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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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At least 253 records · Page 14Linked to original sources

arXiv2026

When Robots Exchange Meaning: A Demo of Goal-Oriented Semantic Communications for Collaborative Robotics

Collaborative robotics is a representative task-oriented 6G use-case, where communication quality should be reflected in mission execution, environment understanding, and closed-loop operation rather than packet delivery alone. This demo paper presents a robot-edge semantic communication (SemCom) testbed integrating robot-side visual compression, edge-side semantic mapping, and dashboard-based mission interaction. A mobile robot equipped with RGB-D sensing and LiDAR runs ROS 2, while a Jetson Orin edge node performs reconstruction, RTAB-Map mapping, semantic object handling, and browserbased visualization. As an initial proof of concept, RGB frames are encoded on the robot into VQ-VAE tokens using an ONNX Runtime encoder and reconstructed on the edge using a PyTorch decoder. A 320 X 240 image is represented by an 80 X 60 token grid with a packed payload of 5400 bytes, corresponding to a 42.67X reduction relative to model-input RGB bytes. The reconstructed visual stream is further associated with depth, pose, and 3D mapping information to generate a semantic map for downstream robotic applications. The demo exposes the full path from semantic visual transport to object-level map interaction, and provides a practical platform for future task-aware 6G networking studies at the intersection of SemCom, embodied AI, and physical AI-enabled robotics. A video of the demo is available at https://tinyurl.com/Tos09

cs.RO↗

arXiv2026

Calming Robot Pitches? Exploring the Influence of Robot Voice Pitch on Children's Stress Levels

This study examined whether variations in robot speech pitch influence children's stress levels during a robot-guided game. Although lower-pitched voices have been shown to facilitate stress regulation in human communication, it remains unclear whether this effect generalizes to synthetic voices in child-robot interactions. Twenty-seven Dutch children aged 8-12 years were randomly assigned to interact with a Zenbo Junior II robot using either a lower-pitched or a higher-pitched voice. The interaction consisted of an introduction followed by a timed LEGO-building game. Stress levels, measured with an adapted version of CAM-S, increased during the game, confirming the stress-inducing nature of the task. No differences emerged between pitch conditions. These findings suggest that the benefits of lower pitch in reducing stress may not directly translate to child-robot interactions. Possible explanations include children's developing sensitivity to emotional tone, mismatches between the robot's voice and appearance, or the use of fixed pitch changes that sound unnatural, since real speech varies dynamically across multiple dimensions. Future research examining combinations of prosodic cues (beyond pitch alone) could provide further insights and help inform robot voice design for effective stress regulation support for children.

cs.HC↗

arXiv2026

Rapid On-Robot Learning for Dynamic Manipulation Skills: Robot Juggling

We present an online learning framework that enables a bimanual robot to acquire diverse juggling patterns directly on physical hardware within minutes, even with a significant sim2real gap. One of the most important lessons from this work is that a model, even when far from reality, can be extremely useful for learning. This motivates a central philosophy of our approach: learning should build upon the robot's current knowledge rather than replace it. Our regularized memory-based learning puts this principle into practice by learning a local model from accumulated experience while retaining the global prior model to extrapolate where experience is sparse. This enables efficient and stable online learning from each new experience without resorting to uninformed exploration over a vast space of possible behaviors. Equally important to continual on-robot learning is safety, allowing the robot to repeatedly practice and improve in the real world. We construct a mutually reachable set that allows safe transitions between successive throws and catches, without driving either arm into a state from which its next action would require violating the robot's joint or actuator limits. Together, these ideas enable a bimanual robot with multi-fingered hands and onboard vision to safely learn and compose five canonical three-ball juggling patterns, including cascade, tennis, half-shower, shower, and box, within less than 5 minutes of real-world interaction. More broadly, this work points toward robots that build upon imperfect prior knowledge and continually refine their behavior through their own real-world experience.

