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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 325 records · Page 18Linked to original sources

arXiv2026

A Soft Robotic Interface for Chick-Robot Affective Interactions

The potential of Animal-Robot Interaction (ARI) in welfare applications depends on how much an animal perceives a robotic agent as socially relevant, non-threatening and potentially attractive (acceptance). Here, we present an animal-centered soft robotic affective interface for newly hatched chicks (Gallus gallus). The soft interface provides safe and controllable cues, including warmth, breathing-like rhythmic deformation, and face-like visual stimuli. We evaluated chick acceptance of the interface and chick-robot interactions by measuring spontaneous approach and touch responses during video tracking. Overall, chicks approached and spent increasing time on or near the interface, demonstrating acceptance of the device. Across different layouts, chicks showed strong preference for warm thermal stimulation, which increased over time. Face-like visual cues elicited a swift and stable preference, speeding up the initial approach to the tactile interface. Although the breathing cue did not elicit any preference, neither did it trigger avoidance, paving the way for further exploration. These findings translate affective interface concepts to ARI, demonstrating that appropriate soft, thermal and visual stimuli can sustain early chick-robot interactions. This work establishes a reliable evaluation protocol and a safe baseline for designing multimodal robotic devices for animal welfare and neuroscientific research.

cs.RO↗

arXiv2026

Do Rigid-Body Simulators Dream of Soft Robots? Learning Contact-Rich Manipulation for Tendon-Driven Continuum Robots

Learning contact-rich, whole-body manipulation for soft continuum robots is held back by the lack of simulation infrastructure that has accelerated rigid-robot manipulation. Existing soft robot simulators are physically grounded but lack the contact handling, actuation support, or learning integration needed for contact-rich manipulation; rigid-body approximations offer these capabilities but sacrifice physical grounding. We bridge this gap for tendon-driven continuum robots (TDCRs) by deriving a continuum-mechanics-informed discretization that places the soft robot natively inside MuJoCo, unifying tendon forces, body contact, and dynamics in a single physics pipeline. We validate the simulator against a Cosserat rod reference (static and dynamic) and real TDCR hardware. We then train state-based imitation learning policies via teleoperation in simulation and deploy them zero-shot to a physical 3-segment TDCR on a 7-DoF Franka arm across two contact-rich manipulation tasks. To our knowledge, this is the first demonstration of sim-to-real transfer for contact-rich manipulation with continuum robots.

cs.RO↗

arXiv2026

The World According to a Social Robot -- Augmenting Human-Robot Dialogue With Vision Language Models

Vision Language Models (VLMs) enable robots to visually perceive their environment as well as the actions and characteristics of their conversation partner or humans in collaboration. Especially for social robots deployed in everyday settings and for uncomplicated, natural use, it is essential that the robot has an understanding of situations that is appropriate to human customs. This paper presents initial experiences with the application of a Mistral AI language model with a Pepper robot for Human-Robot Interaction (HRI) in dialogue, as well as an investigation of the effects of additional visual information on response time in different models. The results show that incorporating visual information adds context to the dialogue with only a moderate increase in response time, enabling both the robot and the human to take into account unspoken elements of the situation. Furthermore, using an LLM hosted in Europe offers a solution that complies with European data protection regulations and can therefore facilitate real-life applications more easily.

cs.HC↗

arXiv2026

CRESSim-Neo: A Batched GPU Simulation Engine for Surgical Robotics and Robot Learning

We introduce CRESSim-Neo, a batched GPU simulation engine for surgical robotics and robot learning. CRESSim-Neo combines position-based simulation of rigid bodies, deformable tissues, fluids, and strands with batched rendering, surgery-specific sensing, and a GPU-resident data pipeline. The engine supports applications including tissue manipulation, fluid suction, suturing, cable-driven robots, and ultrasound image synthesis. Direct access to physics and rendering buffers enables GPU-resident robot learning and zero-copy PyTorch integration using DLPack. We demonstrate CRESSim-Neo across rigid-body, deformable-body, and fluid simulation tasks, including vision-based and surgical robot-learning scenarios. On an NVIDIA RTX 4090, the engine achieves up to 2.03 million environment steps per second for 8192 parallel CartPole environments, and scales to batched surgical scenarios involving tissue deformation, fluid interaction, and ultrasound sensing. Overall, CRESSim-Neo provides a unified and scalable platform for surgical simulation, synthetic data generation, and surgical robot learning.

cs.RO↗

arXiv2026

Why did My Robot Just Change Personality? Prompting Guidelines for a Grounded Robot Persona in LLM-Based HRI

