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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 307 records · Page 17Linked to original sources

arXiv2024

Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction

Humans can learn to manipulate new objects by simply watching others; providing robots with the ability to learn from such demonstrations would enable a natural interface specifying new behaviors. This work develops Robot See Robot Do (RSRD), a method for imitating articulated object manipulation from a single monocular RGB human demonstration given a single static multi-view object scan. We first propose 4D Differentiable Part Models (4D-DPM), a method for recovering 3D part motion from a monocular video with differentiable rendering. This analysis-by-synthesis approach uses part-centric feature fields in an iterative optimization which enables the use of geometric regularizers to recover 3D motions from only a single video. Given this 4D reconstruction, the robot replicates object trajectories by planning bimanual arm motions that induce the demonstrated object part motion. By representing demonstrations as part-centric trajectories, RSRD focuses on replicating the demonstration's intended behavior while considering the robot's own morphological limits, rather than attempting to reproduce the hand's motion. We evaluate 4D-DPM's 3D tracking accuracy on ground truth annotated 3D part trajectories and RSRD's physical execution performance on 9 objects across 10 trials each on a bimanual YuMi robot. Each phase of RSRD achieves an average of 87% success rate, for a total end-to-end success rate of 60% across 90 trials. Notably, this is accomplished using only feature fields distilled from large pretrained vision models -- without any task-specific training, fine-tuning, dataset collection, or annotation. Project page: https://robot-see-robot-do.github.io

cs.RO↗

arXiv2024

MissionGPT: Mission Planner for Mobile Robot based on Robotics Transformer Model

This paper presents a novel approach to building mission planners based on neural networks with Transformer architecture and Large Language Models (LLMs). This approach demonstrates the possibility of setting a task for a mobile robot and its successful execution without the use of perception algorithms, based only on the data coming from the camera. In this work, a success rate of more than 50\% was obtained for one of the basic actions for mobile robots. The proposed approach is of practical importance in the field of warehouse logistics robots, as in the future it may allow to eliminate the use of markings, LiDARs, beacons and other tools for robot orientation in space. In conclusion, this approach can be scaled for any type of robot and for any number of robots.

cs.RO↗

arXiv2024

Development of a Human-Robot Interaction Platform for Dual-Arm Robots Based on ROS and Multimodal Artificial Intelligence

In this paper, we propose the development of an interactive platform between humans and a dual-arm robotic system based on the Robot Operating System (ROS) and a multimodal artificial intelligence model. Our proposed platform consists of two main components: a dual-arm robotic hardware system and software that includes image processing tasks and natural language processing using a 3D camera and embedded computing. First, we designed and developed a dual-arm robotic system with a positional accuracy of less than 2 cm, capable of operating independently, performing industrial and service tasks while simultaneously simulating and modeling the robot in the ROS environment. Second, artificial intelligence models for image processing are integrated to execute object picking and classification tasks with an accuracy of over 90%. Finally, we developed remote control software using voice commands through a natural language processing model. Experimental results demonstrate the accuracy of the multimodal artificial intelligence model and the flexibility of the dual-arm robotic system in interactive human environments.

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arXiv2025

Enhancing Human-Robot Interaction in Healthcare: A Study on Nonverbal Communication Cues and Trust Dynamics with NAO Robot Caregivers

As the population of older adults increases, so will the need for both human and robot care providers. While traditional practices involve hiring human caregivers to serve meals and attend to basic needs, older adults often require continuous companionship and health monitoring. However, hiring human caregivers for this job costs a lot of money. However, using a robot like Nao could be cheaper and still helpful. This study explores the integration of humanoid robots, particularly Nao, in health monitoring and caregiving for older adults. Using a mixed-methods approach with a within-subject factorial design, we investigated the effectiveness of nonverbal communication modalities, including touch, gestures, and LED patterns, in enhancing human-robot interactions. Our results indicate that Nao's touch-based health monitoring was well-received by participants, with positive ratings across various dimensions. LED patterns were perceived as more effective and accurate compared to hand and head gestures. Moreover, longer interactions were associated with higher trust levels and perceived empathy, highlighting the importance of prolonged engagement in fostering trust in human-robot interactions. Despite limitations, our study contributes valuable insights into the potential of humanoid robots to improve health monitoring and caregiving for older adults.

cs.HC↗

arXiv2026

Unreal Robotics Lab: A High-Fidelity Robotics Simulator with Advanced Physics and Rendering

