ICRA 2024 论文列表

Achieving reliable autonomous navigation during nighttime remains a substantial obstacle in the field of robotics. Although systems utilizing Light Detection and Ranging (Li-DAR) and Radio Detection and Ranging (RADAR) enable environmental perception regardless of lighting conditions, they face significant challenges in environments with a high density of agents due to their dependence on active emissions. Cameras operating in the visible spectrum represent a quasi-passive alternative, yet they see a substantial drop in efficiency in low-light conditions, consequently hindering both scene perception and path planning. Here, we introduce a novel end-to-end navigation system, the "Thermal Voyager", which leverages infrared thermal vision to achieve true passive perception in autonomous entities. The system utilizes our architecture, TrajNet to interpret thermal visual inputs to produce desired trajectories and employs a model predictive control strategy to determine the optimal steering angles needed to actualize those trajectories. We train our TrajNet on a comprehensive video dataset incorporating visible and thermal footage along-side Controller Area Network (CAN) frames. We demonstrate that nighttime navigation facilitated by Long-Wave Infrared (LWIR) thermal cameras can rival the performance of daytime navigation systems using RGB cameras. Our work paves the way for scene perception and trajectory prediction empowered entirely by passive thermal sensing technology, heralding a new era where autonomous navigation is both feasible and reliable irrespective of the time of day. We make our code and thermal trajectory dataset public1.

In this paper, we investigate how a customer-facing service robot can support decision making in shopping interactions. In this role, a robot needs sometimes to reject a customer’s choice. Thus, we investigate different rejection strategies with the goal of changing customer behavior. The implemented strategies have been developed based on an ethnographic study on assisted shopping and tested in a lab experiment with 31 participants. The experiment showed significant differences in trust ratings and decision-making depending on the employed strategy.

In this work, we introduce LazyBoE, a multi-query method for kinodynamic motion planning with forward propagation. This algorithm allows for the simultaneous exploration of a robot’s state and control spaces, thereby enabling a wider suite of dynamic tasks in real-world applications. Our contributions are three-fold: i) a method for discretizing the state and control spaces to amortize planning times across multiple queries; ii) lazy approaches to collision checking and propagation of control sequences that decrease the cost of physics-based simulation; and iii) LazyBoE, a robust kinodynamic planner that leverages these two contributions to produce dynamically-feasible trajectories. The proposed framework not only reduces planning time but also increases success rate in comparison to previous approaches.

How easy is it to sneak up on a robot? We examine whether we can detect people using only the incidental sounds they produce as they move, even when they try to be quiet. To do so, we first collect a robotic dataset of high-quality 4-channel audio paired with 360° RGB data of people moving in different indoor settings. Using this dataset, we train models to predict if there is a moving person nearby and then their location using only audio. We implement our method on a robot, allowing it to track a single person moving quietly using only passive audio sensing. For demonstration videos, see our project page.

A risk aware UAS path planning methodology is proposed using monetary value as the sole cost metric. A third party ground risk model is used to generate a non-uniform costmap for a modified A* heuristic search. The Value of a Prevented Fatality provides a basis to convert fatality risk to monetary value terms as a Human Value at Risk (HVaR) measure. Additional operating and UAS Capital Value at Risk (CVaR) costs are modelled to provide a holistic monetary cost model for path cost minimisation. A number of future cost variants are investigated based upon prior work for a realistic urban-rural mix logistics case study in Southern England. Results show increasingly risk averse paths with decreasing future UAS operating costs.

This work introduces the New Dexterity modular, dexterous, anthropomorphic, open-source, bimanual manipulation platform (OpenBMP) that is designed for research and rapid experimentation in robot grasping, dexterous manipulation, and bimanual manipulation. The platform combines adaptive and hybrid actuation systems with lockable joints, facilitating transitions between the execution of delicate and forceful tasks. Antagonistic tendon-driven elbows and inline actuator transmissions reduce the system’s inertial mass while enhancing energy efficiency and overall performance. Leveraging 3D printing and carbon fiber reinforced manufacturing of core parts, the platform is easy to replicate and highly modular. This paper presents the details of the design, the actuation principles, and the experimental validation of the efficiency of the platform with the execution of complex teleoperation and telemanipulation tasks. The designs, the electronics, and the code are open-sourced to allow replication by others.

