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Robotics

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arXiv2021

Navigating A Mobile Robot Using Switching Distributed Sensor Networks

This paper proposes a method to navigate a mobile robot by estimating its state over a number of distributed sensor networks (DSNs) such that it can successively accomplish a sequence of tasks, i.e., its state enters each targeted set and stays inside no less than the desired time, under a resource-aware, time-efficient, and computation- and communication-constrained setting.We propose a new robot state estimation and navigation architecture, which integrates an event-triggered task-switching feedback controller for the robot and a two-time-scale distributed state estimator for each sensor. The architecture has three major advantages over existing approaches: First, in each task only one DSN is active for sensing and estimating the robot state, and for different tasks the robot can switch the active DSN by taking resource saving and system performance into account; Second, the robot only needs to communicate with one active sensor at each time to obtain its state information from the active DSN; Third, no online optimization is required. With the controller, the robot is able to accomplish a task by following a reference trajectory and switch to the next task when an event-triggered condition is fulfilled. With the estimator, each active sensor is able to estimate the robot state. Under proper conditions, we prove that the state estimation error and the trajectory tracking deviation are upper bounded by two time-varying sequences respectively, which play an essential role in the event-triggered condition. Furthermore, we find a sufficient condition for accomplishing a task and provide an upper bound of running time for the task. Numerical simulations of an indoor robot's localization and navigation are provided to validate the proposed architecture.

cs.RO↗

arXiv2021

AuraSense: Robot Collision Avoidance by Full Surface Proximity Detection

Perceiving obstacles and avoiding collisions is fundamental to the safe operation of a robot system, particularly when the robot must operate in highly dynamic human environments. Proximity detection using on-robot sensors can be used to avoid or mitigate impending collisions. However, existing proximity sensing methods are orientation and placement dependent, resulting in blind spots even with large numbers of sensors. In this paper, we introduce the phenomenon of the Leaky Surface Wave (LSW), a novel sensing modality, and present AuraSense, a proximity detection system using the LSW. AuraSense is the first system to realize no-dead-spot proximity sensing for robot arms. It requires only a single pair of piezoelectric transducers, and can easily be applied to off-the-shelf robots with minimal modifications. We further introduce a set of signal processing techniques and a lightweight neural network to address the unique challenges in using the LSW for proximity sensing. Finally, we demonstrate a prototype system consisting of a single piezoelectric element pair on a robot manipulator, which validates our design. We conducted several micro benchmark experiments and performed more than 2000 on-robot proximity detection trials with various potential robot arm materials, colliding objects, approach patterns, and robot movement patterns. AuraSense achieves 100% and 95.3% true positive proximity detection rates when the arm approaches static and mobile obstacles respectively, with a true negative rate over 99%, showing the real-world viability of this system.

cs.RO↗

arXiv2021

A Predictive Application Offloading Algorithm Using Small Datasets for Cloud Robotics

Many robotic applications that are critical for robot performance require immediate feedback, hence execution time is a critical concern. Furthermore, it is common that robots come with a fixed quantity of hardware resources; if an application requires more computational resources than the robot can accommodate, its onboard execution might be extended to a degree that degrades the robot performance. Cloud computing, on the other hand, features on-demand computational resources; by enabling robots to leverage those resources, application execution time can be reduced. The key to enabling robot use of cloud computing is designing an efficient offloading algorithm that makes optimum use of the robot onboard capabilities and also forms a quick consensus on when to offload without any prior knowledge or information about the application. In this paper, we propose a predictive algorithm to anticipate the time needed to execute an application for a given application data input size with the help of a small number of previous observations. To validate the algorithm, we train it on the previous N observations, which include independent (input data size) and dependent (execution time) variables. To understand how algorithm performance varies in terms of prediction accuracy and error, we tested various N values using linear regression and a mobile robot path planning application. From our experiments and analysis, we determined the algorithm to have acceptable error and prediction accuracy when N>40.

cs.RO↗

arXiv2021

Toolbox Release: A WiFi-Based Relative Bearing Sensor for Robotics

This paper presents the WiFi-Sensor-for-Robotics (WSR) toolbox, an open source C++ framework. It enables robots in a team to obtain relative bearing to each other, even in non-line-of-sight (NLOS) settings which is a very challenging problem in robotics. It does so by analyzing the phase of their communicated WiFi signals as the robots traverse the environment. This capability, based on the theory developed in our prior works, is made available for the first time as an opensource tool. It is motivated by the lack of easily deployable solutions that use robots' local resources (e.g WiFi) for sensing in NLOS. This has implications for localization, ad-hoc robot networks, and security in multi-robot teams, amongst others. The toolbox is designed for distributed and online deployment on robot platforms using commodity hardware and on-board sensors. We also release datasets demonstrating its performance in NLOS and line-of-sight (LOS) settings for a multi-robot localization usecase. Empirical results show that the bearing estimation from our toolbox achieves mean accuracy of 5.10 degrees. This leads to a median error of 0.5m and 0.9m for localization in LOS and NLOS settings respectively, in a hardware deployment in an indoor office environment.