High-fidelity simulation is essential for robotics research, enabling safe and efficient testing of perception, control, and navigation algorithms. However, achieving both photorealistic rendering and accurate physics modeling remains a challenge. This paper presents a novel simulation framework, the Unreal Robotics Lab (URL), that integrates the advanced rendering capabilities of the Unreal Engine with MuJoCo's high-precision physics simulation. Our approach enables realistic robotic perception while maintaining accurate physical interactions, facilitating benchmarking and dataset generation for vision-based robotics applications. The system supports complex environmental effects, such as smoke, fire, and water dynamics, which are critical to evaluating robotic performance under adverse conditions. We benchmark visual navigation and SLAM methods within our framework, demonstrating its utility for testing real-world robustness in controlled yet diverse scenarios. By bridging the gap between physics accuracy and photorealistic rendering, our framework provides a powerful tool for advancing robotics research and sim-to-real transfer. Our open-source framework is available at https://unrealroboticslab.github.io/.

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arXiv2025

The Robot of Theseus: A modular robotic testbed for legged locomotion

Robotic models are useful for independently varying specific features, but most quadrupedal robots differ so greatly from animal morphologies that they have minimal biomechanical relevance. Commercially available quadrupedal robots are also prohibitively expensive for biological research programs and difficult to customize. Here, we present a low-cost quadrupedal robot with modular legs that can match a wide range of animal morphologies for biomechanical hypothesis testing. The Robot Of Theseus (TROT) costs approximately $4000 to build out of 3D printed parts and standard off-the-shelf supplies. Each limb consists of 2 or 3 rigid links; the proximal joint can be rotated to become a knee or elbow. Telescoping mechanisms vary the length of each limb link. The open-source software accommodates user-defined gaits and morphology changes. Effective leg length, or crouch, is determined by the four-bar linkage actuating each joint. The backdrivable motors can vary virtual spring stiffness and range of motion. Full descriptions of the TROT hardware and software are freely available online. We demonstrate the use of TROT to compare locomotion among extant, extinct, and theoretical morphologies. In addition to biomechanical hypothesis testing, we envision a variety of different applications for this low-cost, modular, legged robotic platform, including developing novel control strategies, clearing land mines, or remote exploration. All CAD and code is available for download on the TROT project page.

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arXiv2026

Realizing Robotic Swimming with Unified Fluid-Robot Multiphysics

Matching the swimming efficiency and agility of fish has remained an elusive goal in underwater robotics. Such locomotion capabilities rely on complex vortex interactions between the robot's body and the surrounding fluid. However, simulating these dynamics, which are governed by coupled ordinary and partial differential equations, is significantly more difficult than the multi-body dynamics of classical rigid robotic systems. We present a differentiable framework for simulating strongly coupled fluid-robot multiphysics as a unified optimization problem. The coupled manipulator and incompressible Navier-Stokes equations are derived together from a single Lagrangian using the principle of least action. We employ discrete variational mechanics to derive a stable, well-conditioned, and physically accurate scheme for jointly simulating articulated bodies and the surrounding fluid. We leverage the implicit function theorem to compute derivatives of the fully coupled dynamics. Using this simulator and its gradients, we realize undulating swimming gaits and optimize a highly dynamic C-start escape maneuver for a bioinspired eel robot. We validate both gaits on physical hardware, demonstrating successful sim-to-real transfer. Simulation code, hardware data, and schematics for the eel robot can be found here: https://unified-fluid-robot-multiphysics.github.io/

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arXiv2026

Assigning Multi-Robot Tasks to Multitasking Robots

One simplifying assumption in existing and well-performing task allocation methods is that the robots are single-tasking: each robot operates on a single task at any given time. While this assumption is harmless to make in some situations, it can be inefficient or even infeasible in others. In this paper, we consider assigning multi-robot tasks to multitasking robots. The key contribution is a novel task allocation framework that incorporates the consideration of physical constraints introduced by multitasking. This is in contrast to the existing work where such constraints are largely ignored. After formulating the problem, we propose a compilation to weighted MAX-SAT, which allows us to leverage existing solvers for a solution. A more efficient greedy heuristic is then introduced. For evaluation, we first compare our methods with a modern baseline that is efficient for single-tasking robots to validate the benefits of multitasking in synthetic domains. Then, using a site-clearing scenario in simulation, we further illustrate the complex task interaction considered by the multitasking robots in our approach to demonstrate its performance. Finally, we demonstrate a physical experiment to show how multitasking enabled by our approach can benefit task efficiency in a realistic setting.