cs.RO↗

arXiv2016

Socially Impaired Robots: Human Social Disorders and Robots' Socio-Emotional Intelligence

Social robots need intelligence in order to safely coexist and interact with humans. Robots without functional abilities in understanding others and unable to empathise might be a societal risk and they may lead to a society of socially impaired robots. In this work we provide a survey of three relevant human social disorders, namely autism, psychopathy and schizophrenia, as a means to gain a better understanding of social robots' future capability requirements. We provide evidence supporting the idea that social robots will require a combination of emotional intelligence and social intelligence, namely socio-emotional intelligence. We argue that a robot with a simple socio-emotional process requires a simulation-driven model of intelligence. Finally, we provide some critical guidelines for designing future socio-emotional robots.

cs.RO↗

arXiv2016

Systematic and Realistic Testing in Simulation of Control Code for Robots in Collaborative Human-Robot Interactions

Industries such as flexible manufacturing and home care will be transformed by the presence of robotic assistants. Assurance of safety and functional soundness for these robotic systems will require rigorous verification and validation. We propose testing in simulation using Coverage-Driven Verification (CDV) to guide the testing process in an automatic and systematic way. We use a two-tiered test generation approach, where abstract test sequences are computed first and then concretized (e.g., data and variables are instantiated), to reduce the complexity of the test generation problem. To demonstrate the effectiveness of our approach, we developed a testbench for robotic code, running in ROS-Gazebo, that implements an object handover as part of a human-robot interaction (HRI) task. Tests are generated to stimulate the robot's code in a realistic manner, through stimulating the human, environment, sensors, and actuators in simulation. We compare the merits of unconstrained, constrained and model-based test generation in achieving thorough exploration of the code under test, and interesting combinations of human-robot interactions. Our results show that CDV combined with systematic test generation achieves a very high degree of automation in simulation-based verification of control code for robots in HRI.

cs.RO↗

arXiv2024

Towards a Robotic Intrusion Prevention System: Combining Security and Safety in Cognitive Social Robots

Social Robots need to be safe and reliable to share their space with humans. This paper reports on the first results of a research project that aims to create more safe and reliable, intelligent autonomous robots by investigating the implications and interactions between cybersecurity and safety. We propose creating a robotic intrusion prevention system (RIPS) that follows a novel approach to detect and mitigate intrusions in cognitive social robot systems and other cyber-physical systems. The RIPS detects threats at the robotic communication level and enables mitigation of the cyber-physical threats by using System Modes to define what part of the robotic system reduces or limits its functionality while the system is compromised. We demonstrate the validity of our approach by applying it to a cognitive architecture running in a real social robot that preserves the privacy and safety of humans while facing several cyber attack situations.

cs.RO↗

arXiv2022

Towards Robotic Laboratory Automation Plug & Play: Survey and Concept Proposal on Teaching-free Robot Integration with the LAPP Digital Twin

The Laboratory Automation Plug & Play (LAPP) framework is an over-arching reference architecture concept for the integration of robots in life science laboratories. The plug & play nature lies in the fact that manual configuration is not required, including the teaching of the robots. In this paper a digital twin (DT) based concept is proposed that outlines the types of information that have to be provided for each relevant component of the system. In particular, for the devices interfacing with the robot, the robot positions have to be defined beforehand in a device-attached coordinate system (CS) by the vendor. This CS has to be detectable by the vision system of the robot by means of optical markers placed on the front side of the device. With that, the robot is capable of tending the machine by performing the pick-and-place type transportation of standard sample carriers. This basic use case is the primary scope of the LAPP-DT framework. The hardware scope is limited to simple benchtop and mobile manipulators with parallel grippers at this stage. This paper first provides an overview of relevant literature and state-of-the-art solutions, after which it outlines the framework on the conceptual level, followed by the specification of the relevant DT parameters for the robot, for the devices and for the facility. Finally, appropriate technologies and strategies are identified for the implementation.

