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Robotics

Explore arXiv robotics papers and cs.RO metadata. Search for robot learning, motion planning and control, then check experiments in the source manuscript.

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arXiv2022

PuzzleBots: Physical Coupling of Robot Swarms

Robot swarms have been shown to improve the ability of individual robots by inter-robot collaboration. In this paper, we present the PuzzleBots - a low-cost robotic swarm system where robots can physically couple with each other to form functional structures with minimum energy consumption while maintaining individual mobility to navigate within the environment. Each robot has knobs and holes along the sides of its body so that the robots can couple by inserting the knobs into the holes. We present the characterization of knob design and the result of gap-crossing behavior with up to nine robots. We show with hardware experiments that the robots are able to couple with each other to cross gaps and decouple to perform individual tasks. We anticipate the PuzzleBots will be useful in unstructured environments as individuals and coupled systems in real-world applications.

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arXiv2025

GenGrid: A Generalised Distributed Experimental Environmental Grid for Swarm Robotics

GenGrid is a novel comprehensive open-source, distributed platform intended for conducting extensive swarm robotic experiments. The modular platform is designed to run swarm robotics experiments that are compatible with different types of mobile robots ranging from Colias, Kilobot, and E puck. The platform offers programmable control over the experimental setup and its parameters and acts as a tool to collect swarm robot data, including localization, sensory feedback, messaging, and interaction. GenGrid is designed as a modular grid of attachable computing nodes that offers bidirectional communication between the robotic agent and grid nodes and within grids. The paper describes the hardware and software architecture design of the GenGrid system. Further, it discusses some common experimental studies covering multi-robot and swarm robotics to showcase the platform's use. GenGrid of 25 homogeneous cells with identical sensing and communication characteristics with a footprint of 37.5 cm X 37.5 cm, exhibits multiple capabilities with minimal resources. The open-source hardware platform is handy for running swarm experiments, including robot hopping based on multiple gradients, collective transport, shepherding, continuous pheromone deposition, and subsequent evaporation. The low-cost, modular, and open-source platform is significant in the swarm robotics research community, which is currently driven by commercial platforms that allow minimal modifications.

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arXiv2023

Range Limited Coverage Control using Air-Ground Multi-Robot Teams

In this paper, we investigate how heterogeneous multi-robot systems with different sensing capabilities can observe a domain with an apriori unknown density function. Common coverage control techniques are targeted towards homogeneous teams of robots and do not consider what happens when the sensing capabilities of the robots are vastly different. This work proposes an extension to Lloyd's algorithm that fuses coverage information from heterogeneous robots with differing sensing capabilities to effectively observe a domain. Namely, we study a bimodal team of robots consisting of aerial and ground agents. In our problem formulation we use aerial robots with coarse domain sensors to approximate the number of ground robots needed within their sensing region to effectively cover it. This information is relayed to ground robots, who perform an extension to the Lloyd's algorithm that balances a locally focused coverage controller with a globally focused distribution controller. The stability of the Lloyd's algorithm extension is proven and its performance is evaluated through simulation and experiments using the Robotarium, a remotely-accessible, multi-robot testbed.

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arXiv2024

Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework

Recently, data-driven models such as deep neural networks have shown to be promising tools for modelling and state inference in soft robots. However, voluminous amounts of data are necessary for deep models to perform effectively, which requires exhaustive and quality data collection, particularly of state labels. Consequently, obtaining labelled state data for soft robotic systems is challenged for various reasons, including difficulty in the sensorization of soft robots and the inconvenience of collecting data in unstructured environments. To address this challenge, in this paper, we propose a semi-supervised sequential variational Bayes (DSVB) framework for transfer learning and state inference in soft robots with missing state labels on certain robot configurations. Considering that soft robots may exhibit distinct dynamics under different robot configurations, a feature space transfer strategy is also incorporated to promote the adaptation of latent features across multiple configurations. Unlike existing transfer learning approaches, our proposed DSVB employs a recurrent neural network to model the nonlinear dynamics and temporal coherence in soft robot data. The proposed framework is validated on multiple setup configurations of a pneumatic-based soft robot finger. Experimental results on four transfer scenarios demonstrate that DSVB performs effective transfer learning and accurate state inference amidst missing state labels. The data and code are available at https://github.com/shageenderan/DSVB.