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arXiv2025

PAPRLE (Plug-And-Play Robotic Limb Environment): A Modular Ecosystem for Robotic Limbs

We introduce PAPRLE (Plug-And-Play Robotic Limb Environment), a modular ecosystem that enables flexible placement and control of robotic limbs. With PAPRLE, a user can change the arrangement of the robotic limbs, and control them using a variety of input devices, including puppeteers, gaming controllers, and VR-based interfaces. This versatility supports a wide range of teleoperation scenarios and promotes adaptability to different task requirements. To further enhance configurability, we introduce a pluggable puppeteer device that can be easily mounted and adapted to match the target robot configurations. PAPRLE supports bilateral teleoperation through these puppeteer devices, agnostic to the type or configuration of the follower robot. By supporting both joint-space and task-space control, the system provides real-time force feedback, improving user fidelity and physical interaction awareness. The modular design of PAPRLE facilitates novel spatial arrangements of the limbs and enables scalable data collection, thereby advancing research in embodied AI and learning-based control. We validate PAPRLE in various real-world settings, demonstrating its versatility across diverse combinations of leader devices and follower robots. The system will be released as open source, including both hardware and software components, to support broader adoption and community-driven extension. Additional resources and demonstrations are available at the project website: https://uiuckimlab.github.io/paprle-pages

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arXiv2025

What Can Robots Teach Us About Trust and Reliance? An interdisciplinary dialogue between Social Sciences and Social Robotics

As robots find their way into more and more aspects of everyday life, questions around trust are becoming increasingly important. What does it mean to trust a robot? And how should we think about trust in relationships that involve both humans and non-human agents? While the field of Human-Robot Interaction (HRI) has made trust a central topic, the concept is often approached in fragmented ways. At the same time, established work in sociology, where trust has long been a key theme, is rarely brought into conversation with developments in robotics. This article argues that we need a more interdisciplinary approach. By drawing on insights from both social sciences and social robotics, we explore how trust is shaped, tested and made visible. Our goal is to open up a dialogue between disciplines and help build a more grounded and adaptable framework for understanding trust in the evolving world of human-robot interaction.

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arXiv2025

Stinger Robot: A Self-Bracing Robotic Platform for Autonomous Drilling in Confined Underground Environments

The increasing demand for critical raw materials has revitalized interest in abandoned underground mines, which pose extreme challenges for conventional drilling machinery due to confined, unstructured, and infrastructure-less environments. This paper presents the Stinger Robot, a novel compact robotic platform specifically designed for autonomous high-force drilling in such settings. The robot features a mechanically self-locking tri-leg bracing mechanism that enables stable anchoring to irregular tunnel surfaces. A key innovation lies in its force-aware, closed-loop control strategy, which enables force interaction with unstructured environments during bracing and drilling. Implemented as a finite-state machine in ROS 2, the control policy dynamically adapts leg deployment based on real-time contact feedback and load thresholds, ensuring stability without external supports. We demonstrate, through simulation and preliminary hardware tests, that the Stinger Robot can autonomously stabilize and drill in conditions previously inaccessible to nowadays mining machines. This work constitutes the first validated robotic architecture to integrate distributed force-bracing and autonomous drilling in underground environments, laying the groundwork for future collaborative mining operations using modular robot systems.

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arXiv2026

How Robot Dogs See the Unseeable: Improving Visual Interpretability via Peering for Exploratory Robots

In vegetated environments, such as forests, exploratory robots play a vital role in navigating complex, cluttered environments where human access is limited and traditional equipment struggles. Visual occlusion from obstacles, such as foliage, can severely obstruct a robot's sensors, impairing scene understanding. We show that "peering", a characteristic side-to-side movement used by insects to overcome their visual limitations, can also allow robots to markedly improve visual reasoning under partial occlusion. This is accomplished by applying core signal processing principles, specifically optical synthetic aperture sensing, together with the vision reasoning capabilities of modern large multimodal models. Peering enables real-time, high-resolution, and wavelength-independent perception, which is crucial for vision-based scene understanding across a wide range of applications. The approach is low-cost and immediately deployable on any camera-equipped robot. We investigated different peering motions and occlusion masking strategies, demonstrating that, unlike peering, state-of-the-art multi-view 3D vision techniques fail in these conditions due to their high susceptibility to occlusion. Our experiments were carried out on an industrial-grade quadrupedal robot. However, the ability to peer is not limited to such platforms, but potentially also applicable to bipedal, hexapod, wheeled, or crawling platforms. Robots that can effectively see through partial occlusion will gain superior perception abilities - including enhanced scene understanding, situational awareness, camouflage breaking, and advanced navigation in complex environments.