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arXiv2021

Mechanics-based Analysis on Flagellated Robots

We explore the locomotion of soft robots in granular medium (GM) resulting from the elastic deformation of slender rods. A low-cost, rapidly fabricable robot inspired by the physiological structure of bacteria is presented. It consists of a rigid head, with a motor and batteries embedded, and multiple elastic rods (our model for flagella) to investigate locomotion in GM. The elastic flagella are rotated at one end by the motor, and they deform due to the drag from GM, propelling the robot. The external drag is determined by the flagellar shape, while the latter changes due to the competition between external loading and elastic forces. In this coupled fluid-structure interaction problem, we observe that increasing the number of flagella can decrease or increase the propulsive speed of the robot, depending on the physical parameters of the system. This nonlinearity in the functional relation between propulsion and the parameters of this simple robot motivates us to fundamentally analyze its mechanics using theory, numerical simulation, and experiments. We present a simple Euler-Bernoulli beam theory-based analytical framework that is capable of qualitatively capturing both cases. Theoretical prediction quantitatively matches experiments when the flagellar deformation is small. To account for the geometrically nonlinear deformation often encountered in soft robots and microbes, we implement a simulation framework that incorporates discrete differential geometry-based simulations of elastic rods, a resistive force theory-based model for drag, and a modified Stokes law for the hydrodynamics of the robot head. Comparison with experimental data indicates that the simulations can quantitatively predict robotic motion. Overall, the theoretical and numerical tools presented in this paper can shed light on the design and control of this class of articulated robots in granular or fluid media.

cs.RO↗

arXiv2022

A General, Evolution-Inspired Reward Function for Social Robotics

The field of social robotics will likely need to depart from a paradigm of designed behaviours and imitation learning and adopt modern reinforcement learning (RL) methods to enable robots to interact fluidly and efficaciously with humans. In this paper, we present the Social Reward Function as a mechanism to provide (1) a real-time, dense reward function necessary for the deployment of RL agents in social robotics, and (2) a standardised objective metric for comparing the efficacy of different social robots. The Social Reward Function is designed to closely mimic those genetically endowed social perception capabilities of humans in an effort to provide a simple, stable and culture-agnostic reward function. Presently, datasets used in social robotics are either small or significantly out-of-domain with respect to social robotics. The use of the Social Reward Function will allow larger in-domain datasets to be collected close to the behaviour policy of social robots, which will allow both further improvements to reward functions and to the behaviour policies of social robots. We believe this will be the key enabler to developing efficacious social robots in the future.

cs.RO↗

arXiv2022

Air-Releasable Soft Robots for Explosive Ordnance Disposal

The demining of landmines using drones is challenging; air-releasable payloads are typically non-intelligent (e.g., water balloons or explosives) and deploying them at even low altitudes (~6 meter) is inherently inaccurate due to complex deployment trajectories and constrained visual awareness by the drone pilot. Soft robotics offers a unique approach for aerial demining, namely due to the robust, low-cost, and lightweight designs of soft robots. Instead of non-intelligent payloads, here, we propose the use of air-releasable soft robots for demining. We developed a full system consisting of an unmanned aerial vehicle retrofitted to a soft robot carrier including a custom-made deployment mechanism, and an air-releasable, lightweight (296 g), untethered soft hybrid robot with integrated electronics that incorporates a new type of a vacuum-based flasher roller actuator system. We demonstrate a deployment cycle in which the drone drops the soft robotic hybrid from an altitude of 4.5 m meters and after which the robot approaches a dummy landmine. By deploying soft robots at points of interest, we can transition soft robotic technologies from the laboratory to real-world environments.

cs.RO↗

arXiv2022

Efficient Task Allocation in Smart Warehouses with Multi-delivery Stations and Heterogeneous Robots

