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Graph-Structured Super-Resolution for Geometry- Generalized Tomographic Tactile Sensing: Application to Humanoid Faces

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IEEE TRANSACTIONS ON robotics 2025年 41卷 558-572页

作者： Park, Hyunkyu Kim, Woojong Jeon, Sangha Na, Youngjin Kim, Jung Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon 34141 South Korea Sookmyung Womens Univ Dept Mech Syst Engn Seoul 04310 South Korea

Electrical impedance tomographic (EIT) tactile sensing holds great promise for whole-body coverage of contact-rich robotic systems, offering extensive flexibility in sensor geometry. However, low spatial resolution restricts its practical use, despite the existing deep-learning-based reconstruction methods. This study introduces EIT-GNN, a graph-structured data-driven EIT reconstruction framework that achieves super-resolution in large-area tactile perception on unbounded form factors of robots. EIT-GNN represents the arbitrary sensor shape into mesh connections, then employs a twofold architecture of transformer encoder and graph convolutional neural network to best manage such the geometrical prior knowledge, resulting in the accurate, generalized, and parameter-efficient reconstruction procedure. As a proof-of-concept, we demonstrate its application using large-area face-shaped sensor hardware, which represents one of the most complex geometries in human/humanoid anatomy. An extensive set of experiments, including simulation study, ablation analysis, single-touch indentation test, and latent feature analysis, confirm its superiority over alternative models. The beneficial features of the approach are demonstrated through its application in active tactile-servo control of humanoid head motion, paving the new way for integrating tactile sensors with intricate designs into robotic systems.

关键词： Geometry Sensors Tactile sensors Image reconstruction Electrical impedance tomography Robots Shape Hardware Faces Calibration deep learning in robotics and automation force and tactile sensing sensor-based control tomographic reconstruction

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Task-Driven Detection of Distribution Shifts With Statistical Guarantees for Robot learning

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IEEE TRANSACTIONS ON robotics 2025年 41卷 926-945页

作者： Farid, Alec Veer, Sushant Pachisia, Divyanshu Majumdar, Anirudha Princeton Univ Princeton NJ 08540 USA Zoox Foster CA 94404 USA NVIDIA Autonomous Vehicle Res Grp Santa Clara CA 95051 USA Skydio San Mateo CA 94402 USA Princeton Univ Intelligent Robot Mot Lab Princeton NJ USA

Our goal is to perform out-of-distribution (OOD) detection, i.e., to detect when a robot is operating in environments drawn from a different distribution than the ones used to train the robot. We leverage probably approximately correct-Bayes theory to train a policy with a guaranteed bound on performance on the training distribution. Our idea for OOD detection relies on the following intuition: violation of the performance bound on test environments provides evidence that the robot is operating OOD. We formalize this via statistical techniques based on $p$-values and concentration inequalities. The approach provides guaranteed confidence bounds on OOD detection including bounds on both the false-positive and false-negative rates of the detector and is task-driven and only sensitive to changes that impact the robot's performance. We demonstrate our approach in simulation and hardware for a grasping task using objects with unfamiliar shapes or poses and a drone performing vision-based obstacle avoidance in environments with wind disturbances and varied obstacle densities. Our examples demonstrate that we can perform task-driven OOD detection within just a handful of trials.

关键词： deep learning in robotics and automation failure detection and recovery formal methods in robotics and automation PAC-Bayes deep learning in robotics and automation failure detection and recovery formal methods in robotics and automation PAC-Bayes

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COLA: COarse-LAbel Multisource LiDAR Semantic Segmentation for Autonomous Driving

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IEEE TRANSACTIONS ON robotics 2025年 41卷 1742-1754页

作者： Sanchez, Jules Deschaud, Jean-Emmanuel Goulette, Francois PSL Univ Ctr Robot Mines Paris PSL F-75006 Paris France ENSTA Paris Inst Polytech Paris U2IS F-91120 Palaiseau France

LiDAR semantic segmentation (LSS) for autonomous driving has been a growing field of interest in recent years. Datasets and methods have appeared and expanded very quickly, but methods have not been updated to exploit this new data availability and rely on the same classical datasets. Different ways of performing LSS training and inference can be divided into several subfields, which include the following: domain generalization, source-to-source segmentation, and pretraining. In this work, we aim to improve results in all of these subfields with the novel approach of multisource training. Multisource training relies on the availability of various datasets at training time. To overcome the common obstacles in multisource training, we introduce the coarse labels and call the newly created multisource dataset COLA. We propose three applications of this new dataset that display systematic improvement over single-source strategies: COLA-DG for domain generalization (+10% ), COLA-S2S for source-to-source segmentation (+5.3% ), and COLA-PT for pretraining (+12% ). We demonstrate that multisource approaches bring systematic improvement over single-source approaches.