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arXiv2025

Robot-Powered Data Flywheels: Deploying Robots in the Wild for Continual Data Collection and Foundation Model Adaptation

Foundation models (FM) have unlocked powerful zero-shot capabilities in vision and language, yet their reliance on internet pretraining data leaves them brittle in unstructured, real-world settings. The messy, real-world data encountered during deployment (e.g. occluded or multilingual text) remains massively underrepresented in existing corpora. Robots, as embodied agents, are uniquely positioned to close this gap: they can act in physical environments to collect large-scale, real-world data that enriches FM training with precisely the examples current models lack. We introduce the Robot-Powered Data Flywheel, a framework that transforms robots from FM consumers into data generators. By deploying robots equipped with FMs in the wild, we enable a virtuous cycle: robots perform useful tasks while collecting real-world data that improves both domain-specific adaptation and domain-adjacent generalization. We instantiate this framework with Scanford, a mobile manipulator deployed in the East Asia Library for 2 weeks. Scanford autonomously scans shelves, identifies books using a vision-language model (VLM), and leverages the library catalog to label images without human annotation. This deployment both aids librarians and produces a dataset to finetune the underlying VLM, improving performance on the domain-specific in-the-wild library setting and on domain-adjacent multilingual OCR benchmarks. Using data collected from 2103 shelves, Scanford improves VLM performance on book identification from 32.0% to 71.8% and boosts domain-adjacent multilingual OCR from 24.8% to 46.6% (English) and 30.8% to 38.0% (Chinese), while saving an ~18.7 hrs of human time. These results highlight how robot-powered data flywheels can both reduce human effort in real deployments and unlock new pathways for continually adapting FMs to the messiness of reality. More details are at: https://scanford-robot.github.io

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arXiv2025

Impact of Robot Facial-Audio Expressions on Human Robot Trust Dynamics and Trust Repair

Despite recent advances in robotics and human-robot collaboration in the AEC industry, trust has mostly been treated as a static factor, with little guidance on how it changes across events during collaboration. This paper investigates how a robot's task performance and its expressive responses after outcomes shape the dynamics of human trust over time. To this end, we designed a controlled within-subjects study with two construction-inspired tasks, Material Delivery (physical assistance) and Information Gathering (perceptual assistance), and measured trust repeatedly (four times per task) using the 14-item Trust Perception Scale for HRI plus a redelegation choice. The robot produced two multimodal expressions, a "glad" display with a brief confirmation after success, and a "sad" display with an apology and a request for a second chance after failure. The study was conducted in a lab environment with 30 participants and a quadruped platform, and we evaluated trust dynamics and repair across both tasks. Results show that robot success reliably increases trust, failure causes sharp drops, and apology-based expressions partially restores trust (44% recovery in Material Delivery; 38% in Information Gathering). Item-level analysis indicates that recovered trust was driven mostly by interaction and communication factors, with competence recovering partially and autonomy aspects changing least. Additionally, age group and prior attitudes moderated trust dynamics with younger participants showed larger but shorter-lived changes, mid-20s participants exhibited the most durable repair, and older participants showed most conservative dynamics. This work provides a foundation for future efforts that adapt repair strategies to task demands and user profiles to support safe, productive adoption of robots on construction sites.

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arXiv2025

Beyond URDF: The Universal Robot Description Directory for Shared, Extensible, and Standardized Robot Models

Robots are typically described in software by specification files (e.g., URDF, SDF, MJCF, USD) that encode only basic kinematic, dynamic, and geometric information. As a result, downstream applications such as simulation, planning, and control must repeatedly re-derive richer data, leading to redundant computations, fragmented implementations, and limited standardization. In this work, we introduce the Universal Robot Description Directory (URDD), a modular representation that organizes derived robot information into structured, easy-to-parse JSON and YAML modules. Our open-source toolkit automatically generates URDDs from URDFs, with a Rust implementation supporting Bevy-based visualization. Additionally, we provide a JavaScript/Three.js viewer for web-based inspection of URDDs. Experiments on multiple robot platforms show that URDDs can be generated efficiently, encapsulate substantially richer information than standard specification files, and directly enable the construction of core robotics subroutines. URDD provides a unified, extensible resource for reducing redundancy and establishing shared standards across robotics frameworks. We conclude with a discussion on the limitations and implications of our work.

