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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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arXiv2022

Adaptive Admittance Control for Safety-Critical Physical Human Robot Collaboration

Physical human-robot collaboration requires strict safety guarantees since robots and humans work in a shared workspace. This letter presents a novel control framework to handle safety-critical position-based constraints for human-robot physical interaction. The proposed methodology is based on admittance control, exponential control barrier functions (ECBFs) and quadratic program (QP) to achieve compliance during the force interaction between human and robot, while simultaneously guaranteeing safety constraints. In particular, the formulation of admittance control is rewritten as a second-order nonlinear control system, and the interaction forces between humans and robots are regarded as the control input. A virtual force feedback for admittance control is provided in real-time by using the ECBFs-QP framework as a compensator of the external human forces. A safe trajectory is therefore derived from the proposed adaptive admittance control scheme for a low-level controller to track. The innovation of the proposed approach is that the proposed controller will enable the robot to comply with human forces with natural fluidity without violation of any safety constraints even in cases where human external forces incidentally force the robot to violate constraints. The effectiveness of our approach is demonstrated in simulation studies on a two-link planar robot manipulator.

cs.RO↗

arXiv2022

Robotic Exercise Trainer: How Failures and T-HRI Levels Affect User Acceptance and Trust

Physical activity is important for health and wellbeing, but only few fulfill the World Health Organization's criteria for physical activity. The development of a robotic exercise trainer can assist in increasing training accessibility and motivation. The acceptance and trust of users are crucial for the successful implementation of such an assistive robot. This can be affected by the transparency of the robotic system and the robot's performance, specifically, its failures. The study presents an initial investigation into the transparency levels as related to the task, human, robot, and interaction (T-HRI), with robot behavior adjusted accordingly. A failure in robot performance during part of the experiments allowed to analyze the effect of the T-HRI levels as related to failures. Participants who experienced failure in the robot's performance demonstrated a lower level of acceptance and trust than those who did not experience this failure. In addition, there were differences in acceptance measures between T-HRI levels and participant groups, suggesting several directions for future research.

cs.RO↗

arXiv2023

Timor Python: A Toolbox for Industrial Modular Robotics

Modular Reconfigurable Robots (MRRs) represent an exciting path forward for industrial robotics, opening up new possibilities for robot design. Compared to monolithic manipulators, they promise greater flexibility, improved maintainability, and cost-efficiency. However, there is no tool or standardized way to model and simulate assemblies of modules in the same way it has been done for robotic manipulators for decades. We introduce the Toolbox for Industrial Modular Robotics (Timor), a Python toolbox to bridge this gap and integrate modular robotics into existing simulation and optimization pipelines. Our open-source library offers model generation and task-based configuration optimization for MRRs. It can easily be integrated with existing simulation tools - not least by offering URDF export of arbitrary modular robot assemblies. Moreover, our experimental study demonstrates the effectiveness of Timor as a tool for designing modular robots optimized for specific use cases.

cs.RO↗

arXiv2022

Multi-Robot-Assisted Human Crowd Evacuation using Navigation Velocity Fields

This work studies a robot-assisted crowd evacuation problem where we control a small group of robots to guide a large human crowd to safe locations. The challenge lies in how to model human-robot interactions and design robot controls to indirectly control a human population that significantly outnumbers the robots. To address the challenge, we treat the crowd as a continuum and formulate the evacuation objective as driving the crowd density to target locations. We propose a novel mean-field model which consists of a family of microscopic equations that explicitly model how human motions are locally guided by the robots and an associated macroscopic equation that describes how the crowd density is controlled by the navigation velocity fields generated by all robots. Then, we design density feedback controllers for the robots to dynamically adjust their states such that the generated navigation velocity fields drive the crowd density to a target density. Stability guarantees of the proposed controllers are proven. Agent-based simulations are included to evaluate the proposed evacuation algorithms.