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arXiv2023

Transferring Implicit Knowledge of Non-Visual Object Properties Across Heterogeneous Robot Morphologies

Humans leverage multiple sensor modalities when interacting with objects and discovering their intrinsic properties. Using the visual modality alone is insufficient for deriving intuition behind object properties (e.g., which of two boxes is heavier), making it essential to consider non-visual modalities as well, such as the tactile and auditory. Whereas robots may leverage various modalities to obtain object property understanding via learned exploratory interactions with objects (e.g., grasping, lifting, and shaking behaviors), challenges remain: the implicit knowledge acquired by one robot via object exploration cannot be directly leveraged by another robot with different morphology, because the sensor models, observed data distributions, and interaction capabilities are different across these different robot configurations. To avoid the costly process of learning interactive object perception tasks from scratch, we propose a multi-stage projection framework for each new robot for transferring implicit knowledge of object properties across heterogeneous robot morphologies. We evaluate our approach on the object-property recognition and object-identity recognition tasks, using a dataset containing two heterogeneous robots that perform 7,600 object interactions. Results indicate that knowledge can be transferred across robots, such that a newly-deployed robot can bootstrap its recognition models without exhaustively exploring all objects. We also propose a data augmentation technique and show that this technique improves the generalization of models. We release our code and datasets, here: https://github.com/gtatiya/Implicit-Knowledge-Transfer.

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arXiv2018

Efficient Multi-Robot Coverage of a Known Environment

This paper addresses the complete area coverage problem of a known environment by multiple-robots. Complete area coverage is the problem of moving an end-effector over all available space while avoiding existing obstacles. In such tasks, using multiple robots can increase the efficiency of the area coverage in terms of minimizing the operational time and increase the robustness in the face of robot attrition. Unfortunately, the problem of finding an optimal solution for such an area coverage problem with multiple robots is known to be NP-complete. In this paper we present two approximation heuristics for solving the multi-robot coverage problem. The first solution presented is a direct extension of an efficient single robot area coverage algorithm, based on an exact cellular decomposition. The second algorithm is a greedy approach that divides the area into equal regions and applies an efficient single-robot coverage algorithm to each region. We present experimental results for two algorithms. Results indicate that our approaches provide good coverage distribution between robots and minimize the workload per robot, meanwhile ensuring complete coverage of the area.

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arXiv2022

Conservative Filtering for Heterogeneous Decentralized Data Fusion in Dynamic Robotic Systems

This paper presents a method for Bayesian multi-robot peer-to-peer data fusion where any pair of autonomous robots hold non-identical, but overlapping parts of a global joint probability distribution, representing real world inference tasks (e.g., mapping, tracking). It is shown that in dynamic stochastic systems, filtering, which corresponds to marginalization of past variables, results in direct and hidden dependencies between variables not mutually monitored by the robots, which might lead to an overconfident fused estimate. The paper makes both theoretical and practical contributions by providing (i) a rigorous analysis of the origin of the dependencies and and (ii) a conservative filtering algorithm for heterogeneous data fusion in dynamic systems that can be integrated with existing fusion algorithms. This work uses factor graphs as an analysis tool and an inference engine. Each robot in the network maintains a local factor graph and communicates only relevant parts of it (a sub-graph) to its neighboring robot. We discuss the applicability to various multi-robot robotic applications and demonstrate the performance using a multi-robot multi-target tracking simulation, showing that the proposed algorithm produces conservative estimates at each robot.