cs.RO↗

arXiv2023

Chronos and CRS: Design of a miniature car-like robot and a software framework for single and multi-agent robotics and control

From both an educational and research point of view, experiments on hardware are a key aspect of robotics and control. In the last decade, many open-source hardware and software frameworks for wheeled robots have been presented, mainly in the form of unicycles and car-like robots, with the goal of making robotics accessible to a wider audience and to support control systems development. Unicycles are usually small and inexpensive, and therefore facilitate experiments in a larger fleet, but they are not suited for high-speed motion. Car-like robots are more agile, but they are usually larger and more expensive, thus requiring more resources in terms of space and money. In order to bridge this gap, we present Chronos, a new car-like 1/28th scale robot with customized open-source electronics, and CRS, an open-source software framework for control and robotics. The CRS software framework includes the implementation of various state-of-the-art algorithms for control, estimation, and multi-agent coordination. With this work, we aim to provide easier access to hardware and reduce the engineering time needed to start new educational and research projects.

cs.RO↗

arXiv2024

Behavior Tree Capabilities for Dynamic Multi-Robot Task Allocation with Heterogeneous Robot Teams

While individual robots are becoming increasingly capable, with new sensors and actuators, the complexity of expected missions increased exponentially in comparison. To cope with this complexity, heterogeneous teams of robots have become a significant research interest in recent years. Making effective use of the robots and their unique skills in a team is challenging. Dynamic runtime conditions often make static task allocations infeasible, therefore requiring a dynamic, capability-aware allocation of tasks to team members. To this end, we propose and implement a system that allows a user to specify missions using Bheavior Trees (BTs), which can then, at runtime, be dynamically allocated to the current robot team. The system allows to statically model an individual robot's capabilities within our ros_bt_py BT framework. It offers a runtime auction system to dynamically allocate tasks to the most capable robot in the current team. The system leverages utility values and pre-conditions to ensure that the allocation improves the overall mission execution quality while preventing faulty assignments. To evaluate the system, we simulated a find-and-decontaminate mission with a team of three heterogeneous robots and analyzed the utilization and overall mission times as metrics. Our results show that our system can improve the overall effectiveness of a team while allowing for intuitive mission specification and flexibility in the team composition.

cs.RO↗

arXiv2025

Hand Gesture Recognition for Collaborative Robots Using Lightweight Deep Learning in Real-Time Robotic Systems

Direct and natural interaction is essential for intuitive human-robot collaboration, eliminating the need for additional devices such as joysticks, tablets, or wearable sensors. In this paper, we present a lightweight deep learning-based hand gesture recognition system that enables humans to control collaborative robots naturally and efficiently. This model recognizes eight distinct hand gestures with only 1,103 parameters and a compact size of 22 KB, achieving an accuracy of 93.5%. To further optimize the model for real-world deployment on edge devices, we applied quantization and pruning using TensorFlow Lite, reducing the final model size to just 7 KB. The system was successfully implemented and tested on a Universal Robot UR5 collaborative robot within a real-time robotic framework based on ROS2. The results demonstrate that even extremely lightweight models can deliver accurate and responsive hand gesture-based control for collaborative robots, opening new possibilities for natural human-robot interaction in constrained environments.

cs.RO↗

arXiv2024

Enhancing the LLM-Based Robot Manipulation Through Human-Robot Collaboration

Large Language Models (LLMs) are gaining popularity in the field of robotics. However, LLM-based robots are limited to simple, repetitive motions due to the poor integration between language models, robots, and the environment. This paper proposes a novel approach to enhance the performance of LLM-based autonomous manipulation through Human-Robot Collaboration (HRC). The approach involves using a prompted GPT-4 language model to decompose high-level language commands into sequences of motions that can be executed by the robot. The system also employs a YOLO-based perception algorithm, providing visual cues to the LLM, which aids in planning feasible motions within the specific environment. Additionally, an HRC method is proposed by combining teleoperation and Dynamic Movement Primitives (DMP), allowing the LLM-based robot to learn from human guidance. Real-world experiments have been conducted using the Toyota Human Support Robot for manipulation tasks. The outcomes indicate that tasks requiring complex trajectory planning and reasoning over environments can be efficiently accomplished through the incorporation of human demonstrations.