cs.RO↗

arXiv2022

HyperPalm: DNN-based hand gesture recognition interface for intelligent communication with quadruped robot in 3D space

Nowadays, autonomous mobile robots support people in many areas where human presence either redundant or too dangerous. They have successfully proven themselves in expeditions, gas industry, mines, warehouses, etc. However, even legged robots may stuck in rough terrain conditions requiring human cognitive abilities to navigate the system. While gamepads and keyboards are convenient for wheeled robot control, the quadruped robot in 3D space can move along all linear coordinates and Euler angles, requiring at least 12 buttons for independent control of their DoF. Therefore, more convenient interfaces of control are required. In this paper we present HyperPalm: a novel gesture interface for intuitive human-robot interaction with quadruped robots. Without additional devices, the operator has full position and orientation control of the quadruped robot in 3D space through hand gesture recognition with only 5 gestures and 6 DoF hand motion. The experimental results revealed to classify 5 static gestures with high accuracy (96.5%), accurately predict the position of the 6D position of the hand in three-dimensional space. The absolute linear deviation Root mean square deviation (RMSD) of the proposed approach is 11.7 mm, which is almost 50% lower than for the second tested approach, the absolute angular deviation RMSD of the proposed approach is 2.6 degrees, which is almost 27% lower than for the second tested approach. Moreover, the user study was conducted to explore user's subjective experience from human-robot interaction through the proposed gesture interface. The participants evaluated their interaction with HyperPalm as intuitive (2.0), not causing frustration (2.63), and requiring low physical demand (2.0).

cs.RO↗

arXiv2022

Fingerprinting Robot Movements via Acoustic Side Channel

In this paper, we present an acoustic side channel attack which makes use of smartphone microphones recording a robot in operation to exploit acoustic properties of the sound to fingerprint a robot's movements. In this work we consider the possibility of an insider adversary who is within physical proximity of a robotic system (such as a technician or robot operator), equipped with only their smartphone microphone. Through the acoustic side-channel, we demonstrate that it is indeed possible to fingerprint not only individual robot movements within 3D space, but also patterns of movements which could lead to inferring the purpose of the movements (i.e. surgical procedures which a surgical robot is undertaking) and hence, resulting in potential privacy violations. Upon evaluation, we find that individual robot movements can be fingerprinted with around 75% accuracy, decreasing slightly with more fine-grained movement meta-data such as distance and speed. Furthermore, workflows could be reconstructed with around 62% accuracy as a whole, with more complex movements such as pick-and-place or packing reconstructed with near perfect accuracy. As well as this, in some environments such as surgical settings, audio may be recorded and transmitted over VoIP, such as for education/teaching purposes or in remote telemedicine. The question here is, can the same attack be successful even when VoIP communication is employed, and how does packet loss impact the captured audio and the success of the attack? Using the same characteristics of acoustic sound for plain audio captured by the smartphone, the attack was 90% accurate in fingerprinting VoIP samples on average, 15% higher than the baseline without the VoIP codec employed. This opens up new research questions regarding anonymous communications to protect robotic systems from acoustic side channel attacks via VoIP communication networks.

cs.CR↗

arXiv2022

GLSO: Grammar-guided Latent Space Optimization for Sample-efficient Robot Design Automation

Robots have been used in all sorts of automation, and yet the design of robots remains mainly a manual task. We seek to provide design tools to automate the design of robots themselves. An important challenge in robot design automation is the large and complex design search space which grows exponentially with the number of components, making optimization difficult and sample inefficient. In this work, we present Grammar-guided Latent Space Optimization (GLSO), a framework that transforms design automation into a low-dimensional continuous optimization problem by training a graph variational autoencoder (VAE) to learn a mapping between the graph-structured design space and a continuous latent space. This transformation allows optimization to be conducted in a continuous latent space, where sample efficiency can be significantly boosted by applying algorithms such as Bayesian Optimization. GLSO guides training of the VAE using graph grammar rules and robot world space features, such that the learned latent space focus on valid robots and is easier for the optimization algorithm to explore. Importantly, the trained VAE can be reused to search for designs specialized to multiple different tasks without retraining. We evaluate GLSO by designing robots for a set of locomotion tasks in simulation, and demonstrate that our method outperforms related state-of-the-art robot design automation methods.