cs.RO↗

arXiv2026

A Decade of Human-Robot Interaction Through Immersive Lenses: Reviewing Extended Reality as a Research Instrument in Social Robotics

Over the past decade, Extended Reality (XR), including Virtual, Augmented, and Mixed Reality, gained attention as a research instrument in human-robot interaction studies, but remains underexplored in empirical investigations of social robotics. To map the field, we systematically reviewed empirical studies from 2015 to 2025. Of 6,527 peer-reviewed articles, only 33 met strict inclusion criteria. We examined (1) how XR and virtual social robots are used, focusing on the software and hardware employed and the application contexts in which they are deployed, (2) data collection and analysis methods, (3) demographics of the researchers and participants, and (4) the challenges and future directions. Our findings show that social XR-HRI research is still driven by laboratory simulations, while crucial specifications - such as the hardware, software, and robots used - are often not reported. Robots typically act as passive and hardly interactive visual stimulus, while the rich biosignal (e.g., eye-tracking) and logging (e.g. motion capturing) functions of modern head-mounted displays remain largely untapped. While there are gaps in demographic reporting, the research teams and samples are predominantly tech-centric, Western, young, and male. Key limitations include hardware delays, small homogeneous samples, and short study cycles. We propose a four-phase roadmap to establish social XR-HRI as a reliable research medium, which includes (1) strengthen application contexts, (2) more robust and testable technological iterations, (3) embedding diversity in samples and research teams, and (4) the need for reporting standards, e.g., in form of a suitable taxonomy. Advancing in these directions is essential for XR to mature from a lab prototype into an ecologically valid research instrument for social robotics.

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arXiv2026

Human-to-Robot Interaction: Learning from Video Demonstration for Robot Imitation

Learning from Demonstration (LfD) offers a promising paradigm for robot skill acquisition. Recent approaches attempt to extract manipulation commands directly from video demonstrations, yet face two critical challenges: (1) general video captioning models prioritize global scene features over task-relevant objects, producing descriptions unsuitable for precise robotic execution, and (2) end-to-end architectures coupling visual understanding with policy learning require extensive paired datasets and struggle to generalize across objects and scenarios. To address these limitations, we propose a novel ``Human-to-Robot'' imitation learning pipeline that enables robots to acquire manipulation skills directly from unstructured video demonstrations, inspired by the human ability to learn by watching and imitating. Our key innovation is a modular framework that decouples the learning process into two distinct stages: (1) Video Understanding, which combines Temporal Shift Modules (TSM) with Vision-Language Models (VLMs) to extract actions and identify interacted objects, and (2) Robot Imitation, which employs TD3-based deep reinforcement learning to execute the demonstrated manipulations. We validated our approach in PyBullet simulation environments with a UR5e manipulator and in a real-world experiment with a UF850 manipulator across four fundamental actions: reach, pick, move, and put. For video understanding, our method achieves 89.97% action classification accuracy and BLEU-4 scores of 0.351 on standard objects and 0.265 on novel objects, representing improvements of 76.4% and 128.4% over the best baseline, respectively. For robot manipulation, our framework achieves an average success rate of 87.5% across all actions, with 100% success on reaching tasks and up to 90% on complex pick-and-place operations. The project website is available at https://thanhnguyencanh.github.io/LfD4hri.

cs.RO↗

arXiv2026

Directing the Robot: Scaffolding Creative Human-AI-Robot Interaction

Robots are moving beyond industrial settings into creative, educational, and public environments where interaction is open-ended and improvisational. Yet much of human-AI-robot interaction remains framed around performance and efficiency, positioning humans as supervisors rather than collaborators. We propose a re-framing of AI interaction with robots as scaffolding: infrastructure that enables humans to shape robotic behaviour over time while remaining meaningfully in control. Through scenarios from creative practice, learning-by-teaching, and embodied interaction, we illustrate how humans can act as executive directors, defining intent and steering revisions, while AI mediates between human expression and robotic execution. We outline design and evaluation implications that foreground creativity, agency, and flow. Finally, we discuss open challenges in social, scalable, and mission-critical contexts. We invite the community to rethink interacting with Robots and AI not as autonomy, but as sustained support for human creativity.

cs.HC↗