We present a synchronized multisensory dataset for accurate and robust indoor localization: the Lund University Vision, Radio, and Audio (LuViRA) Dataset. The dataset includes color images, corresponding depth maps, inertial measurement unit (IMU) readings, channel response between a 5G massive multiple-input and multiple-output (MIMO) testbed and user equipment, audio recorded by 12 microphones, and accurate six degrees of freedom (6DOF) pose ground truth of 0.5 mm. We synchronize these sensors to ensure that all data is recorded simultaneously. A camera, speaker, and transmit antenna are placed on top of a slowly moving service robot, and 89 trajectories are recorded. Each trajectory includes 20 to 50 seconds of recorded sensor data and ground truth labels. Data from different sensors can be used separately or jointly to perform localization tasks, and data from the motion capture (mocap) system is used to verify the results obtained by the localization algorithms. The main aim of this dataset is to enable research on sensor fusion with the most commonly used sensors for localization tasks. Moreover, the full dataset or some parts of it can also be used for other research areas such as channel estimation, image classification, etc. Our dataset is available at: https://github.com/ilaydayaman/LuViRA_Dataset

Hand motion tracking is essential in many fields, e.g., immersive virtual reality, teleoperation of robotic hand, and hand rehabilitation of stroke patient, as human hand plays a crucial role in our daily life. The highly under-actuated hand exoskeleton, which can track the 6-DoF motions of each fingertip via a highly under-actuated kinematic chain, exhibits many benefits in wearability and portability over other solutions. However, due to the non-anthropomorphic linkage, this hand exoskeleton also encounters difficulties in measuring human-finger’s joint angles. While the joint-space is important in many scenarios, such as teleoperating a robotic hand with anthropomorphic kinematics but with different size to human. Here we proposed a new method to reconstruct the human finger joints by using a highly under-actuated hand exoskeleton. Our key contribution is the arc-fitting algorithm, which is able to calibrate the misalignment between the exoskeleton’s and the human-finger’s base frames and estimate the length of human’s phalanxes, by using the fingertip’s circular motions. With knowing the aforementioned informations, the joint angles can be reconstructed in high precision based on the inverse kinematics models of human fingers. Furthermore, our proposed method is compared with a baseline method, in which the joint angles obtained by a motion capture system are served as ground-truth. The results demonstrate that our proposed method exhibits excellent performance in reconstructing finger’s joint configurations.

Most Simultaneous localisation and mapping (SLAM) systems have traditionally assumed a static world, which does not align with real-world scenarios. To enable robots to safely navigate and plan in dynamic environments, it is essential to employ representations capable of handling moving objects. Dynamic SLAM is an emerging field in SLAM research as it improves the overall system accuracy while providing additional estimation of object motions. State-of-the-art literature informs two main formulations for Dynamic SLAM, representing dynamic object points in either the world or object coordinate frame. While expressing object points in their local reference frame may seem intuitive, it does not necessarily lead to the most accurate and robust solutions. This paper conducts and presents a thorough analysis of various Dynamic SLAM formulations, identifying the best approach to address the problem. To this end, we introduce a front-end agnostic framework using GTSAM [1] that can be used to evaluate various Dynamic SLAM formulations.1

Robotic exoskeletons can enhance human strength and aid people with physical disabilities. However, designing them to ensure safety and optimal performance presents significant challenges. Developing exoskeletons should incorporate specific optimization algorithms to find the best design. This study investigates the potential of Evolutionary Computation (EC) methods in robotic design optimization, with an underactuated hand exoskeleton (U-HEx) used as a case study. We propose improving the performance and usability of the U-HEx design, which was initially optimized using a naive brute-force approach, by integrating EC techniques such as Genetic Algorithm and Big Bang-Big Crunch Algorithm. Comparative analysis revealed that EC methods consistently yield more precise and optimal solutions than brute force in a significantly shorter time. This allowed us to improve the optimization by increasing the number of variables in the design, which was impossible with naive methods. The results show significant improvements in terms of the torque magnitude the device transfers to the user, enhancing its efficiency. These findings underline the importance of performing proper optimization while designing exoskeletons, as well as providing a significant improvement to this specific robotic design.