关键词： Training Laser radar Semantic segmentation Three-dimensional displays Robot sensing systems Autonomous vehicles Annotations Point cloud compression Robustness deep learning 3-D computer vision computer vision for transportation deep learning in robotics and automation semantic scene understanding

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High-Fidelity Integrated Aerial Platform Simulation for Control, Perception, and learning

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IEEE TRANSACTIONS ON automation SCIENCE AND ENGINEERING 2025年 22卷 13662-13683页

作者： Du, Jianrui Wang, Kaidi Fan, Yingjun Lai, Ganghua Yu, Yushu Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China

This paper presents a simulator framework tailored Integrated Aerial Platforms (IAPs) using multiple quadrotors. Our framework prioritizes photo and contact fidelity, achieved through a modular design that balances rendering and dynamics computation. Key features include: i) support for diverse IAP configurations;ii) a customizable physics engine for realistic motion and contact simulation for aerial manipulation;and iii) Unreal Engine 5 for lifelike rendering, with sensor designs for visual-inertial SLAM positioning simulation. We showcase our framework's versatility through a range of scenarios, including trajectory tracking for both fully and under-actuated IAPs, peg-in-hole and direct wrench control tasks under external wrench influence, tightly-coupled SLAM positioning with physical constraints, and air docking task training and testing using offline-to-online reinforcement learning. Furthermore, we validate our simulator framework's fidelity by comparing results with real flight data for trajectory tracking and direct wrench control tasks. Our simulator framework promises to be valuable for developing and testing integrated aerial platform systems for aerial manipulation.

关键词： Robots Engines Physics Drones Simultaneous localization and mapping Training Aircraft Rendering (computer graphics) Propellers Vehicle dynamics Simulation and animation aerial systems: perception and autonomy SLAM deep learning in robotics and automation

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Single-View and Multi-View Depth Fusion

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IEEE robotics AND automation LETTERS 2017年第4期2卷 1994-2001页

作者： Facil, Jose M. Concha, Alejo Montesano, Luis Civera, Javier Univ Zaragoza I3A Zaragoza 50018 Spain Bit&Brain Technol Zaragoza 50008 Spain

Dense and accurate 3-D mapping from a monocular sequence is a key technology for several applications and still an open research area. This letter leverages recent results on single-view convolutional network (CNN)-based depth estimation and fuses them with multi-view depth estimation. Both approaches present complementary strengths. Multi-view depth is highly accurate but only in high-texture areas and high-parallax cases. Single-view depth captures the local structure of midlevel regions, including texture-less areas, but the estimated depth lacks global coherence. The single and multi-view fusion we propose is challenging in several aspects. First, both depths are related by a deformation that depends on the image content. Second, the selection of multi-view points of high accuracy might be difficult for low-parallax configurations. We present contributions for both problems. Our results in the public datasets of NYUv2 and TUM shows that our algorithm outperforms the individual single and multi-view approaches. A video showing the key aspects of mapping in our single and multi-view depth proposal is available at https://***/ipc5HukTb4k.

关键词： deep learning in robotics and automation mapping SLAM

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A Multimodal Classifier Generative Adversarial Network for Carry and Place Tasks From Ambiguous Language Instructions

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IEEE robotics AND automation LETTERS 2018年第4期3卷 3113-3120页

作者： Magassouba, Aly Sugiura, Komei Kawai, Hisashi Natl Inst Informat & Commun Technol Kyoto 6190289 Japan

This letter focuses on a multimodal language understanding method for carry-and-place tasks with domestic service robots. We address the case of ambiguous instructions, that is, when the target area is not specified. For instance "put away the milk and cereal" is a natural instruction where there is ambiguity regarding the target area, considering environments in daily life. Conventionally, this instruction can he disambiguated from a dialogue system, but at the cost of time and cumbersome interaction. Instead, we propose a multimodal approach, in which the instructions are disambiguated using the robot's state and environment context. We develop the Multi-Modal Classifier Generative Adversarial Network (MMC-GAN) to predict the likelihood of different target areas considering the robot's physical limitation and the target clutter. Our approach, MMC-GAN, significantly improves accuracy compared with baseline methods that use instructions only or simple deep neural networks.

