Visual odometry (VO) in underwater environments presents significant challenges due to poor visibility and dynamic scene changes, which render conventional (in-air) VO solutions unsuitable for underwater applications. We propose an underwater robust monocular visual odometry (UR-MVO) pipeline tailored for underwater scenarios with feature extraction and matching based on SuperPoint and SuperGlue models, respectively. We enhance the robustness of the feature extractor through field-specific fine-tuning of the SuperPoint model using few-shot unsupervised learning. This tuning was done on real images of underwater scenes in order to enhance its performance in the harsh underwater image conditions. Moreover, we integrate semantic segmentation trained on underwater images into our pipeline to eliminate unreliable features belonging to dynamic objects and background. We evaluated the proposed solution on the Aqualoc dataset, demonstrating higher localization accuracy compared to other SOTA direct and feature-based monocular VO methods like DSO and SVO and also obtained very competitive results compared to more resource-intensive monocular VSLAM approaches with loop closure process like LDSO, UVS, and ORB-SLAM. The results show a high potential for our approach for further applications in underwater exploration and mapping using affordable sensory setups. We publish the code for the benefit of the community https://github.com/be2rlab/UR-MVO

Legged robots require low-inertia limbs capable of carrying a high payload. The design of such limbs poses challenges in integrating optimal kinematic structures with practical design considerations. In the search for optimal design parameters, advantages of optimization algorithms can be applied. This paper introduces an open-source framework for optimizing topology and parameters of closed linkage mechanisms, addressing the need for task-specific robotic limbs. Closed-loop structures are motivated by two main purposes: (1) to decrease robotic limb inertia by relocation of actuators close to the robot’s body and (2) to redistribute efforts among actuators. The framework leverages joint-based spatial graph representations, kinetostatic criteria, and multi-objective genetic algorithms to optimize mechanism topology and parameters. Focusing on kinetostatic criteria such as Jacobian metrics and inertia properties, the framework swiftly explores the design space to balance trade-offs in robot linkages. We demonstrate the framework pipeline for the task of optimizing planar robotic legs with 2 degrees of freedom. Project github page: https://licaibeerlab.github.io/jmoves.github.io/

Open Semantic Mapping (OSM) is a key technology in robotic perception, combining semantic segmentation and SLAM techniques. This paper introduces a dynamically configurable and highly automated LLM/LVLM-powered pipeline for evaluating OSM solutions called OSMa-Bench (Open Semantic Mapping Benchmark). The study focuses on evaluating state-of-the-art semantic mapping algorithms under varying indoor lighting conditions, a critical challenge in indoor environments. We introduce a novel dataset with simulated RGB-D sequences and ground truth 3D reconstructions, facilitating the rigorous analysis of mapping performance across different lighting conditions. Through experiments on leading models such as ConceptGraphs [1], BBQ [2], and OpenScene [3], we evaluate the semantic fidelity of object recognition and segmentation. Additionally, we introduce a scene graph evaluation method to analyze the ability of models to interpret semantic structure. The results provide insights into the robustness of these models, forming future research directions for developing resilient and adaptable robotic systems. Our code is available at https://be2rlab.github.io/OSMa-Bench/.

Visual localization methods often present a trade-off between the high efficiency of specialized approaches, such as scene coordinate regression, and the need for rich, versatile scene representations for broader robotics tasks. To bridge this gap, we explore the use of 3D Gaussian Splatting (3DGS), which enables a unified, photorealistic encoding of 3D geometry and appearance. We propose GSplatLoc, a self-contained framework that tightly integrates structure-based keypoint matching with rendering-based pose refinement. Our two-stage procedure first distills robust descriptors from the lightweight XFeat extractor into the 3DGS model, enabling coarse pose estimation via 2D-3D correspondences without external dependencies. In the second stage, the initial pose is refined by minimizing a photometric warp loss, which leverages the fast, differentiable rendering of 3DGS. Benchmarking on widely used indoor and outdoor datasets demonstrates state-of-the-art performance among neural rendering-based localization methods and highlights the framework’s robustness in challenging dynamic scenes. Project page: https://gsplatloc.github.io

This paper describes a novel parametric optimization procedure for three flexure cross hinges (TFCH) integrated into multi-link leg mechanisms with closed-loop kinematics. Despite advantages such as compliance, no need for joint lubrication, light weight and cost-efficiency, such shaftless mechanisms have not been widely used, especially in the field of dynamic locomotion, also because their design is challenging and barely studied. Using a morphological computation approach, we have optimized the TFCH geometry to achieve the desired joint stiffness using frequency analysis, ensuring safe and stable hopping under external perturbations. We combined rigid body dynamics with lumped stiffness model and finite element modeling using the SPACAR toolbox to simulate various designs within our optimization pipeline. To illustrate the efficiency of the resulting designs, we built a prototype and conducted a series of full-scale experiments with ramp jumps whose trajectories were recorded by a motion capture system. The experiments showed that TFCH can be effectively integrated into leg mechanisms, providing benefits such as impact robustness, energy recuperation, and the ability to work in extreme conditions.