Large language models (LLMs) are increasingly used for verbal interaction in social robots, yet prompt design in human-robot interaction (HRI) remains underspecified. As a result, robots may present hallucinated capabilities, unclear behavioural boundaries, and misleading personas. This paper develops a framework for prompt design in LLM-based robots and introduces a structured prompt template comprising eight functional components through which robot behaviour can be specified, bounded, and adapted. The framework is grounded in a review of prior LLM-based HRI work and complemented by survey and discussion data from HRI experts gathered at the Robo-Identity workshop at IEEE RO-MAN 2025 (N=27). The qualitative findings highlight limited legibility of robot personality, the need for user adaptation, and strong ethical concerns about safety, deception, and governance. Based on these findings, we present prompting guidelines accompanied by proof-of-concept template as a structured design and reporting aid for HRI research. We argue that prompt design should be treated as a socio-technical problem rather than a minor implementation detail, requiring explicit capability boundaries, transparent behavioural assumptions, and context-sensitive safeguards to support reliable and interpretable HRI.

cs.AI↗

arXiv2026

From Gameplay to Policy: Towards Scalable Robot Data Collection via Gamified Robot-Free Interaction

Learning generalizable robot manipulation policies requires large-scale and diverse interaction data, yet collecting real-world demonstrations remains costly and difficult to scale. Existing approaches to data collection are either dependent on specific robot hardware that limits crowdsourcing and transferability, or suffer from incomplete annotation and limited behavioral diversity. Inspired by how games sustain long-term human engagement, we explore an alternative paradigm that turns data collection into an engaging gameplay experience and transfers the resulting human manipulation experience to real robots. We present Project Kitchen, a VR-based gamified egocentric data collection platform that elicits diverse, goal-directed manipulation while remaining independent of specific robot embodiments and hardware, making it applicable to broader and potentially large-scale deployment. To bridge the game-to-real gap, we further introduce Game2Policy, which extracts embodiment-invariant affordance cues, including contact points and sub-goal states, from gameplay trajectories. An affordance model is pre-trained on game-collected data and then jointly fine-tuned with downstream policies using only a handful of real-robot demonstrations. Experiments show that Game2Policy improves average success rates by 10.0 points in simulation and 18.3 points on real robots in the few-shot setting. User studies and quantitative analyses further show that Project Kitchen promotes diverse manipulation behaviors and provides an engaging data collection experience. These results demonstrate the potential of gamified virtual environments as a scalable source of manipulation knowledge. The platform and code will be released upon acceptance.

cs.RO↗

arXiv2026

Laser-Tracker-Assisted Camera-to-Robot Calibration for Mobile Robots

We present a laser-tracker-assisted hand-eye calibration method for camera-equipped mobile robots. The method combines laser-tracker-based 3D metrology with camera-based 2D observations. Building on our previous laser-tracker-assisted camera-to-robot calibration method for ground-observing mobile robots, we present a generalized formulation for calibrating the camera pose in the coordinate system of tracker-localized mobile robots. The new approach relaxes assumptions of our previous method on robot and camera configuration by chaining multiple calibration targets resulting in a more general approach supporting various camera-equipped mobile robot systems.

cs.RO↗

arXiv2025

Centralization vs. decentralization in multi-robot sweep coverage with ground robots and UAVs

In swarm robotics, decentralized control is often proposed as a more scalable and fault-tolerant alternative to centralized control. However, centralized behaviors are often faster and more efficient than their decentralized counterparts. In any given application, the goals and constraints of the task being solved should guide the choice to use centralized control, decentralized control, or a combination of the two. Currently, the exact trade-offs that exist between centralization and decentralization are not well defined. In this paper, we compare the performance of centralization and decentralization in the example task of sweep coverage, across five different types of multi-robot control structures: random walk, decentralized with beacons, hybrid formation control using self-organizing hierarchy, centralized formation control, and predetermined. In all five approaches, the coverage task is completed by a group of ground robots. In each approach, except for the random walk, the ground robots are assisted by UAVs, acting as supervisors or beacons. We compare the approaches in terms of three performance metrics for which centralized approaches are expected to have an advantage -- coverage completeness, coverage uniformity, and sweep completion time -- and two metrics for which decentralized approaches are expected to have an advantage -- scalability (4, 8, or 16 ground robots) and fault tolerance (0%, 25%, 50%, or 75% ground robot failure).

cs.RO↗

arXiv2019

Robotic Hierarchical Graph Neurons. A novel implementation of HGN for swarm robotic behaviour control