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arXiv2024

Casting manipulation of unknown string by robot arm

Casting manipulation has been studied to expand the robot's movable range. In this manipulation, the robot throws and reaches the end effector to a distant target. Usually, a special casting manipulator, which consists of rigid arm links and specific flexible linear objects, is constructed for an effective casting manipulation. However, the special manipulator cannot perform normal manipulations, such as picking and placing, grasping, and operating objects. We propose that the normal robot arm, which can perform normal tasks, picks up an unknown string in the surrounding environment and realizes casting manipulation with it. As the properties of the string are not provided in advance, it is crucial how to reflect it in casting manipulation. This is realized by the motion generation of the robot arm with the simulation of string movement, actual string manipulation by the robot arm, and string parameter estimation from the actual string movement. After repeating these three steps, the simulated string movement approximates the actual to realize casting manipulation with the unknown string. We confirmed the effectiveness of the proposed method through experiments. The try of this study will lead to enhancement of the performance of home service robot, exploration robot, rescue robot and entertainment robot.

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arXiv2021

In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning

In this study, we report the successful execution of in-air knotting of rope using a dual-arm two-finger robot based on deep learning. Owing to its flexibility, the state of the rope was in constant flux during the operation of the robot. This required the robot control system to dynamically correspond to the state of the object at all times. However, a manual description of appropriate robot motions corresponding to all object states is difficult to be prepared in advance. To resolve this issue, we constructed a model that instructed the robot to perform bowknots and overhand knots based on two deep neural networks trained using the data gathered from its sensorimotor, including visual and proximity sensors. The resultant model was verified to be capable of predicting the appropriate robot motions based on the sensory information available online. In addition, we designed certain task motions based on the Ian knot method using the dual-arm two-fingers robot. The designed knotting motions do not require a dedicated workbench or robot hand, thereby enhancing the versatility of the proposed method. Finally, experiments were performed to estimate the knotting performance of the real robot while executing overhand knots and bowknots on rope and its success rate. The experimental results established the effectiveness and high performance of the proposed method.

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arXiv2023

Assignment Algorithms for Multi-Robot Multi-Target Tracking with Sufficient and Limited Sensing Capability

We study the problem of assigning robots with actions to track targets. The objective is to optimize the robot team's tracking quality which can be defined as the reduction in the uncertainty of the targets' states. Specifically, we consider two assignment problems given the different sensing capabilities of the robots. In the first assignment problem, a single robot is sufficient to track a target. To this end, we present a greedy algorithm (Algorithm 1) that assigns a robot with its action to each target. We prove that the greedy algorithm has a 1/2 approximation bound and runs in polynomial time. Then, we study the second assignment problem where two robots are necessary to track a target. We design another greedy algorithm (Algorithm 2) that assigns a pair of robots with their actions to each target. We prove that the greedy algorithm achieves a 1/3 approximation bound and has a polynomial running time. Moreover, we illustrate the performance of the two greedy algorithms in the ROS-Gazebo environment where the tracking patterns of one robot following one target using Algorithm 1 and two robots following one target using Algorithm 2 are clearly observed. Further, we conduct extensive comparisons to demonstrate that the two greedy algorithms perform close to their optimal counterparts and much better than their respective (1/2 and 1/3) approximation bounds.

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arXiv2023

Scalable Multi-robot Motion Planning for Congested Environments With Topological Guidance

Multi-robot motion planning (MRMP) is the problem of finding collision-free paths for a set of robots in a continuous state space. The difficulty of MRMP increases with the number of robots and is exacerbated in environments with narrow passages that robots must pass through, like warehouse aisles where coordination between robots is required. In single-robot settings, topology-guided motion planning methods have shown improved performance in these constricted environments. In this work, we extend an existing topology-guided single-robot motion planning method to the multi-robot domain to leverage the improved efficiency provided by topological guidance. We demonstrate our method's ability to efficiently plan paths in complex environments with many narrow passages, scaling to robot teams of size up to 25 times larger than existing methods in this class of problems. By leveraging knowledge of the topology of the environment, we also find higher-quality solutions than other methods.