The task allocation problem in multi-robot systems (MRTA) is an NP-hard problem whose viable solutions are usually found by heuristic algorithms. Considering the increasing need of improvement on logistics, the use of robots for increasing the efficiency of logistics warehouses is becoming a requirement. In a smart warehouse the main tasks consist of employing a fleet of automated picking and mobile robots that coordinate by picking up items from a set of orders from the shelves and dropping them at the delivery stations. Two aspects generally justify multi-robot task allocation complexity: (i) environmental aspects, such as multi-delivery stations and dispersed robots (since they remain in constant motion) and (ii) fleet heterogeneity, where robots' traffic speed and capacity loads are different from each other. Despite these properties have been widely researched in the literature, they usually are investigated separately. This work proposes a scalable and efficient task allocation algorithm for smart warehouses with multi-delivery stations and heterogeneous fleets. Our strategy employs a novel cost estimator, which computes costs as a function of the robots' variable characteristics and capacity while they receive new tasks. For validating the strategy a series of experiments is performed simulating the operation of smart warehouses with multiple delivery stations and heteregenous fleets. The results show that our strategy generates routes costing up to $33$\% less than the routes generated by a state-of-the-art task allocation algorithm and $96$\% faster in test instances representing our target scenario. Considering single-delivery stations and non-dispersed robots, we reduced the number of robots by up to $18$\%, allocating tasks $92$\% faster, and generating routes whose costs are statistically similar to the routes generated by the state-of-the-art algorithm.

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arXiv2022

Robot Hacking Manual (RHM)

Robots are often shipped insecure and in some cases fully unprotected. The rationale behind is fourfold: first, defensive security mechanisms for robots are still on their early stages, not covering the complete threat landscape. Second, the inherent complexity of robotic systems makes their protection costly, both technically and economically. Third, robot vendors do not generally take responsibility in a timely manner, extending the zero-days exposure window (time until mitigation of a zero-day) to several years on average. Fourth, contrary to the common-sense expectations in 21st century and similar to Ford in the 1920s with cars, most robot manufacturers oppose or difficult robot repairs. The Robot Hacking Manual (RHM) is an introductory series about cybersecurity for robots, with an attempt to provide comprehensive case studies and step-by-step tutorials with the intent to raise awareness in the field and highlight the importance of taking a security-first approach. The material available here is also a personal learning attempt and it's disconnected from any particular organization. Content is provided as is and by no means it's encouraged or promoted the unauthorized tampering of robotic systems or related technologies.

cs.RO↗

arXiv2022

Design and Evaluation of an Augmented Reality Head-Mounted Display Interface for Human Robot Teams Collaborating in Physically Shared Manufacturing Tasks

We provide an experimental evaluation of a wearable augmented reality (AR) system we have developed for human-robot teams working on tasks requiring collaboration in shared physical workspace. Recent advances in AR technology have facilitated the development of more intuitive user interfaces for many human-robot interaction applications. While it has been anticipated that AR can provided a more intuitive interface to robot assistants helping human workers in various manufacturing scenarios, existing studies in robotics have been largely limited to teleoperation and programming. Industry 5.0 envisions cooperation between human and robot working in teams. Indeed, there exist many industrial task that can benefit from human-robot collaboration. A prime example is high-value composite manufacturing. Working with our industry partner towards this example application, we evaluated our AR interface design for shared physical workspace collaboration in human-robot teams. We conducted a multi-dimensional analysis of our interface using establish metrics. Results from our user study (n=26) show that subjectively, the AR interface feels more novel and a standard joystick interface feels more dependable to users. However, the AR interface was found to reduce physical demand and task completion time, while increasing robot utilization. Furthermore, user's freedom of choice to collaborate with the robot may also affect the perceived usability of the system.

cs.RO↗

arXiv2023

SERA: Safe and Efficient Reactive Obstacle Avoidance for Collaborative Robotic Planning in Unstructured Environments

Safe and efficient collaboration among multiple robots in unstructured environments is increasingly critical in the era of Industry 4.0. However, achieving robust and autonomous collaboration among humans and other robots requires modern robotic systems to have effective proximity perception and reactive obstacle avoidance. In this paper, we propose a novel methodology for reactive whole-body obstacle avoidance that ensures conflict-free robot-robot interactions even in dynamic environment. Unlike existing approaches based on Jacobian-type, sampling based or geometric techniques, our methodology leverages the latest deep learning advances and topological manifold learning, enabling it to be readily generalized to other problem settings with high computing efficiency and fast graph traversal techniques. Our approach allows a robotic arm to proactively avoid obstacles of arbitrary 3D shapes without direct contact, a significant improvement over traditional industrial cobot settings. To validate our approach, we implement it on a robotic platform consisting of dual 6-DoF robotic arms with optimized proximity sensor placement, capable of working collaboratively with varying levels of interference. Specifically, one arm performs reactive whole-body obstacle avoidance while achieving its pre-determined objective, while the other arm emulates the presence of a human collaborator with independent and potentially adversarial movements. Our methodology provides a robust and effective solution for safe human-robot collaboration in non-stationary environments.