The design process of robotic systems is a complex journey that involves multiple phases. Throughout this process, the aim is to tackle various criteria simultaneously, even though they often contradict each other. The ultimate goal is to uncover the optimal solution that resolves these conflicting factors. Within this paper we propose a design methodology to generate linkage mechanisms for robots with morphological computation. We use a graph grammar and a heuristic search algorithm to create robot mechanism graphs that are converted into simulation models for testing the design output. To verify the design methodology we have applied it to a relatively simple quasi-static problem of object grasping. Designing a fully actuated gripper may seem simple, but we found a way to automatically design an underactuated tendon-driven gripper that can grasp a wide range of objects. This is possible because of its structure, not because of sophisticated planning or learning. To test the applicability of the proposed method in real engineering practice, we used it to create physical prototypes. Simulation results together with results of testing of physical prototypes are given at the end of the paper. The framework is open source and the link to GitHub is given in the paper.

The main advantages of legged robots over wheeled ones are their abilities to traverse on uneven terrain due to the use of intermittent contacts and an ability to shift the center of mass relative to the contact location. A robot's leg design can be implemented by using an open-chain mechanism actuated with high-density torque actuators though this solution needs a vast energy budget. An alternative way to design a leg mechanism is the application of morphological computation principle. According to the principle, most of the desired robot's behavior can be delegated to the mechanics with minimum control effort needed to excite, stabilize or augment it. Within this paper, we have proposed a method to synthesize a leg for hopping robots. Due to optimization of mechanical structure, geometric parameters, mass distribution, and elasticity allocation, our method allows getting an energy-efficient robot with minimal control system complexity, which is accomplished via series elastic allocation and active variable length link. Based on this approach, we have designed a hopping robot with two low performance actuators that can achieve hopping, running, and, in the case of a biped or quadruped robot, walking motion. The paper describes a synthesized leg linkage and overviews prototype design, control strategy, and test results of a physical prototype.

Designing robots is a multiphase process aimed at solving a multi-criteria optimization problem to find the best possible detailed design. Generative design (GD) aims to accelerate the design process compared to manual design, since GD allows exploring and exploiting the vast design space more efficiently. In the field of robotics, however, relevant research focuses mostly on the generation of fully-actuated open chain kinematics, which is trivial in mechanical engineering perspective. Within this paper, we address the problem of generative design of closed-chain linkage mechanisms. A GD algorithm has to be able to generate meaningful mechanisms which satisfy conditions of existence. We propose an optimization-driven algorithm for generation of planar closed-chain linkages to follow a predefined trajectory. The algorithm creates an unlimited range of physically reproducible design alternatives that can be further tested in simulation. These tests could be done in order to find solutions that satisfy extra criteria, e.g., desired dynamic behavior or low energy consumption. The proposed algorithm is called “respawn” since it builds a new linkage after the ancestor has been tested in a virtual environment in pursuit for the optimal solution. To show that the algorithm is general enough, we show a set of generated linkages that can be used for a wide class of robots.

This paper presents RVWO, a system designed to provide robust localization and mapping for wheeled mobile robots in challenging scenarios. The proposed approach leverages a probabilistic framework that incorporates semantic prior information about landmarks and visual re-projection error to create a landmark reliability model, which acts as an adaptive kernel for the visual residuals in optimization. Additionally, we fuse visual residuals with wheel odometry measurements, taking advantage of the planar motion assumption. The RVWO system is designed to be robust against wrong data association due to moving objects, poor visual texture, bad illumination, and wheel slippage. Evaluation results demonstrate that the proposed system shows competitive results in dynamic environments and outperforms existing approaches on both public benchmarks and our custom hardware setup. We also provide the code as an open-source contribution to the robotics community22https://github.com/be2rlab/rvwo.

3D mapping is crucial for many applications in robotics and related industries. To build dense high-quality point clouds accurate depth estimation or completion is needed. This paper presents the development of a metric-semantic mapping pipeline based on Deep Neural Networks (DNN) which assures geometrical consistency with enhancements for chal-lenging environments with transparent and reflecting objects like glass walls, doors, and mirrors. The suggested approach uses camera ego-motion alongside its sparse visual features to avoid the scale ambiguity issue caused by monocular depth affine-invariant estimations and to able to restore metric consistent depth information. Visual-inertial odometry data is used for camera pose graph optimization with no need to use RGB-D cameras. The proposed pipeline incorporates semantic segmentation and robust filtering to refine point clouds by removing outliers associated with mirrors and glass surfaces. Latency-aware performance and quality evaluation of 3D scene reconstruction were carried out on both a specially prepared dataset that reflects office-like scenes with multiple transparent objects and a public ScanNet dataset. The quantitative and qualitative results show that the proposed solution outperforms other state-of-art DNN-based models and algorithms as well as RGB-D cameras in terms of metric depth geometric consistency, 3D reconstruction accuracy, and the ability to preserve mesh quality in challenging scenarios with transparent and reflective surfaces.