cs.RO↗

arXiv2022

REMS: Middleware for Robotics Education and Development

This paper introduces REMS, a robotics middleware and control framework that is designed to introduce the Zen of Python to robotics and to improve robotics education and development flow. Although existing middleware can serve hardware abstraction and modularity, setting up environments and learning middleware-specific syntax and procedures are less viable in education. They can curb opportunities to understand robotics concepts, theories, and algorithms. Robotics is a field of integration; students and developers from various backgrounds will be involved in programming. Establishing Pythonic and object-oriented robotic framework in a natural way can enhance modular and abstracted programming for better readability, reusability, and simplicity, but also supports useful and practical skills generally in coding. REMS is to be a valuable robot educational medium not just as a tool and to be a platform from one robot to multi-agent across hardware, simulation, and analytical model implementations.

cs.RO↗

arXiv2022

Improving User's Sense of Participation in Robot-Driven Dialogue

In task-oriented dialogues with symbiotic robots, the robot usually takes the initiative in dialogue progression and topic selection. In such robot-driven dialogue, the user's sense of participation in the dialogue is reduced because the degree of freedom in timing and content of speech is limited, and as a result, the user's familiarity with and trust in the robot as a dialogue partner and the level of dialogue satisfaction decrease. In this study, we constructed a travel agent dialogue system focusing on improving the sense of dialogue participation. At the beginning of the dialogue, the robot tells the user the purpose of the upcoming dialogue and indicates that it is responsible for assisting the user in making decisions. In addition, in situations where users were asked to state their preferences, the robot encourages them to express their intentions with actions, as well as spoken language responses. In addition, we attempted to reduce the sense of discomfort felt toward the android robot by devising a timing control for the robot's detailed movements and facial expressions.

cs.RO↗

arXiv2023

Human-centered Benchmarking for Socially-compliant Robot Navigation

Social compatibility is one of the most important parameters for service robots. It characterizes the quality of interaction between a robot and a human. In this paper, a human-centered benchmarking framework is proposed for socially-compliant robot navigation. In an end-to-end manner, four open-source robot navigation methods are benchmarked, two of which are socially-compliant. All aspects of the benchmarking are clarified to ensure the reproducibility and replicability of the experiments. The social compatibility of robot navigation methods with the Robotic Social Attributes Scale (RoSAS) is measured. After that, the correspondence between RoSAS and the robot-centered metrics is validated. Based on experiments, the extra robot time ratio and the extra distance ratio are the most suitable to judge social compatibility.

cs.RO↗

arXiv2022

Learning Locally, Communicating Globally: Reinforcement Learning of Multi-robot Task Allocation for Cooperative Transport

We consider task allocation for multi-object transport using a multi-robot system, in which each robot selects one object among multiple objects with different and unknown weights. The existing centralized methods assume the number of robots and tasks to be fixed, which is inapplicable to scenarios that differ from the learning environment. Meanwhile, the existing distributed methods limit the minimum number of robots and tasks to a constant value, making them applicable to various numbers of robots and tasks. However, they cannot transport an object whose weight exceeds the load capacity of robots observing the object. To make it applicable to various numbers of robots and objects with different and unknown weights, we propose a framework using multi-agent reinforcement learning for task allocation. First, we introduce a structured policy model consisting of 1) predesigned dynamic task priorities with global communication and 2) a neural network-based distributed policy model that determines the timing for coordination. The distributed policy builds consensus on the high-priority object under local observations and selects cooperative or independent actions. Then, the policy is optimized by multi-agent reinforcement learning through trial and error. This structured policy of local learning and global communication makes our framework applicable to various numbers of robots and objects with different and unknown weights, as demonstrated by numerical simulations.