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arXiv2024

GISR: Geometric Initialization and Silhouette-based Refinement for Single-View Robot Pose and Configuration Estimation

In autonomous robotics, measurement of the robot's internal state and perception of its environment, including interaction with other agents such as collaborative robots, are essential. Estimating the pose of the robot arm from a single view has the potential to replace classical eye-to-hand calibration approaches and is particularly attractive for online estimation and dynamic environments. In addition to its pose, recovering the robot configuration provides a complete spatial understanding of the observed robot that can be used to anticipate the actions of other agents in advanced robotics use cases. Furthermore, this additional redundancy enables the planning and execution of recovery protocols in case of sensor failures or external disturbances. We introduce GISR - a deep configuration and robot-to-camera pose estimation method that prioritizes execution in real-time. GISR consists of two modules: (i) a geometric initialization module that efficiently computes an approximate robot pose and configuration, and (ii) a deep iterative silhouette-based refinement module that arrives at a final solution in just a few iterations. We evaluate GISR on publicly available data and show that it outperforms existing methods of the same class in terms of both speed and accuracy, and can compete with approaches that rely on ground-truth proprioception and recover only the pose.

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arXiv2026

Transport and Delivery of Objects with a Soft Everting Robot

Soft everting robots present significant advantages over traditional rigid robots, including enhanced dexterity, improved environmental interaction, and safe navigation in unpredictable environments. While soft everting robots have been widely demonstrated for exploration type tasks, their potential to move and deploy payloads in such tasks has been less investigated, with previous work focusing on sensors and tools for the robot. Leveraging the navigation capabilities, and deployed body, of the soft everting robot to deliver payloads in hazardous areas, e.g. carrying a water bottle to a person stuck under debris, would represent a significant capability in many applications. In this work, we present an analysis of how soft everting robots can be used to deploy larger, heavier payloads through the inside of the robot. We analyze both what objects can be deployed and what terrain features they can be carried through. Building on existing models, we present methods to quantify the effects of payloads on robot growth and self-support, and develop a model to predict payload slip. We then experimentally quantify payload transport using soft everting robot with a variety of payload shapes, sizes, and weights and though a series of tasks: steering, vertical transport, movement through holes, and movement across gaps. Overall, the results show that we can transport payloads in a variety of shapes and up to 1.5kg in weight and that we can move through circular apertures with as little as 0.01cm clearance around payloads, carry out discrete turns up to 135 degrees, and move across unsupported gaps of 1.15m in length.

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arXiv2020

Vine Robots: Design, Teleoperation, and Deployment for Navigation and Exploration

A new class of continuum robots has recently been explored, characterized by tip extension, significant length change, and directional control. Here, we call this class of robots "vine robots," due to their similar behavior to plants with the growth habit of trailing. Due to their growth-based movement, vine robots are well suited for navigation and exploration in cluttered environments, but until now, they have not been deployed outside the lab. Portability of these robots and steerability at length scales relevant for navigation are key to field applications. In addition, intuitive human-in-the-loop teleoperation enables movement in unknown and dynamic environments. We present a vine robot system that is teleoperated using a custom designed flexible joystick and camera system, long enough for use in navigation tasks, and portable for use in the field. We report on deployment of this system in two scenarios: a soft robot navigation competition and exploration of an archaeological site. The competition course required movement over uneven terrain, past unstable obstacles, and through a small aperture. The archaeological site required movement over rocks and through horizontal and vertical turns. The robot tip successfully moved past the obstacles and through the tunnels, demonstrating the capability of vine robots to achieve navigation and exploration tasks in the field.

