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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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arXiv2021

On Healthcare Robots: Concepts, definitions, and considerations for healthcare robot governance

Although healthcare is a remarkably sensitive domain of application, and systems that exert direct control over the world can cause harm in a way that humans cannot necessarily correct or oversee, it is still unclear whether and how healthcare robots are currently regulated or should be regulated. Existing regulations are primarily unprepared to provide guidance for such a rapidly evolving field and accommodate devices that rely on machine learning and AI. Moreover, the field of healthcare robotics is very rich and extensive, but it is still very much scattered and unclear in terms of definitions, medical and technical classifications, product characteristics, purpose, and intended use. As a result, these devices often navigate between the medical device regulation or other non-medical norms, such as the ISO personal care standard. Before regulating the field of healthcare robots, it is therefore essential to map the major state-of-the-art developments in healthcare robotics, their capabilities and applications, and the challenges we face as a result of their integration within the healthcare environment. This contribution fills in this gap and lack of clarity currently experienced within healthcare robotics and its governance by providing a structured overview of and further elaboration on the main categories now established, their intended purpose, use, and main characteristics. We explicitly focus on surgical, assistive, and service robots to rightfully match the definition of healthcare as the organized provision of medical care to individuals, including efforts to maintain, treat, or restore physical, mental, or emotional well-being. We complement these findings with policy recommendations to help policymakers unravel an optimal regulatory framing for healthcare robot technologies

cs.RO↗

arXiv2021

Deploying COTS Legged Robot Platforms into a Heterogeneous Robot Team

The recent availability of commercial-off-the-shelf (COTS) legged robot platforms have opened up new opportunities in deploying legged systems into different scenarios. While the main advantage of legged robots is their ability to traverse unstructured terrain, there are still large gaps between what robot platforms can achieve and their animal counterparts. Therefore, when deploying as part of a heterogeneous robot team of different platforms, it is beneficial to understand the different scenarios where a legged platform would perform better than a wheeled, tracked or aerial platform. Two COTS quadruped robots, Ghost Robotics' Vision 60 and Boston Dynamics' Spot, were deployed into a heterogeneous team. A description of some of the challenges faced while integrating the platforms, as well as some experiments in traversing different terrains are provided to give insight into the real-world deployment of legged robots.

cs.RO↗

arXiv2022

Put the Bear on the Chair! Intelligent Robot Interaction with Previously Unseen Chairs via Robot Imagination

In this paper, we study the problem of autonomously seating a teddy bear on a previously unseen chair. To achieve this goal, we present a novel method for robots to imagine the sitting pose of the bear by physically simulating a virtual humanoid agent sitting on the chair. We also develop a robotic system which leverages motion planning to plan SE(2) motions for a humanoid robot to walk to the chair and whole-body motions to put the bear on it. Furthermore, to cope with cases where the chair is not in an accessible pose for placing the bear, a human assistance module is introduced for a human to follow language instructions given by the robot to rotate the chair and help make the chair accessible. We implement our method with a robot arm and a humanoid robot. We calibrate the proposed system with 3 chairs and test on 12 previously unseen chairs in both accessible and inaccessible poses extensively. Results show that our method enables the robot to autonomously seat the teddy bear on the 12 previously unseen chairs with a very high success rate. The human assistance module is also shown to be very effective in changing the accessibility of the chair. Video demos and more details are available at https://chirikjianlab.github.io/putbearonchair/.

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arXiv2021

An explorative study on how human-robot interaction is taken into account by robot developers in praxis

How is human-robot interaction considered within the development of new robotic systems by practitioners? This study sets out to inquire, whether the development teams of robotic products have been considering human factor methods in their design and implementation process. We were specifically interested in the non-verbal communication methods they were aiming to implement, and how they have approached the design process for these. Although valuable insights on tasks and communication needs during the different phases of robot operation could be gathered, the results of this study indicate, that the perspective of the human user or bystander is very often neglected and that knowledge on methods for engineering human-robot interaction is missing. The study was conducted with eleven development teams consisting of robot manufacturers and students within a robot building course representing overall 68 individual participants.

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arXiv2021

Contact Anticipation for Physical Human-Robot Interaction with Robotic Manipulators using Onboard Proximity Sensors

In this paper, we present a framework that unites obstacle avoidance and deliberate physical interaction for robotic manipulators. As humans and robots begin to coexist in work and household environments, pure collision avoidance is insufficient, as human-robot contact is inevitable and, in some situations, desired. Our work enables manipulators to anticipate, detect, and act on contact. To achieve this, we allow limited deviation from the robot's original trajectory through velocity reduction and motion restrictions. Then, if contact occurs, a robot can detect it and maneuver based on a novel dynamic contact thresholding algorithm. The core contribution of this work is dynamic contact thresholding, which allows a manipulator with onboard proximity sensors to track nearby objects and reduce contact forces in anticipation of a collision. Our framework elicits natural behavior during physical human-robot interaction. We evaluate our system on a variety of scenarios using the Franka Emika Panda robot arm; collectively, our results demonstrate that our contribution is not only able to avoid and react on contact, but also anticipate it.