Advancing robotic grasping and manipulation requires the ability to test algorithms and/or train learning models on large numbers of grasps. Towards the goal of more advanced grasping, we present the Grasp Reset Mechanism (GRM), a fully automated apparatus for conducting large-scale grasping trials. The GRM automates the process of resetting a grasping environment, repeatably placing an object in a fixed location and controllable 1-D orientation. It also collects data and swaps between multiple objects enabling robust dataset collection with no human intervention. We also present a standardized state machine interface for control, which allows for integration of most manipulators with minimal effort. In addition to the physical design and corresponding software, we include a dataset of 1,020 grasps. The grasps were created with a Kinova Gen3 robot arm and Robotiq 2F-85 Adaptive Gripper to enable training of learning models and to demonstrate the capabilities of the GRM. The dataset includes ranges of grasps conducted across four objects and a variety of orientations. Manipulator states, object pose, video, and grasp success data are provided for every trial.

This paper studies robotic manipulation of deformable, one-dimensional objects (DOOs) like ropes or cables, which has important potential applications in manufacturing, agriculture, and surgery. In such environments, the task may involve threading through or avoiding becoming tangled with other objects. Grasping with multiple grippers can create closed loops between the robot and DOO, and if an obstacle lies within this loop, it may be impossible to reach the goal. However, prior work has only considered the topology of the DOO in isolation, ignoring the arms that are manipulating it. Searching over possible grasps to accomplish the task without considering such topological information is very inefficient, as many grasps will not lead to progress on the task due to topological constraints. Therefore, we propose the {{\mathcal{G}}_L} - {\text{signature}}{{\mathcal{G}}_L} - {\text{signature}} which categorizes the topology of these grasp loops and show how it can be used to guide planning. We perform experiments in simulation on two DOO manipulation tasks to show that using the {{\mathcal{G}}_L} - {\text{signature}}{{\mathcal{G}}_L} - {\text{signature}} is faster and more successful than methods that rely on local geometry or additional finite-horizon planning. Finally, we demonstrate using the {{\mathcal{G}}_L} - {\text{signature}}{{\mathcal{G}}_L} - {\text{signature}} in a real-world dual-arm cable manipulation task.

The potential for deploying autonomous systems can be significantly increased by improving the perception and interpretation of the environment. However, the development of deep learning-based techniques for autonomous systems in unstructured outdoor environments poses challenges due to limited data availability for training and testing. To address this gap, we present the German Outdoor and Offroad Dataset (GOOSE), a comprehensive dataset specifically designed for unstructured outdoor environments. The GOOSE dataset incorporates 10000 labeled pairs of images and point clouds, which are utilized to train a range of state-of-the-art segmentation models on both image and point cloud data. We open source the dataset, along with an ontology for unstructured terrain, as well as dataset standards and guidelines. This initiative aims to establish a common framework, enabling the seamless inclusion of existing datasets and a fast way to enhance the perception capabilities of various robots operating in unstructured environments. This framework also makes it possible to query data for specific weather conditions or sensor setups from a database in future. The dataset, pre-trained models for offroad perception, and additional documentation can be found at https://goose-dataset.de/.

Nowadays, robotic arms are ubiquitously employed for object manipulation across a spectrum of applications, spanning from production lines to warehouses, and encompassing both stationary and mobile robotic systems. Among the most prevalent end-effectors, used for the majority of these applications, are suction cups. The rudimentary act of grasping an object and relocating it, devoid of a cognizant awareness of the forces stemming from the object’s motion and grip, can result in suboptimal and inefficient robot movements. In more dire circumstances, such negligent handling may precipitate detachment of the object from the end-effector, potentially incurring damage to either the object or the arm.In this paper, we build upon the advanced sensing and attaching capabilities of our suction cup MIGHTY, and introduce GEM-C, a novel Gravity, External forces and Motion Compensation controller, that constantly adapts the orientation of the suction cup so as to enhance the quality of attachment. Throughout all examined scenarios and experiments, our approach remarkably improved the robot’s performance by providing the optimal end-effector pose while also reducing the stress on the motors and the overall power consumption. The derived results, clearly demonstrate the MIGHTY and GEM-C schema’s potential for a wide range of demanding robotic manipulation tasks.