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arXiv2026

MEVIUS2: Practical Open-Source Quadruped Robot with Sheet Metal Welding and Multimodal Perception

Various quadruped robots have been developed to date, and thanks to reinforcement learning, they are now capable of traversing diverse types of rough terrain. In parallel, there is a growing trend of releasing these robot designs as open-source, enabling researchers to freely build and modify robots themselves. However, most existing open-source quadruped robots have been designed with 3D printing in mind, resulting in structurally fragile systems that do not scale well in size, leading to the construction of relatively small robots. Although a few open-source quadruped robots constructed with metal components exist, they still tend to be small in size and lack multimodal sensors for perception, making them less practical. In this study, we developed MEVIUS2, an open-source quadruped robot with a size comparable to Boston Dynamics' Spot, whose structural components can all be ordered through e-commerce services. By leveraging sheet metal welding and metal machining, we achieved a large, highly durable body structure while reducing the number of individual parts. Furthermore, by integrating sensors such as LiDARs and a high dynamic range camera, the robot is capable of detailed perception of its surroundings, making it more practical than previous open-source quadruped robots. We experimentally validated that MEVIUS2 can traverse various types of rough terrain and demonstrated its environmental perception capabilities. All hardware, software, and training environments can be obtained from Supplementary Materials or https://github.com/haraduka/mevius2.

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arXiv2021

End-User Programming of Low- and High-Level Actions for Robotic Task Planning

Programming robots for general purpose applications is extremely challenging due to the great diversity of end-user tasks ranging from manufacturing environments to personal homes. Recent work has focused on enabling end-users to program robots using Programming by Demonstration. However, teaching robots new actions from scratch that can be reused for unseen tasks remains a difficult challenge and is generally left up to robotic experts. We propose iRoPro, an interactive Robot Programming framework that allows end-users to teach robots new actions from scratch and reuse them with a task planner. In this work we provide a system implementation on a two-armed Baxter robot that (i) allows simultaneous teaching of low- and high-level actions by demonstration, (ii) includes a user interface for action creation with condition inference and modification, and (iii) allows creating and solving previously unseen problems using a task planner for the robot to execute in real-time. We evaluate the generalisation power of the system on six benchmark tasks and show how taught actions can be easily reused for complex tasks. We further demonstrate its usability with a user study (N=21), where users completed eight tasks to teach the robot new actions that are reused with a task planner. The study demonstrates that users with any programming level and educational background can easily learn and use the system.

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arXiv2022

Seeing Thru Walls: Visualizing Mobile Robots in Augmented Reality

We present an approach for visualizing mobile robots through an Augmented Reality headset when there is no line-of-sight visibility between the robot and the human. Three elements are visualized in Augmented Reality: 1) Robot's 3D model to indicate its position, 2) An arrow emanating from the robot to indicate its planned movement direction, and 3) A 2D grid to represent the ground plane. We conduct a user study with 18 participants, in which each participant are asked to retrieve objects, one at a time, from stations at the two sides of a T-junction at the end of a hallway where a mobile robot is roaming. The results show that visualizations improved the perceived safety and efficiency of the task and led to participants being more comfortable with the robot within their personal spaces. Furthermore, visualizing the motion intent in addition to the robot model was found to be more effective than visualizing the robot model alone. The proposed system can improve the safety of automated warehouses by increasing the visibility and predictability of robots.

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arXiv2023

We are all Individuals: The Role of Robot Personality and Human Traits in Trustworthy Interaction

As robots take on roles in our society, it is important that their appearance, behaviour and personality are appropriate for the job they are given and are perceived favourably by the people with whom they interact. Here, we provide an extensive quantitative and qualitative study exploring robot personality but, importantly, with respect to individual human traits. Firstly, we show that we can accurately portray personality in a social robot, in terms of extroversion-introversion using vocal cues and linguistic features. Secondly, through garnering preferences and trust ratings for these different robot personalities, we establish that, for a Robo-Barista, an extrovert robot is preferred and trusted more than an introvert robot, regardless of the subject's own personality. Thirdly, we find that individual attitudes and predispositions towards robots do impact trust in the Robo-Baristas, and are therefore important considerations in addition to robot personality, roles and interaction context when designing any human-robot interaction study.

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