cs.RO↗

arXiv2022

AAAI SSS-22 Symposium on Closing the Assessment Loop: Communicating Proficiency and Intent in Human-Robot Teaming

The proposed symposium focuses understanding, modeling, and improving the efficacy of (a) communicating proficiency from human to robot and (b) communicating intent from a human to a robot. For example, how should a robot convey predicted ability on a new task? How should it report performance on a task that was just completed? How should a robot adapt its proficiency criteria based on human intentions and values? Communities in AI, robotics, HRI, and cognitive science have addressed related questions, but there are no agreed upon standards for evaluating proficiency and intent-based interactions. This is a pressing challenge for human-robot interaction for a variety of reasons. Prior work has shown that a robot that can assess its performance can alter human perception of the robot and decisions on control allocation. There is also significant evidence in robotics that accurately setting human expectations is critical, especially when proficiency is below human expectations. Moreover, proficiency assessment depends on context and intent, and a human teammate might increase or decrease performance standards, adapt tolerance for risk and uncertainty, demand predictive assessments that affect attention allocation, or otherwise reassess or adapt intent.

cs.AI↗

arXiv2022

Factory: Fast Contact for Robotic Assembly

Robotic assembly is one of the oldest and most challenging applications of robotics. In other areas of robotics, such as perception and grasping, simulation has rapidly accelerated research progress, particularly when combined with modern deep learning. However, accurately, efficiently, and robustly simulating the range of contact-rich interactions in assembly remains a longstanding challenge. In this work, we present Factory, a set of physics simulation methods and robot learning tools for such applications. We achieve real-time or faster simulation of a wide range of contact-rich scenes, including simultaneous simulation of 1000 nut-and-bolt interactions. We provide $60$ carefully-designed part models, 3 robotic assembly environments, and 7 robot controllers for training and testing virtual robots. Finally, we train and evaluate proof-of-concept reinforcement learning policies for nut-and-bolt assembly. We aim for Factory to open the doors to using simulation for robotic assembly, as well as many other contact-rich applications in robotics. Please see https://sites.google.com/nvidia.com/factory for supplementary content, including videos.

cs.RO↗

arXiv2022

LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments

Search and rescue with a team of heterogeneous mobile robots in unknown and large-scale underground environments requires high-precision localization and mapping. This crucial requirement is faced with many challenges in complex and perceptually-degraded subterranean environments, as the onboard perception system is required to operate in off-nominal conditions (poor visibility due to darkness and dust, rugged and muddy terrain, and the presence of self-similar and ambiguous scenes). In a disaster response scenario and in the absence of prior information about the environment, robots must rely on noisy sensor data and perform Simultaneous Localization and Mapping (SLAM) to build a 3D map of the environment and localize themselves and potential survivors. To that end, this paper reports on a multi-robot SLAM system developed by team CoSTAR in the context of the DARPA Subterranean Challenge. We extend our previous work, LAMP, by incorporating a single-robot front-end interface that is adaptable to different odometry sources and lidar configurations, a scalable multi-robot front-end to support inter- and intra-robot loop closure detection for large scale environments and multi-robot teams, and a robust back-end equipped with an outlier-resilient pose graph optimization based on Graduated Non-Convexity. We provide a detailed ablation study on the multi-robot front-end and back-end, and assess the overall system performance in challenging real-world datasets collected across mines, power plants, and caves in the United States. We also release our multi-robot back-end datasets (and the corresponding ground truth), which can serve as challenging benchmarks for large-scale underground SLAM.