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arXiv2022

Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots

Machine learning (ML) has become a pervasive tool across computing systems. An emerging application that stress-tests the challenges of ML system design is tiny robot learning, the deployment of ML on resource-constrained low-cost autonomous robots. Tiny robot learning lies at the intersection of embedded systems, robotics, and ML, compounding the challenges of these domains. Tiny robot learning is subject to challenges from size, weight, area, and power (SWAP) constraints; sensor, actuator, and compute hardware limitations; end-to-end system tradeoffs; and a large diversity of possible deployment scenarios. Tiny robot learning requires ML models to be designed with these challenges in mind, providing a crucible that reveals the necessity of holistic ML system design and automated end-to-end design tools for agile development. This paper gives a brief survey of the tiny robot learning space, elaborates on key challenges, and proposes promising opportunities for future work in ML system design.

cs.LG↗

arXiv2022

Increased Complexity of a Human-Robot Collaborative Task May Increase the Need for a Socially Competent Robot

An important factor in developing control models for human-robot collaboration is how acceptable they are to their human partners. One such method for creating acceptable control models is to attempt to mimic human-like behaviour in robots so that their actions appear more intuitive to humans. To investigate how task complexity affects human perception and acceptance of their robot partner, we propose a novel human-based robot control model for obstacle avoidance that can account for the leader-follower dynamics that normally occur in human collaboration. The performance and acceptance of the proposed control method were evaluated using an obstacle avoidance scenario in which we compared task performance between individual tasks and collaborative tasks with different leader-follower dynamics roles for the robotic partner. The evaluation results showed that the robot control method is able to replicate human behaviour to improve the overall task performance of the subject in collaboration. However, regarding the acceptance of the robotic partner, the participants' opinions were mixed. Compared to the results of a study with a similar control method developed for a less complex task, the new results show a lower acceptance of the proposed control model, even though the control method was adapted to the more complex task from a dynamic standpoint. This suggests that the complexity of the collaborative task at hand increases the need not only for a more complex control model but also a more socially competent control model.

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arXiv2022

World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator

Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans.The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in the real home environments, and importantly, tested for full system performances.For this challenge, we developed an entire household service robot system, which leverages a data-driven approach to adapt to numerous edge cases that occur during the execution, instead of classical manual pre-programmed solutions. In this paper, we describe the core ingredients of the proposed robot system, including visual recognition, object manipulation, and motion planning. Our robot system won the second prize, verifying the effectiveness and potential of data-driven robot systems for mobile manipulation in home environments.

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arXiv2022

RGB-D Robotic Pose Estimation For a Servicing Robotic Arm

A large number of robotic and human-assisted missions to the Moon and Mars are forecast. NASA's efforts to learn about the geology and makeup of these celestial bodies rely heavily on the use of robotic arms. The safety and redundancy aspects will be crucial when humans will be working alongside the robotic explorers. Additionally, robotic arms are crucial to satellite servicing and planned orbit debris mitigation missions. The goal of this work is to create a custom Computer Vision (CV) based Artificial Neural Network (ANN) that would be able to rapidly identify the posture of a 7 Degree of Freedom (DoF) robotic arm from a single (RGB-D) image - just like humans can easily identify if an arm is pointing in some general direction. The Sawyer robotic arm is used for developing and training this intelligent algorithm. Since Sawyer's joint space spans 7 dimensions, it is an insurmountable task to cover the entire joint configuration space. In this work, orthogonal arrays are used, similar to the Taguchi method, to efficiently span the joint space with the minimal number of training images. This ``optimally'' generated database is used to train the custom ANN and its degree of accuracy is on average equal to twice the smallest joint displacement step used for database generation. A pre-trained ANN will be useful for estimating the postures of robotic manipulators used on space stations, spacecraft, and rovers as an auxiliary tool or for contingency plans.