cs.RO↗

arXiv2026

GHOST in the Robots: Real-Time Exocentric Dual-Robot VR Teleoperation from Onboard Cameras

Teleoperating multiple robots simultaneously enables additional views and coordinated control. Yet, it poses fundamental challenges: the system must present sensor data cohesively and allow operators to manage multiple robot bases, arms, and cameras while maintaining low latency. Current multi-robot teleoperation systems require multiple operators, rely on autonomy, or restrict operators to high-level commands. We present GHOST: an open-source VR teleoperation system that enables single operator control of two mobile manipulators via direct lowlevel commands using only onboard sensing. GHOST creates an exocentric 3D workspace by aligning real-time point clouds from the robots' RGB-D cameras, where scene coverage is improved through learning-based completion to aid operator spatial awareness. For control, the operator uses a mode-switching architecture to command either robot individually or both robots simultaneously. Experiments with 15 novice participants demonstrate 1.6-4x the success rate of an off-the-shelf tablet interface. For experts across nine challenging dual-robot tasks, our system enabled completion of two tasks that were infeasible with the tablet, and was 1.47x faster on average than the tablet. Website and code: https://h2r.github.io/GHOST/.

cs.RO↗

arXiv2025

Robot Cell Modeling via Exploratory Robot Motions: A Novel and Accessible Data-Driven Approach

Generating a collision-free robot motion is crucial for safe applications in real-world settings. This requires an accurate model of all obstacle shapes within the constrained robot cell, which is particularly challenging and time-consuming. The difficulty is heightened in flexible production lines, where the environment model must be updated each time the robot cell is modified. Furthermore, sensor-based methods often necessitate costly hardware and calibration procedures and can be influenced by environmental factors (e.g., light conditions or reflections). To address these challenges, we present a novel data-driven approach to modeling a cluttered workspace, leveraging solely the robot internal joint encoders to capture exploratory motions. By computing the corresponding swept volume (SV), we generate a (conservative) mesh of the environment that is subsequently used for collision checking within established path planning and control methods. Our method significantly reduces the complexity and cost of classical environment modeling by removing the need for computer-aided design (CAD) files and external sensors. We validate the approach with the KUKA LBR iisy collaborative robot in a pick-and-place scenario. In less than three minutes of exploratory robot motions and less than four additional minutes of computation time, we obtain an accurate model that enables collision-free motions. Our approach is intuitive and easy to use, making it accessible to users without specialized technical knowledge. It is applicable to all types of industrial robots or cobots.

cs.RO↗

arXiv2021

Best Viewpoints for External Robots or Sensors Assisting Other Robots

This work creates a model of the value of different external viewpoints of a robot performing tasks. The current state of the practice is to use a teleoperated assistant robot to provide a view of a task being performed by a primary robot; however, the choice of viewpoints is ad hoc and does not always lead to improved performance. This research applies a psychomotor approach to develop a model of the relative quality of external viewpoints using Gibsonian affordances. In this approach, viewpoints for the affordances are rated based on the psychomotor behavior of human operators and clustered into manifolds of viewpoints with the equivalent value. The value of 30 viewpoints is quantified in a study with 31 expert robot operators for 4 affordances (Reachability, Passability, Manipulability, and Traversability) using a computer-based simulator of two robots. The adjacent viewpoints with similar values are clustered into ranked manifolds using agglomerative hierarchical clustering. The results show the validity of the affordance-based approach by confirming that there are manifolds of statistically significantly different viewpoint values, viewpoint values are statistically significantly dependent on the affordances, and viewpoint values are independent of a robot. Furthermore, the best manifold for each affordance provides a statistically significant improvement with a large Cohen's d effect size (1.1-2.3) in performance (improving time by 14%-59% and reducing errors by 87%-100%) and improvement in performance variation over the worst manifold. This model will enable autonomous selection of the best possible viewpoint and path planning for the assistant robot.