Anthropomorphic robotic grippers are required for robots, prostheses, and orthosis to enable manipulation of a priori unknown and variable-shape objects. It has to meet a wide range of sometimes contradictory requirements in terms of adaptivity, dexterity, high payload to weight ratio, robustness, aesthetics, compactness, lightweight, etc. Within this paper, we utilize the morphological computation approach to introduce design for anthropomorphic re-configurable underactuated grippers. The key to fingers' adaptivity is embedded passive variable length links and elastic elements at input joints. Based on this concept, we designed a palm-size five-finger gripper, where 14 DoFs, including thumb, are controlled by just 4 motors, such that it can perform both precision pinch and encompassing power grasps of various objects. The paper describes synthesized linkages for digits, hand design overview, control strategy, and test results of a physical prototype.

We address the problem of motion control for safe physical interaction, and in particular finding new ways for impedance controller parameters’ adaptation to ensure better safety with minimum possible lose in robot performance. We propose an exteroception-based dynamically updated safety metric that takes into account current robot state and inertia as well as external objects’ mass, shape, material properties, velocity, sensor confidence and existing sampling rates. We also present how this metric can be applied to design a finite state machine of the multi-stage controller, which allows us to prioritize either safety of performance by setting different energy and power constraints with smooth transition in between free motion and interaction modes.

We present an optimization-based structural-parametric synthesis method for reconfigurable closed-chain underactuated linkages for robotic systems that physically interact with the environment with an emphasis on adaptive grasping. The key idea is to implement morphological computation concepts to keep both necessary trajectory-specific holonomic constraints and mechanism adaptivity using variable length links (VLL), while we evolve from a fully actuated to an underactuated system satisfying imposed design requirements. It allows to minimize the number of actuators, weight, and cost but keep high payload and endurance that are not reachable by tendon-driven designs. Despite the method is general enough, for clarity, we demonstrate its use on a number of finger mechanisms for adaptive grippers.

One way to create a quadruped galloping robot from scratch is to design a brick-shaped body and utilize relatively simple open-chain leg mechanisms controlled with relatively complex control algorithms. Alternatively, we can look at how nature solved the same task designing fast mammals such as cheetah, and by means of morphological computation, we can design a complex mechanical system that has much of the desired behavior within inherent dynamics and only a little control effort is needed to stabilize or augment the motion.In this paper, we have analyzed a real cheetah motion using video tracking and looked for a way to match the dynamic model parameters of the real cheetah with a galloping robot with an elastic spine. We believe the elastic spine is the essential feature for a fast-running energy-efficient galloping robot. Within this paper, we are focused on the flying stage when the elastic spine affects the motion of the robot’s front and rear bodies. We have found how to optimize mass distribution and elasticity in the spine in order to get the cheetah-like galloping motion of a quadruped robot.

The biomimetic approach in robotics is promising: nature has found many good solutions through millions of years of evolution. However, creating a design that enables fast and energy-efficient locomotion remains a major challenge. This paper focuses on the development of a full leg mechanism for a fast and energy-efficient 4-legged robot inspired by a cheetah morphology. In particular, we analyze how the allocation of flexible elements and their stiffness affects the cost of transport and peak power characteristics for vertical jumps and a galloping motion. The study includes the femur and full leg mechanism's locomotory behavior simulation, capturing its interaction with the ground.

This paper describes the design of a gripper device for handling heavy steel tubes with variable physical properties such as diameter, mass and length. This grasping device represents an alternative solution to expensive and sophisticated anthropomorphic grippers. This research is focused on hardware design and concept issues. The designed device for assistive robotic applications can represent a hardware component of an industrial cyber-physical system. Design of such tools is extremely relevant for modern industry and science. Research work, mechanical design and implementation steps are described in the paper in details. Testing procedures and corresponding illustrative results are also reported.

This paper is a result of the university-industry collaboration between ITMO University and Thermex. A subject of this cooperative investigation is robotic automation of industrial operations in the sense of organizing a cyber-physical system and reducing the human factor from the production. Tasks of a technological process considered in the paper are welding, transporting and polishing. They are simulated on the basis of the laboratory of the Department of Control Systems and Informatics. Robots used in this study are Mitsubishi MELFA RV-3SDB, KUKA youBot and Kawasaki FS06N. Three control levels are highlighted in the paper. There is the strategic level at the top. Its main part is a central control software based on MATLAB used to set up a network and coordinate robots between the operations. Control of a separate robot corresponds to the tactic level. Finally, the local level is assigned to control of actuators and get data from sensors.

Our work addresses the problem of planning an optimal pitching trajectory for compliant serial manipulators. We propose a generic framework for finding the longest possible pitch given angle, velocity, and torque constraints. Within this framework we can decompose an original infinite-dimensional task to finite number of similar steps. As a result, the implemented procedure gives an optimal release configuration, temporal control torque profiles satisfying dynamic constraints, and optimal stiffness coefficients for compliant joints. Computational complexity of the nonlinear optimization task was reduced applying a novel motion parametrization method. The proposed method was illustrated for a particular example and experimentally verified with the KUKA LWR4+ arm.