Robotics (cs.RO)

Abstract: The Finite Element Method (FEM) is a powerful modeling tool for predicting the behavior of soft robots. However, its use for control can be difficult for non-specialists of numerical computation: it requires an optimization of the computation to make it real-time. In this paper, we propose a learning-based approach to obtain a compact but sufficiently rich mechanical representation. Our choice is based on nonlinear compliance data in the actuator/effector space provided by a condensation of the FEM model. We demonstrate that this compact model can be learned with a reasonable amount of data and, at the same time, be very efficient in terms of modeling, since we can deduce the direct and inverse kinematics of the robot. We also show how to couple some models learned individually in particular on an example of a gripper composed of two soft fingers. Other results are shown by comparing the inverse model derived from the full FEM model and the one from the compact learned version. This work opens new perspectives, namely for the embedded control of soft robots, but also for their design. These perspectives are also discussed in the paper.

Abstract: Unmanned aerial vehicles (UAVs) rely on optical sensors such as cameras and lidar for autonomous operation. However, such optical sensors are error-prone in bad lighting, inclement weather conditions including fog and smoke, and around textureless or transparent surfaces. In this paper, we ask: is it possible to fly UAVs without relying on optical sensors, i.e., can UAVs fly without seeing? We present BatMobility, a lightweight mmWave radar-only perception system for UAVs that eliminates the need for optical sensors. BatMobility enables two core functionalities for UAVs -- radio flow estimation (a novel FMCW radar-based alternative for optical flow based on surface-parallel doppler shift) and radar-based collision avoidance. We build BatMobility using commodity sensors and deploy it as a real-time system on a small off-the-shelf quadcopter running an unmodified flight controller. Our evaluation shows that BatMobility achieves comparable or better performance than commercial-grade optical sensors across a wide range of scenarios.

Abstract: This paper contributes a novel and modularized learning-based method for aerial robots navigating cluttered environments containing hard-to-perceive thin obstacles without assuming access to a map or the full pose estimation of the robot. The proposed solution builds upon a semantically-enhanced Variational Autoencoder that is trained with both real-world and simulated depth images to compress the input data, while preserving semantically-labeled thin obstacles and handling invalid pixels in the depth sensor's output. This compressed representation, in addition to the robot's partial state involving its linear/angular velocities and its attitude are then utilized to train an uncertainty-aware 3D Collision Prediction Network in simulation to predict collision scores for candidate action sequences in a predefined motion primitives library. A set of simulation and experimental studies in cluttered environments with various sizes and types of obstacles, including multiple hard-to-perceive thin objects, were conducted to evaluate the performance of the proposed method and compare against an end-to-end trained baseline. The results demonstrate the benefits of the proposed semantically-enhanced deep collision prediction for learning-based autonomous navigation.

Abstract: Efficient cargo packing and transport unit stacking play a vital role in enhancing logistics efficiency and reducing costs in the field of logistics. This article focuses on the challenging problem of loading transport units onto pallets, which belongs to the class of NP-hard problems. We propose a novel method for solving the pallet loading problem using a branch and bound algorithm, where there is a loading order of transport units. The derived algorithm considers only a heuristically favourable subset of possible positions of the transport units, which has a positive effect on computability. Furthermore, it is ensured that the pallet configuration meets real-world constraints, such as the stability of the position of transport units under the influence of transport inertial forces and gravity.

Abstract: The paper introduces CycleIK, a neuro-robotic approach that wraps two novel neuro-inspired methods for the inverse kinematics (IK) task, a Generative Adversarial Network (GAN), and a Multi-Layer Perceptron architecture. These methods can be used in a standalone fashion, but we also show how embedding these into a hybrid neuro-genetic IK pipeline allows for further optimization via sequential least-squares programming (SLSQP) or a genetic algorithm (GA). The models are trained and tested on dense datasets that were collected from random robot configurations of the new Neuro-Inspired COLlaborator (NICOL), a semi-humanoid robot with two redundant 8-DoF manipulators. We utilize the weighted multi-objective function from the state-of-the-art BioIK method to support the training process and our hybrid neuro-genetic architecture. We show that the neural models can compete with state-of-the-art IK approaches, which allows for deployment directly to robotic hardware. Additionally, it is shown that the incorporation of the genetic algorithm improves the precision while simultaneously reducing the overall runtime.

Abstract: Athletic robots demand a whole-body actuation system design that utilizes motors up to the boundaries of their performance. However, creating such robots poses challenges of integrating design principles and reasoning of practical design choices. This paper presents a design framework that guides designers to find optimal design choices to create an actuation system that can rapidly generate torques and velocities required to achieve a given set of tasks, by minimizing inertia and leveraging cooperation between actuators. The framework serves as an interactive tool for designers who are in charge of providing design rules and candidate components such as motors, reduction mechanism, and coupling mechanisms between actuators and joints. A binary integer linear optimization explores design combinations to find optimal components that can achieve a set of tasks. The framework is demonstrated with 200 optimal design studies of a biped with 5-degree-of-freedom (DoF) legs, focusing on the effect of achieving multiple tasks (walking, lifting), constraining the mass budget of all motors in the system and the use of coupling mechanisms. The result provides a comprehensive view of how design choices and rules affect reflected inertia, copper loss of motors, and force capability of optimal actuation systems.

Abstract: In this paper, we present a benchmarking study of vision-based grasp synthesis algorithms, each with distinct approaches, and provide a comparative analysis of their performance under different experimental conditions. In particular, we compare two machine-learning-based and two analytical algorithms to determine their strengths and weaknesses in different scenarios. In addition, we provide an open-source benchmarking tool developed from state-of-the-art benchmarking procedures and protocols to systematically evaluate different grasp synthesis algorithms. Our findings offer insights into the performance of the evaluated algorithms, which can aid in selecting the most appropriate algorithm for different scenarios.

Abstract: In this study, we introduce an online monocular lane mapping approach that solely relies on a single camera and odometry for generating spline-based maps. Our proposed technique models the lane association process as an assignment issue utilizing a bipartite graph, and assigns weights to the edges by incorporating Chamfer distance, pose uncertainty, and lateral sequence consistency. Furthermore, we meticulously design control point initialization, spline parameterization, and optimization to progressively create, expand, and refine splines. In contrast to prior research that assessed performance using self-constructed datasets, our experiments are conducted on the openly accessible OpenLane dataset. The experimental outcomes reveal that our suggested approach enhances lane association and odometry precision, as well as overall lane map quality. We have open-sourced our code1 for this project.

Abstract: Robotic pruning of dormant grapevines is an area of active research in order to promote vine balance and grape quality, but so far robotic efforts have largely focused on planar, simplified vines not representative of commercial vineyards. This paper aims to advance the robotic perception capabilities necessary for pruning in denser and more complex vine structures by extending plant skeletonization techniques. The proposed pipeline generates skeletal grapevine models that have lower reprojection error and higher connectivity than baseline algorithms. We also show how 3D and skeletal information enables prediction accuracy of pruning weight for dense vines surpassing prior work, where pruning weight is an important vine metric influencing pruning site selection.

Abstract: We propose a new frontier concept called the Gaussian Process Frontier (GP-Frontier) that can be used to locally navigate a robot towards a goal without building a map. The GP-Frontier is built on the uncertainty assessment of an efficient variant of sparse Gaussian Process. Based only on local ranging sensing measurement, the GP-Frontier can be used for navigation in both known and unknown environments. The proposed method is validated through intensive evaluations, and the results show that the GP-Frontier can navigate the robot in a safe and persistent way, i.e., the robot moves in the most open space (thus reducing the risk of collision) without relying on a map or a path planner.