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arXiv2023

Real-life Implementation of Internet of Robotic Things Using 5 DoF Heterogeneous Robotic Arm

Establishing a communication bridge by transferring data driven from different embedded sensors via internet or reconcilable network protocols between enormous number of distinctively addressable objects or "things", is known as the Internet of Things (IoT). IoT can be amalgamated with multitudinous objects such as thermostats, cars, lights, refrigerators, and many more appliances which will be able to build a connection via internet. Where objects of our diurnal life can establish a network connection and get smarter with IoT, robotics can be another aspect which will get beneficial to be brought under the concept of IoT and is able to add a new perception in robotics having "Mechanical Smart Intelligence" which is generally called "Internet of Robotic Things" (IoRT). A robotic arm is a part of robotics where it is usually a programmable mechanical arm which has human arm like functionalities. In this paper, IoRT will be represented by a 5 DoF (degree of freedoms) Robotic Arm which will be able to communicate as an IoRT device, controlled with heterogeneous devices using IoT and "Cloud Robotics".

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arXiv2023

AR2-D2:Training a Robot Without a Robot

Diligently gathered human demonstrations serve as the unsung heroes empowering the progression of robot learning. Today, demonstrations are collected by training people to use specialized controllers, which (tele-)operate robots to manipulate a small number of objects. By contrast, we introduce AR2-D2: a system for collecting demonstrations which (1) does not require people with specialized training, (2) does not require any real robots during data collection, and therefore, (3) enables manipulation of diverse objects with a real robot. AR2-D2 is a framework in the form of an iOS app that people can use to record a video of themselves manipulating any object while simultaneously capturing essential data modalities for training a real robot. We show that data collected via our system enables the training of behavior cloning agents in manipulating real objects. Our experiments further show that training with our AR data is as effective as training with real-world robot demonstrations. Moreover, our user study indicates that users find AR2-D2 intuitive to use and require no training in contrast to four other frequently employed methods for collecting robot demonstrations.

cs.RO↗

arXiv2023

CloudGripper: An Open Source Cloud Robotics Testbed for Robotic Manipulation Research, Benchmarking and Data Collection at Scale

We present CloudGripper, an open source cloud robotics testbed, consisting of a scalable, space and cost-efficient design constructed as a rack of 32 small robot arm work cells. Each robot work cell is fully enclosed and features individual lighting, a low-cost custom 5 degree of freedom Cartesian robot arm with an attached parallel jaw gripper and a dual camera setup for experimentation. The system design is focused on continuous operation and features a 10 Gbit/s network connectivity allowing for high throughput remote-controlled experimentation and data collection for robotic manipulation. CloudGripper furthermore is intended to form a community testbed to study the challenges of large scale machine learning and cloud and edge-computing in the context of robotic manipulation. In this work, we describe the mechanical design of the system, its initial software stack and evaluate the repeatability of motions executed by the proposed robot arm design. A local network API throughput and latency analysis is also provided. CloudGripper-Rope-100, a dataset of more than a hundred hours of randomized rope pushing interactions and approximately 4 million camera images is collected and serves as a proof of concept demonstrating data collection capabilities. A project website with more information is available at https://cloudgripper.org.

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arXiv2024

Software Engineering for Robotics: Future Research Directions; Report from the 2023 Workshop on Software Engineering for Robotics

Robots are experiencing a revolution as they permeate many aspects of our daily lives, from performing house maintenance to infrastructure inspection, from efficiently warehousing goods to autonomous vehicles, and more. This technical progress and its impact are astounding. This revolution, however, is outstripping the capabilities of existing software development processes, techniques, and tools, which largely have remained unchanged for decades. These capabilities are ill-suited to handling the challenges unique to robotics software such as dealing with a wide diversity of domains, heterogeneous hardware, programmed and learned components, complex physical environments captured and modeled with uncertainty, emergent behaviors that include human interactions, and scalability demands that span across multiple dimensions. Looking ahead to the need to develop software for robots that are ever more ubiquitous, autonomous, and reliant on complex adaptive components, hardware, and data, motivated an NSF-sponsored community workshop on the subject of Software Engineering for Robotics, held in Detroit, Michigan in October 2023. The goal of the workshop was to bring together thought leaders across robotics and software engineering to coalesce a community, and identify key problems in the area of SE for robotics that that community should aim to solve over the next 5 years. This report serves to summarize the motivation, activities, and findings of that workshop, in particular by articulating the challenges unique to robot software, and identifying a vision for fruitful near-term research directions to tackle them.