We introduce a novel Fractal Hand robotic gripper. The hand has only 1 actuator, but (2n+1 −1) joints, where a design parameter n defines the depth of the fingers’ tree structures. The hand is synergistic in its operation (because its joint movements are coupled through the hand’s interaction with the grasped object), but it is not anthropomorphic. The basic finger and hand geometry, governing kinematics, and quasi-statics mechanics of a rigid version of the hand are developed. These analyses remarkably show that under mild constraints, the grasped object is compliantly stable at an equilibrium grasp configuration. Thus, the Fractal Hand adapts to a very wide range of planar objects with a single design. Grasp planning is thus simplified. A companion paper [33] introduces a design methodology for this new class of robot hands, and multiple prototypes.

This paper and its companion propose a new fractal robotic gripper, drawing inspiration from the centuryold Fractal Vise. The unusual synergistic properties allow it to passively conform to diverse objects using only one actuator. Designed to be easily integrated with prevailing parallel jaw grippers, it alleviates the complexities tied to perception and grasp planning, especially when dealing with unpredictable object poses and geometries. We build on the foundational principles of the Fractal Vise to a broader class of gripping mechanisms and address the limitations that had led to its obscurity. Two Fractal Fingers, coupled with a closing actuator, can form an adaptive and synergistic Fractal Hand. We articulate a design methodology for low-cost, easy-to-fabricate, large workspace, and compliant Fractal Fingers. The companion paper delves into the kinematics and grasping properties of a specific class of Fractal Fingers and Hands.

Food handling is a challenging task for robotic grippers, as it requires to manipulate highly deformable and fragile items, that can be easily damaged. Moreover, ingredients for the preparation of the different dishes are usually stored in small containers that are often not easily accessible. This paper introduces an innovative soft-rigid, tendon-driven gripper: the Double-Scoop Gripper (DSG). Its two-fingered design exploits a specialized structure to cope with constrained spaces (e.g., containers in narrow shelves). The DSG can delicately grasp objects of various shapes by employing two scoop-shaped fingertips that can form a single plate when fingers are flexed. Data obtained from an on-board camera are used to detect the food item features and plan the grasping strategy that better exploits the possible environmental constraints regulating the opening of the two fingers and the approaching direction of the gripper. DSG capabilities are verified with experiments conducted using real food ingredients within a pick-and-place setup to evaluate both the grasping and the releasing capability of the gripper. Obtained results are promising and suggest that this approach could be particularly advantageous in the context of automated food serving.

This paper is about 3D pose estimation on LiDAR scans with extremely minimal storage requirements to enable scalable mapping and localisation. We achieve this by clustering all points of segmented scans into semantic objects and representing them only with their respective centroid and semantic class. In this way, each LiDAR scan is reduced to a compact collection of four-number vectors. This abstracts away important structural information from the scenes, which is crucial for traditional registration approaches. To mitigate this, we introduce an object-matching network based on self- and cross-correlation that captures geometric and semantic relationships between entities. The respective matches allow us to recover the relative transformation between scans through weighted Singular Value Decomposition (SVD) and RANdom SAmple Consensus (RANSAC). We demonstrate that such representation is sufficient for metric localisation by registering point clouds taken under different viewpoints on the KITTI dataset, and at different periods of time localising between KITTI and KITTI-360. We achieve accurate metric estimates comparable with state-of-the-art methods with almost half the representation size, specifically 1.33 kB on average.

Automatic landing of tethered unmanned aerial vehicles (UAVs) is an important issue. Typically, UAVs rely on location sensors such as global navigation satellite system (GNSS) and external cameras to obtain location data. However, harsh environments such as denial GNSS or strong winds make it difficult for UAVs to approach the landing area, and common solutions cannot be used for automatic landing. A tethered lifting-wing multicopter has a structure and static stability similar to a kite. Inspired by kites, this paper proposes a new landing method for tethered lifting-wing multicopters, which can be used without location or velocity sensors. During the landing phase, the tethered lifting-wing multicopter only needs to keep the rotor thrust to actively straighten the tethered cable and a constant attitude similar to that of a kite to keep position stability and increase damping. Meanwhile, the winch only needs to recover the cable at a constant speed until the tethered lifting-wing multicopter returns to its base. Real flight experiments demonstrate the feasibility and practicability of this method.