cs.RO↗

arXiv2022

HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration

The ability to learn from human demonstration endows robots with the ability to automate various tasks. However, directly learning from human demonstration is challenging since the structure of the human hand can be very different from the desired robot gripper. In this work, we show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning, where a five-finger human dexterous hand robot gradually evolves into a commercial robot, while repeated interacting in a physics simulator to continuously update the policy that is first learned from human demonstration. To deal with the high dimensions of robot parameters, we propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy. Through experiments on human object manipulation datasets, we show that our framework can efficiently transfer the expert human agent policy trained from human demonstrations in diverse modalities to target commercial robots.

cs.RO↗

arXiv2022

Can a Robot Shoot an Olympic Recurve Bow? A preliminary study

The field of robotics, and more especially humanoid robotics, has several established competitions with research oriented goals in mind. Challenging the robots in a handful of tasks, these competitions provide a way to gauge the state of the art in robotic design, as well as an indicator for how far we are from reaching human performance. The most notable competitions are RoboCup, which has the long-term goal of competing against a real human team in 2050, and the FIRA HuroCup league, in which humanoid robots have to perform tasks based on actual Olympic events. Having robots compete against humans under the same rules is a challenging goal, and, we believe that it is in the sport of archery that humanoid robots have the most potential to achieve it in the near future. In this work, we perform a first step in this direction. We present a humanoid robot that is capable of gripping, drawing and shooting a recurve bow at a target 10 meters away with considerable accuracy. Additionally, we show that it is also capable of shooting distances of over 50 meters.

cs.RO↗

arXiv2023

Guaranteed Encapsulation of Targets with Unknown Motion by a Minimalist Robotic Swarm

We present a decentralized control algorithm for a robotic swarm given the task of encapsulating static and moving targets in a bounded unknown environment. We consider minimalist robots without memory, explicit communication, or localization information. The state-of-the-art approaches generally assume that the robots in the swarm are able to detect the relative position of neighboring robots and targets in order to provide convergence guarantees. In this work, we propose a novel control law for the guaranteed encapsulation of static and moving targets while avoiding all collisions, when the robots do not know the exact relative location of any robot or target in the environment. We make use of the Lyapunov stability theory to prove the convergence of our control algorithm and provide bounds on the ratio between the target and robot speeds. Furthermore, our proposed approach is able to provide stochastic guarantees under the bounds that we determine on task parameters for scenarios where a target moves faster than a robot. Finally, we present an analysis of how the emergent behavior changes with different parameters of the task and noisy sensor readings.

cs.RO↗

arXiv2023

World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges

Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and developmental robotics. Their learning processes should be based on interactions with their physical and social world in the manner of human learning and cognitive development. Based on this context, in this paper, we focus on the two concepts of world models and predictive coding. Recently, world models have attracted renewed attention as a topic of considerable interest in artificial intelligence. Cognitive systems learn world models to better predict future sensory observations and optimize their policies, i.e., controllers. Alternatively, in neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment. Both ideas may be considered as underpinning the cognitive development of robots and humans capable of continual or lifelong learning. Although many studies have been conducted on predictive coding in cognitive robotics and neurorobotics, the relationship between world model-based approaches in AI and predictive coding in robotics has rarely been discussed. Therefore, in this paper, we clarify the definitions, relationships, and status of current research on these topics, as well as missing pieces of world models and predictive coding in conjunction with crucially related concepts such as the free-energy principle and active inference in the context of cognitive and developmental robotics. Furthermore, we outline the frontiers and challenges involved in world models and predictive coding toward the further integration of AI and robotics, as well as the creation of robots with real cognitive and developmental capabilities in the future.