cs.RO↗

arXiv2022

The Before, During, and After of Multi-Robot Deadlock

Collision avoidance for multirobot systems is a well-studied problem. Recently, control barrier functions (CBFs) have been proposed for synthesizing controllers that guarantee collision avoidance and goal stabilization for multiple robots. However, it has been noted that reactive control synthesis methods (such as CBFs) are prone to \textit{deadlock}, an equilibrium of system dynamics that causes the robots to stall before reaching their goals. In this paper, we analyze the closed-loop dynamics of robots using CBFs, to characterize controller parameters, initial conditions, and goal locations that invariably lead the system to deadlock. Using tools from duality theory, we derive geometric properties of robot configurations of an $N$ robot system once it is in deadlock and we justify them using the mechanics interpretation of KKT conditions. Our key deductions are that 1) system deadlock is characterized by a force-equilibrium on robots and 2) deadlock occurs to ensure safety when safety is on the brink of being violated. These deductions allow us to interpret deadlock as a subset of the state space, and we show that this set is non-empty and located on the boundary of the safe set. By exploiting these properties, we analyze the number of admissible robot configurations in deadlock and develop a provably-correct decentralized algorithm for deadlock resolution to safely deliver the robots to their goals. This algorithm is validated in simulations as well as experimentally on Khepera-IV robots.

cs.RO↗

arXiv2023

Variational Meta Reinforcement Learning for Social Robotics

With the increasing presence of robots in our every-day environments, improving their social skills is of utmost importance. Nonetheless, social robotics still faces many challenges. One bottleneck is that robotic behaviors need to be often adapted as social norms depend strongly on the environment. For example, a robot should navigate more carefully around patients in a hospital compared to workers in an office. In this work, we investigate meta-reinforcement learning (meta-RL) as a potential solution. Here, robot behaviors are learned via reinforcement learning where a reward function needs to be chosen so that the robot learns an appropriate behavior for a given environment. We propose to use a variational meta-RL procedure that quickly adapts the robots' behavior to new reward functions. As a result, given a new environment different reward functions can be quickly evaluated and an appropriate one selected. The procedure learns a vectorized representation for reward functions and a meta-policy that can be conditioned on such a representation. Given observations from a new reward function, the procedure identifies its representation and conditions the meta-policy to it. While investigating the procedures' capabilities, we realized that it suffers from posterior collapse where only a subset of the dimensions in the representation encode useful information resulting in a reduced performance. Our second contribution, a radial basis function (RBF) layer, partially mitigates this negative effect. The RBF layer lifts the representation to a higher dimensional space, which is more easily exploitable for the meta-policy. We demonstrate the interest of the RBF layer and the usage of meta-RL for social robotics on four robotic simulation tasks.

cs.RO↗

arXiv2022

Gender and Robots: A Literature Review

Here, I ask what we can learn about how gender affects how people engage with robots. I review 46 empirical studies of social robots, published 2018 or earlier, which report on the gender of their participants or the perceived or intended gender of the robot, or both, and perform some analysis with respect to either participant or robot gender. From these studies, I find that robots are by default perceived as male, that robots absorb human gender stereotypes, and that men tend to engage with robots more than women. I highlight open questions about how such gender effects may be different in younger participants, and whether one should seek to match the gender of the robot to the gender of the participant to ensure positive interaction outcomes. I conclude by suggesting that future research should: include gender diverse participant pools, include non-binary participants, rely on self-identification for discerning gender rather than researcher perception, control for known covariates of gender, test for different study outcomes with respect to gender, and test whether the robot used was perceived as gendered by participants. I include an appendix with a narrative summary of gender-relevant findings from each of the 46 papers to aid in future literature reviews.

cs.RO↗

arXiv2022

Robot-Assisted Nuclear Disaster Response: Report and Insights from a Field Exercise

This paper reports on insights by robotics researchers that participated in a 5-day robot-assisted nuclear disaster response field exercise conducted by Kerntechnische Hilfdienst GmbH (KHG) in Karlsruhe, Germany. The German nuclear industry established KHG to provide a robot-assisted emergency response capability for nuclear accidents. We present a systematic description of the equipment used; the robot operators' training program; the field exercise and robot tasks; and the protocols followed during the exercise. Additionally, we provide insights and suggestions for advancing disaster response robotics based on these observations. Specifically, the main degradation in performance comes from the cognitive and attentional demands on the operator. Furthermore, robotic platforms and modules should aim to be robust and reliable in addition to their ease of use. Last, as emergency response stakeholders are often skeptical about using autonomous systems, we suggest adopting a variable autonomy paradigm to integrate autonomous robotic capabilities with the human-in-the-loop gradually. This middle ground between teleoperation and autonomy can increase end-user acceptance while directly alleviating some of the operator's robot control burden and maintaining the resilience of the human-in-the-loop.

cs.RO↗