-technical systems constitute of blended workforce with a symbiotic relationship between human and robot partners working collaboratively. This thesis attempts to address some of the research challenges in enabling human-robot collaboration. In particular, the challenge of a holistic perception of a human partner to continuously communicate his intentions and needs in real-time to a robot partner is crucial for the successful realization of a collaborative task. Towards that end, we present a holistic human perception framework for real-time monitoring of whole-body human motion and dynamics. On the other hand, the challenge of leveraging assistance from a human partner will lead to improved human-robot collaboration. In this direction, we attempt at methodically defining what constitutes assistance from a human partner and propose partner-aware robot control strategies to endow robots with the capacity to meaningfully engage in a collaborative task.

cs.RO↗

arXiv2020

SIGVerse: A cloud-based VR platform for research on social and embodied human-robot interaction

Common sense and social interaction related to daily-life environments are considerably important for autonomous robots, which support human activities. One of the practical approaches for acquiring such social interaction skills and semantic information as common sense in human activity is the application of recent machine learning techniques. Although recent machine learning techniques have been successful in realizing automatic manipulation and driving tasks, it is difficult to use these techniques in applications that require human-robot interaction experience. Humans have to perform several times over a long term to show embodied and social interaction behaviors to robots or learning systems. To address this problem, we propose a cloud-based immersive virtual reality (VR) platform which enables virtual human-robot interaction to collect the social and embodied knowledge of human activities in a variety of situations. To realize the flexible and reusable system, we develop a real-time bridging mechanism between ROS and Unity, which is one of the standard platforms for developing VR applications. We apply the proposed system to a robot competition field named RoboCup@Home to confirm the feasibility of the system in a realistic human-robot interaction scenario. Through demonstration experiments at the competition, we show the usefulness and potential of the system for the development and evaluation of social intelligence through human-robot interaction. The proposed VR platform enables robot systems to collect social experiences with several users in a short time. The platform also contributes in providing a dataset of social behaviors, which would be a key aspect for intelligent service robots to acquire social interaction skills based on machine learning techniques.

cs.RO↗

arXiv2020

I can attend a meeting too! Towards a human-like telepresence avatar robot to attend meeting on your behalf

Telepresence robots are used in various forms in various use-cases that helps to avoid physical human presence at the scene of action. In this work, we focus on a telepresence robot that can be used to attend a meeting remotely with a group of people. Unlike a one-to-one meeting, participants in a group meeting can be located at a different part of the room, especially in an informal setup. As a result, all of them may not be at the viewing angle of the robot, a.k.a. the remote participant. In such a case, to provide a better meeting experience, the robot should localize the speaker and bring the speaker at the center of the viewing angle. Though sound source localization can easily be done using a microphone-array, bringing the speaker or set of speakers at the viewing angle is not a trivial task. First of all, the robot should react only to a human voice, but not to the random noises. Secondly, if there are multiple speakers, to whom the robot should face or should it rotate continuously with every new speaker? Lastly, most robotic platforms are resource-constrained and to achieve a real-time response, i.e., avoiding network delay, all the algorithms should be implemented within the robot itself. This article presents a study and implementation of an attention shifting scheme in a telepresence meeting scenario which best suits the needs and expectations of the collocated and remote attendees. We define a policy to decide when a robot should rotate and how much based on real-time speaker localization. Using user satisfaction study, we show the efficacy and usability of our system in the meeting scenario. Moreover, our system can be easily adapted to other scenarios where multiple people are located.

cs.RO↗

arXiv2020

A Three-limb Teleoperated Robotic System with Foot Control for Flexible Endoscopic Surgery

Flexible endoscopy requires high skills to manipulate both the endoscope and associated instruments. In most robotic flexible endoscopic systems, the endoscope and instruments are controlled separately by two operators, which may result in communication errors and inefficient operation. We present a novel teleoperation robotic endoscopic system that can be commanded by a surgeon alone. This 13 degrees-of-freedom (DoF) system integrates a foot-controlled robotic flexible endoscope and two hand-controlled robotic endoscopic instruments (a robotic grasper and a robotic cauterizing hook). A foot-controlled human-machine interface maps the natural foot gestures to the 4-DoF movements of the endoscope, and two hand-controlled interfaces map the movements of the two hands to the two instruments individually. The proposed robotic system was validated in an ex-vivo experiment carried out by six subjects, where foot control was also compared with a sequential clutch-based hand control scheme. The participants could successfully teleoperate the endoscope and the two instruments to cut the tissues at scattered target areas in a porcine stomach. Foot control yielded 43.7% faster task completion and required less mental effort as compared to the clutch-based hand control scheme. The system introduced in this paper is intuitive for three-limb manipulation even for operators without experience of handling the endoscope and robotic instruments. This three-limb teleoperated robotic system enables one surgeon to intuitively control three endoscopic tools which normally require two operators, leading to reduced manpower, less communication errors, and improved efficiency.