cs.RO↗

arXiv2023

SOCRATES: Text-based Human Search and Approach using a Robot Dog

In this paper, we propose a SOCratic model for Robots Approaching humans based on TExt System (SOCRATES) focusing on the human search and approach based on free-form textual description; the robot first searches for the target user, then the robot proceeds to approach in a human-friendly manner. In particular, textual descriptions are composed of appearance (e.g., wearing white shirts with black hair) and location clues (e.g., is a student who works with robots). We initially present a Human Search Socratic Model that connects large pre-trained models in the language domain to solve the downstream task, which is searching for the target person based on textual descriptions. Then, we propose a hybrid learning-based framework for generating target-cordial robotic motion to approach a person, consisting of a learning-from-demonstration module and a knowledge distillation module. We validate the proposed searching module via simulation using a virtual mobile robot as well as through real-world experiments involving participants and the Boston Dynamics Spot robot. Furthermore, we analyze the properties of the proposed approaching framework with human participants based on the Robotic Social Attributes Scale (RoSAS)

cs.RO↗

arXiv2023

Design Project of an Open-Source, Low-Cost, and Lightweight Robotic Manipulator for High School Students

In recent years, there is an increasing interest in high school robotics extracurriculars such as robotics clubs and robotics competitions. The growing demand is a result of more ubiquitous open-source software and affordable off-the-shelf hardware kits, which significantly help lower the barrier for entry-level robotics hobbyists. In this project, we present an open-source, low-cost, and lightweight robotic manipulator designed and developed by a high school researcher under the guidance of a university faculty and a Ph.D. student. We believe the presented project is suitable for high school robotics research and educational activities. Our open-source package consists of mechanical design models, mechatronics specifications, and software program source codes. The mechanical design models include CAD (Computer Aided Design) files that are ready for prototyping (3D printing technology) and serve as an assembly guide accommodated with a complete bill of materials. Electrical wiring diagrams and low-level controllers are documented in detail as part of the open-source software package. The educational objective of this project is to enable high school student teams to replicate and build a robotic manipulator. The engineering experience that high school students acquire in the proposed project is full-stack, including mechanical design, mechatronics, and programming. The project significantly enriches their hands-on engineering experience in a project-based environment. Throughout this project, we discovered that the high school researcher was able to apply multidisciplinary knowledge from K-12 STEM courses to build the robotic manipulator. The researcher was able to go through a system engineering design and development process and obtain skills to use professional engineering tools including SolidWorks and Arduino microcontrollers.

cs.RO↗

arXiv2023

Snake and Snake Robot Locomotion in Complex, 3-D Terrain

Snakes can traverse almost all types of environments by bending their elongate bodies in 3-D to interact with the terrain. Similarly, a snake robot is a promising platform to perform critical tasks in various environments. Understanding how 3-D body bending effectively interacts with the terrain for propulsion and stability can not only inform how snakes traverse natural environments, but also allow snake robots to achieve similar performance. How snakes and snake robots move on flat surfaces has been understood well. However, such ideal terrain is rare in natural environments and little was understood about how to generate propulsion and maintain stability in 3-D terrain, except for some studies on arboreal snake locomotion and on robots using geometric planning. To bridge the knowledge gap, we integrated animal experiments and robotic studies in three representative environments: a large smooth step, an uneven arena of blocks of large height variation, and large bumps. We discovered that vertical body bending induces stability challenges but can generate large propulsion. When traversing a large smooth step, a snake robot is challenged by roll instability that increases with the amplitude of vertical bending. The instability can be reduced by body compliance that statistically improves body-terrain contact. Despite this, vertical body bending can potentially allow snakes to push against terrain for propulsion, as demonstrated by corn snakes traversing an uneven arena. A snake robot can generate large propulsion like this if contact is well maintained. Contact feedback control can help accommodate perturbations such as novel terrain geometry or excessive external forces by improving contact. Our findings provide insights into how snakes and snake robots can use vertical body bending for efficient and versatile traversal of the 3-D world stably.

physics.bio-ph↗