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arXiv2024

Long-Term, Store-Front Robotics: Interactive Music for Robotic Arm, Caxixi and Frame Drums

This paper presents an innovative exploration into the integration of interactive robotic musicianship within a commercial retail environment, specifically through a three-week-long in-store installation featuring a UR3 robotic arm, custom-built frame drums, and an adaptive music generation system. Situated in a prominent storefront in one of the world's largest cities, this project aimed to enhance the shopping experience by creating dynamic, engaging musical interactions that respond to the store's ambient soundscape. Key contributions include the novel application of industrial robotics in artistic expression, the deployment of interactive music to enrich retail ambiance, and the demonstration of continuous robotic operation in a public setting over an extended period. Challenges such as system reliability, variation in musical output, safety in interactive contexts, and brand alignment were addressed to ensure the installation's success. The project not only showcased the technical feasibility and artistic potential of robotic musicianship in retail spaces but also offered insights into the practical implications of such integration, including system reliability, the dynamics of human-robot interaction, and the impact on store operations. This exploration opens new avenues for enhancing consumer retail experiences through the intersection of technology, music, and interactive art, suggesting a future where robotic musicianship contributes meaningfully to public and commercial spaces.

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arXiv2024

State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey

With the continuous breakthroughs in core technology, the dawn of large-scale integration of robotic systems into daily human life is on the horizon. Multi-robot systems (MRS) built on this foundation are undergoing drastic evolution. The fusion of artificial intelligence technology with robot hardware is seeing broad application possibilities for MRS. This article surveys the state-of-the-art of robot learning in the context of Multi-Robot Cooperation (MRC) of recent. Commonly adopted robot learning methods (or frameworks) that are inspired by humans and animals are reviewed and their advantages and disadvantages are discussed along with the associated technical challenges. The potential trends of robot learning and MRS integration exploiting the merging of these methods with real-world applications is also discussed at length. Specifically statistical methods are used to quantitatively corroborate the ideas elaborated in the article.

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arXiv2024

Real-Time Adaptive Industrial Robots: Improving Safety And Comfort In Human-Robot Collaboration

Industrial robots become increasingly prevalent, resulting in a growing need for intuitive, comforting human-robot collaboration. We present a user-aware robotic system that adapts to operator behavior in real time while non-intrusively monitoring physiological signals to create a more responsive and empathetic environment. Our prototype dynamically adjusts robot speed and movement patterns while measuring operator pupil dilation and proximity. Our user study compares this adaptive system to a non-adaptive counterpart, and demonstrates that the adaptive system significantly reduces both perceived and physiologically measured cognitive load while enhancing usability. Participants reported increased feelings of comfort, safety, trust, and a stronger sense of collaboration when working with the adaptive robot. This highlights the potential of integrating real-time physiological data into human-robot interaction paradigms. This novel approach creates more intuitive and collaborative industrial environments where robots effectively 'read' and respond to human cognitive states, and we feature all data and code for future use.

cs.RO↗

arXiv2024

Hey Robot! Personalizing Robot Navigation through Model Predictive Control with a Large Language Model

Robot navigation methods allow mobile robots to operate in applications such as warehouses or hospitals. While the environment in which the robot operates imposes requirements on its navigation behavior, most existing methods do not allow the end-user to configure the robot's behavior and priorities, possibly leading to undesirable behavior (e.g., fast driving in a hospital). We propose a novel approach to adapt robot motion behavior based on natural language instructions provided by the end-user. Our zero-shot method uses an existing Visual Language Model to interpret a user text query or an image of the environment. This information is used to generate the cost function and reconfigure the parameters of a Model Predictive Controller, translating the user's instruction to the robot's motion behavior. This allows our method to safely and effectively navigate in dynamic and challenging environments. We extensively evaluate our method's individual components and demonstrate the effectiveness of our method on a ground robot in simulation and real-world experiments, and across a variety of environments and user specifications.

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arXiv2025

Robo-Platform: A Robotic System for Recording Sensors and Controlling Robots

Mobile smartphones compactly provide sensors such as cameras, IMUs, GNSS measurement units, and wireless and wired communication channels required for robotics projects. They are affordable, portable, and programmable, which makes them ideal for testing, data acquisition, controlling mobile robots, and many other robotic applications. A robotic system is proposed in this paper, consisting of an Android phone, a microcontroller board attached to the phone via USB, and a remote wireless controller station. In the data acquisition mode, the Android device can record a dataset of a diverse configuration of multiple cameras, IMUs, GNSS units, and external USB ADC channels in the rawest format used for, but not limited to, pose estimation and scene reconstruction applications. In robot control mode, the Android phone, a microcontroller board, and other peripherals constitute the mobile or stationary robotic system. This system is controlled using a remote server connected over Wi-Fi or Bluetooth. Experiments show that although the SLAM and AR applications can utilize the acquired data, the proposed system can pave the way for more advanced algorithms for processing these noisy and sporadic measurements. Moreover, the characteristics of the communication media are studied, and two example robotic projects, which involve controlling a toy car and a quadcopter, are included.

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