cs.RO↗

arXiv2020

Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception

Autonomous or teleoperated robots have been playing increasingly important roles in civil applications in recent years. Across the different civil domains where robots can support human operators, one of the areas where they can have more impact is in search and rescue (SAR) operations. In particular, multi-robot systems have the potential to significantly improve the efficiency of SAR personnel with faster search of victims, initial assessment and mapping of the environment, real-time monitoring and surveillance of SAR operations, or establishing emergency communication networks, among other possibilities. SAR operations encompass a wide variety of environments and situations, and therefore heterogeneous and collaborative multi-robot systems can provide the most advantages. In this paper, we review and analyze the existing approaches to multi-robot SAR support, from an algorithmic perspective and putting an emphasis on the methods enabling collaboration among the robots as well as advanced perception through machine vision and multi-agent active perception. Furthermore, we put these algorithms in the context of the different challenges and constraints that various types of robots (ground, aerial, surface or underwater) encounter in different SAR environments (maritime, urban, wilderness or other post-disaster scenarios). This is, to the best of our knowledge, the first review considering heterogeneous SAR robots across different environments, while giving two complimentary points of view: control mechanisms and machine perception. Based on our review of the state-of-the-art, we discuss the main open research questions, and outline our insights on the current approaches that have potential to improve the real-world performance of multi-robot SAR systems.

cs.RO↗

arXiv2020

Co-Evolution of Multi-Robot Controllers and Task Cues for Off-World Open Pit Mining

Robots are ideal for open-pit mining on the Moon as its a dull, dirty, and dangerous task. The challenge is to scale up productivity with an ever-increasing number of robots. This paper presents a novel method for developing scalable controllers for use in multi-robot excavation and site-preparation scenarios. The controller starts with a blank slate and does not require human-authored operations scripts nor detailed modeling of the kinematics and dynamics of the excavator. The 'Artificial Neural Tissue' (ANT) architecture is used as a control system for autonomous robot teams to perform resource gathering. This control architecture combines a variable-topology neural-network structure with a coarse-coding strategy that permits specialized areas to develop in the tissue. Our work in this field shows that fleets of autonomous decentralized robots have an optimal operating density. Too few robots result in insufficient labor, while too many robots cause antagonism, where the robots undo each other's work and are stuck in gridlock. In this paper, we explore the use of templates and task cues to improve group performance further and minimize antagonism. Our results show light beacons and task cues are effective in sparking new and innovative solutions at improving robot performance when placed under stressful situations such as severe time-constraint.

cs.RO↗

arXiv2020

Blockchain for Multi-Robot Collaboration to Combat COVID-19 and Future Pandemics

This conceptual paper overviews how blockchain technology is involving the operation of multi-robot collaboration for combating COVID-19 and future pandemics. Robots are a promising technology for providing many tasks such as spraying, disinfection, cleaning, treating, detecting high body temperature/mask absence, and delivering goods and medical supplies experiencing an epidemic COVID-19. For combating COVID-19, many heterogeneous and homogenous robots are required to perform different tasks for supporting different purposes in the quarantine area. Controlling and decentralizing multi-robot play a vital role in combating COVID-19 by reducing human interaction, monitoring, delivering goods. Blockchain technology can manage multi-robot collaboration in a decentralized fashion, improve the interaction among them to exchange information, share representation, share goals, and trust. We highlight the challenges and provide the tactical solutions enabled by integrating blockchain and multi-robot collaboration to combat COVID-19 pandemic. The framework of our conceptual proposed can increase the intelligence, decentralization, and autonomous operations of connected multi-robot collaboration in the blockchain network. We overview blockchain potential benefits to defining a framework of multi-robot collaboration applications to combat COVID-19 epidemics such as monitoring and outdoor and hospital End to End (E2E) delivery systems. Furthermore, we discuss the challenges and opportunities of integrated blockchain, multi-robot collaboration, and the Internet of Things (IoT) for combating COVID-19 and future pandemics.

cs.RO↗