cs.RO↗

arXiv2023

Piezoelectric Soft Robot Inchworm Motion by Tuning Ground Friction through Robot Shape: Quasi-Static Modeling and Experimental Validation

Electrically-driven soft robots based on piezoelectric actuators may enable compact form factors and maneuverability in complex environments. In most prior work, piezoelectric actuators are used to control a single degree of freedom. In this work, the coordinated activation of five independent piezoelectric actuators, attached to a common metal foil, is used to implement inchworm-inspired crawling motion in a robot that is less than 0.5 mm thick. The motion is based on the control of its friction to the ground through the robot's shape, in which one end of the robot (depending on its shape) is anchored to the ground by static friction, while the rest of its body expands or contracts. A complete analytical model of the robot shape, which includes gravity, is developed to quantify the robot shape, friction, and displacement. After validation of the model by experiments, the robot's five actuators are collectively sequenced for inchworm-like forward and backward motion.

cs.RO↗

arXiv2013

Work in Progress: Enabling robot device discovery through robot device descriptions

There is no dearth of new robots that provide both generalized and customized platforms for learning and research. Unfortunately as we attempt to adapt existing software components, we are faced with an explosion of device drivers that interface each hardware platform with existing frameworks. We certainly gain the efficiencies of reusing algorithms and tools developed across platforms but only once the device driver is created. We propose a domain specific language that describes the development and runtime interface of a robot and defines its link to existing frameworks. The Robot Device Interface Specification (RDIS) takes advantage of the internal firmware present on many existing devices by defining the communication mechanism, syntax and semantics in such a way to enable the generation of automatic interface links and resource discovery. We present the current domain model as it relates to differential drive robots as a mechanism to use the RDIS to link described robots to HTML5 via web sockets and ROS (Robot Operating System).

cs.RO↗

arXiv2017

Human-Robot Collaboration: From Psychology to Social Robotics

With the advances in robotic technology, research in human-robot collaboration (HRC) has gained in importance. For robots to interact with humans autonomously they need active decision making that takes human partners into account. However, state-of-the-art research in HRC does often assume a leader-follower division, in which one agent leads the interaction. We believe that this is caused by the lack of a reliable representation of the human and the environment to allow autonomous decision making. This problem can be overcome by an embodied approach to HRC which is inspired by psychological studies of human-human interaction (HHI). In this survey, we review neuroscientific and psychological findings of the sensorimotor patterns that govern HHI and view them in a robotics context. Additionally, we study the advances made by the robotic community into the direction of embodied HRC. We focus on the mechanisms that are required for active, physical human-robot collaboration. Finally, we discuss the similarities and differences in the two fields of study which pinpoint directions of future research.

cs.RO↗

arXiv2018

An information model for modular robots: the Hardware Robot Information Model (HRIM)

Today's landscape of robotics is dominated by vertical integration where single vendors develop the final product leading to slow progress, expensive products and customer lock-in. Opposite to this, an horizontal integration would result in a rapid development of cost-effective mass-market products with an additional consumer empowerment. The transition of an industry from vertical integration to horizontal integration is typically catalysed by de facto industry standards that enable a simplified and seamless integration of products. However, in robotics there is currently no leading candidate for a global plug-and-play standard. This paper tackles the problem of incompatibility between robot components that hinder the reconfigurability and flexibility demanded by the robotics industry. Particularly, it presents a model to create plug-and-play robot hardware components. Rather than iteratively evolving previous ontologies, our proposed model answers the needs identified by the industry while facilitating interoperability, measurability and comparability of robotics technology. Our approach differs significantly with the ones presented before as it is hardware-oriented and establishes a clear set of actions towards the integration of this model in real environments and with real manufacturers.

cs.RO↗