This paper explores the use of a novel form of Hierarchical Graph Neurons (HGN) for in-operation behaviour selection in a swarm of robotic agents. This new HGN is called Robotic-HGN (R-HGN), as it matches robot environment observations to environment labels via fusion of match probabilities from both temporal and intra-swarm collections. This approach is novel for HGN as it addresses robotic observations being pseudo-continuous numbers, rather than categorical values. Additionally, the proposed approach is memory and computation-power conservative and thus is acceptable for use in mobile devices such as single-board computers, which are often used in mobile robotic agents. This R-HGN approach is validated against individual behaviour implementation and random behaviour selection. This contrast is made in two sets of simulated environments: environments designed to challenge the held behaviours of the R-HGN, and randomly generated environments which are more challenging for the robotic swarm than R-HGN training conditions. R-HGN has been found to enable appropriate behaviour selection in both these sets, allowing significant swarm performance in pre-trained and unexpected environment conditions.

cs.RO↗

arXiv2019

Brain-Computer Interface meets ROS: A robotic approach to mentally drive telepresence robots

This paper shows and evaluates a novel approach to integrate a non-invasive Brain-Computer Interface (BCI) with the Robot Operating System (ROS) to mentally drive a telepresence robot. Controlling a mobile device by using human brain signals might improve the quality of life of people suffering from severe physical disabilities or elderly people who cannot move anymore. Thus, the BCI user is able to actively interact with relatives and friends located in different rooms thanks to a video streaming connection to the robot. To facilitate the control of the robot via BCI, we explore new ROS-based algorithms for navigation and obstacle avoidance, making the system safer and more reliable. In this regard, the robot can exploit two maps of the environment, one for localization and one for navigation, and both can be used also by the BCI user to watch the position of the robot while it is moving. As demonstrated by the experimental results, the user's cognitive workload is reduced, decreasing the number of commands necessary to complete the task and helping him/her to keep attention for longer periods of time.

cs.RO↗

arXiv2023

Towards Human-Robot Collaboration with Parallel Robots by Kinetostatic Analysis, Impedance Control and Contact Detection

Parallel robots provide the potential to be leveraged for human-robot collaboration (HRC) due to low collision energies even at high speeds resulting from their reduced moving masses. However, the risk of unintended contact with the leg chains increases compared to the structure of serial robots. As a first step towards HRC, contact cases on the whole parallel robot structure are investigated and a disturbance observer based on generalized momenta and measurements of motor current is applied. In addition, a Kalman filter and a second-order sliding-mode observer based on generalized momenta are compared in terms of error and detection time. Gearless direct drives with low friction improve external force estimation and enable low impedance. The experimental validation is performed with two force-torque sensors and a kinetostatic model. This allows a new identification method of the motor torque constant of an assembled parallel robot to estimate external forces from the motor current and via a dynamics model. A Cartesian impedance control scheme for compliant robot-environmental dynamics with stiffness from 0.1-2N/mm and the force observation for low forces over the entire structure are validated. The observers are used for collisions and clamping at velocities of 0.4-0.9m/s for detection within 9-58ms and a reaction in the form of a zero-g mode.

cs.RO↗

arXiv2026

AI-IoT-Robotics Integration: Survey of Frameworks, Emerging Trends, and the Path Toward Connected Robotics

The convergence of Artificial Intelligence, the Internet of Things, and Robotics is no longer a futuristic vision; it is rapidly becoming the foundation of real-time, intelligent, and context-aware systems. AI enables perception and reasoning, IoT provides scalable sensing and communication, and robotics delivers embodied actuation. Despite significant progress in pairwise combinations such as AIoT and the Internet of Robotic Things (IoRT), there remains a lack of unified design frameworks that fully integrate all three. This survey synthesizes the state-of-the-art across these domains, emphasizing the emerging role of Small Language Models (SLMs) at the edge and Large Language Models (LLMs) in the cloud for distributed cognition and autonomous decision-making. We propose a modular system architecture that aligns with these trends, analyze persistent gaps in interoperability and feedback control, and classify existing work by integration depth. Our review highlights how hybrid SLM-LLM systems, when coupled with IoT infrastructure and robotic agents, can address challenges in real-time adaptation, scalability, and reliability. This work offers a conceptual and technical roadmap for designing next-generation AI-IoT-Robotic ecosystems that are modular, interpretable, and capable of learning within dynamic environments, paving the way for the emerging paradigm of Connected Robotics and Physical AI.

cs.RO↗

arXiv2024

An Aerial Manipulator for Robot-to-robot Torch Relay Task: System Design and Control Scheme

Torch relay is an important tradition of the Olympics and heralds the start of the Games. Robots applied in the torch relay activity can not only demonstrate the technological capability of humans to the world but also provide a sight of human lives with robots in the future. This article presents an aerial manipulator designed for the robot-to-robot torch relay task of the Beijing 2022 Winter Olympics. This aerial manipulator system is composed of a quadrotor, a 3 DoF (Degree of Freedom) manipulator, and a monocular camera. This article primarily describes the system design and system control scheme of the aerial manipulator. The experimental results demonstrate that it can complete robot-to-robot torch relay task under the guidance of vision in the ice and snow field.

cs.RO↗

arXiv2019

MobiKa - Low-Cost Mobile Robot for Human-Robot Interaction

One way to allow elderly people to stay longer in their homes is to use of service robots to support them with everyday tasks. With this goal, we design, develop and evaluate a low-cost mobile robot to communicate with elderly people. The main idea is to create an affordable communication assistant robot which is optimized for multimodal Human-Robot Interaction (HRI). Our robot can navigate autonomously through dynamic environments using a new algorithm to calculate poses for approaching persons. The robot was tested in a real life scenario in an elderly care home.

cs.RO↗

arXiv2014

Towards the Safety of Human-in-the-Loop Robotics: Challenges and Opportunities for Safety Assurance of Robotic Co-Workers

The success of the human-robot co-worker team in a flexible manufacturing environment where robots learn from demonstration heavily relies on the correct and safe operation of the robot. How this can be achieved is a challenge that requires addressing both technical as well as human-centric research questions. In this paper we discuss the state of the art in safety assurance, existing as well as emerging standards in this area, and the need for new approaches to safety assurance in the context of learning machines. We then focus on robotic learning from demonstration, the challenges these techniques pose to safety assurance and indicate opportunities to integrate safety considerations into algorithms "by design". Finally, from a human-centric perspective, we stipulate that, to achieve high levels of safety and ultimately trust, the robotic co-worker must meet the innate expectations of the humans it works with. It is our aim to stimulate a discussion focused on the safety aspects of human-in-the-loop robotics, and to foster multidisciplinary collaboration to address the research challenges identified.

cs.RO↗

arXiv2024

Malleable Robots: Reconfigurable Robotic Arms with Continuum Links of Variable Stiffness

Through the implementation of reconfigurability to achieve flexibility and adaptation to tasks by morphology changes rather than by increasing the number of joints, malleable robots present advantages over traditional serial robot arms in regards to reduced weight, size, and cost. While limited in degrees of freedom (DOF), malleable robots still provide versatility across operations typically served by systems using higher DOF than required by the tasks. In this paper, we present the creation of a 2-DOF malleable robot, detailing the design of joints and malleable link, along with its modelling through forward and inverse kinematics, and a reconfiguration methodology that informs morphology changes based on end effector location -- determining how the user should reshape the robot to enable a task previously unattainable. The recalibration and motion planning for making robot motion possible after reconfiguration are also discussed, and thorough experiments with the prototype to evaluate accuracy and reliability of the system are presented. Results validate the approach and pave the way for further research in the area.

cs.RO↗

arXiv2025

To Lead or to Follow? Adaptive Robot Task Planning in Human-Robot Collaboration

Adaptive task planning is fundamental to ensuring effective and seamless human-robot collaboration. This paper introduces a robot task planning framework that takes into account both human leading/following preferences and performance, specifically focusing on task allocation and scheduling in collaborative settings. We present a proactive task allocation approach with three primary objectives: enhancing team performance, incorporating human preferences, and upholding a positive human perception of the robot and the collaborative experience. Through a user study, involving an autonomous mobile manipulator robot working alongside participants in a collaborative scenario, we confirm that the task planning framework successfully attains all three intended goals, thereby contributing to the advancement of adaptive task planning in human-robot collaboration. This paper mainly focuses on the first two objectives, and we discuss the third objective, participants' perception of the robot, tasks, and collaboration in a companion paper.

cs.RO↗

arXiv2018

Compact Real-time avoidance on a Humanoid Robot for Human-robot Interaction

With robots leaving factories and entering less controlled domains, possibly sharing the space with humans, safety is paramount and multimodal awareness of the body surface and the surrounding environment is fundamental. Taking inspiration from peripersonal space representations in humans, we present a framework on a humanoid robot that dynamically maintains such a protective safety zone, composed of the following main components: (i) a human 2D keypoints estimation pipeline employing a deep learning based algorithm, extended here into 3D using disparity; (ii) a distributed peripersonal space representation around the robot's body parts; (iii) a reaching controller that incorporates all obstacles entering the robot's safety zone on the fly into the task. Pilot experiments demonstrate that an effective safety margin between the robot's and the human's body parts is kept. The proposed solution is flexible and versatile since the safety zone around individual robot and human body parts can be selectively modulated---here we demonstrate stronger avoidance of the human head compared to rest of the body. Our system works in real time and is self-contained, with no external sensory equipment and use of onboard cameras only.

cs.RO↗