关键词： deep learning in robotics and automation domestic robots robot audition

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learning a Generalizable Trajectory Sampling Distribution for Model Predictive Control

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IEEE TRANSACTIONS ON robotics 2024年 40卷 2111-2127页

作者： Power, Thomas Berenson, Dmitry Univ Michigan Robot Dept Ann Arbor MI 48109 USA

We propose a sample-based model predictive control (MPC) method for collision-free navigation that uses a normalizing flow as a sampling distribution, conditioned on the start, goal, environment, and cost parameters. This representation allows us to learn a distribution that accounts for both the dynamics of the robot and complex obstacle geometries. We propose a way to incorporate this sampling distribution into two sampling-based MPC methods, MPPI, and iCEM. However, when deploying these methods, the robot may encounter an out-of-distribution (OOD) environment. To generalize our method to OOD environments, we also present an approach that performs projection on the representation of the environment. This projection changes the environment representation to be more in-distribution while also optimizing trajectory quality in the true environment. Our simulation results on a 2-D double-integrator, a 12-DoF quadrotor and a seven-DoF kinematic manipulator suggest that using a learned sampling distribution with projection outperforms MPC baselines on both in-distribution and OOD environments over different cost functions, including OOD environments generated from real-world data.

关键词： deep generative models deep learning in robotics and automation motion and path planning nonholonomic motion planning

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Robot Motion Planning in Learned Latent Spaces

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IEEE robotics AND automation LETTERS 2019年第3期4卷 2407-2414页

作者： Ichter, Brian Pavone, Marco Google Brain Mountain View CA 94043 USA Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94305 USA

This letter presents latent sampling-based motion planning (L-SBMP), a methodology toward computing motion plans for complex robotic systems by learning a plannable latent representation. Recent works in control of robotic systems have effectively leveraged local, low-dimensional embeddings of high-dimensional dynamics. In this letter, we combine these recent advances with techniques from sampling-based motion planning (SBMP) in order to design a methodology capable of planning for high-dimensional robotic systems beyond the reach of traditional approaches (e.g., humanoids, or even systems where planning occurs in the visual space). Specifically, the learned latent space is constructed through an autoencoding network, a dynamics network, and a collision checking network, which mirror the three main algorithmic primitives of SBMP, namely state sampling, local steering, and collision checking. Notably, these networks can be trained through only raw data of the system's states and actions along with a supervising collision checker. Building upon these networks, an RRT-based algorithm is used to plan motions directly in the latent space-we refer to this exploration algorithm as learned latent RRT. This algorithm globally explores the latent space and is capable of generalizing to new environments. The overall methodology is demonstrated on two planning problems, namely a visual planning problem, whereby planning happens in the visual (pixel) space, and a humanoid robot planning problem.

关键词： Motion and path planning deep learning in robotics and automation

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MFuseNet: Robust Depth Estimation With Learned Multiscopic Fusion

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IEEE robotics AND automation LETTERS 2020年第2期5卷 3113-3120页

作者： Yuan, Weihao Fan, Rui Wang, Michael Yu Chen, Qifeng Hong Kong Univ Sci & Technol Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Dept Mech & Aerosp Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Dept Comp Sci & Engn Hong Kong Peoples R China

We design a multiscopic vision system that utilizes a low-cost monocular RGB camera to acquire accurate depth estimation. Unlike multi-view stereo with images captured at unconstrained camera poses, the proposed system controls the motion of a camera to capture a sequence of images in horizontally or vertically aligned positions with the same parallax. In this system, we propose a new heuristic method and a robust learning-based method to fuse multiple cost volumes between the reference image and its surrounding images. To obtain training data, we build a synthetic dataset with multiscopic images. The experiments on the real-world Middlebury dataset and real robot demonstration show that our multiscopic vision system outperforms traditional two-frame stereo matching methods in depth estimation. Our code and dataset are available at https://***/view/multiscopic.

关键词： Visual learning deep learning in robotics and automation computer vision for automation depth estimation multiscopic vision

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deep learning of Neuromuscular and Visuomotor Control of a Biomimetic Simulated Humanoid

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IEEE robotics AND automation LETTERS 2020年第3期5卷 3952-3959页

作者： Nakada, Masaki Zhou, Tao Chen, Honglin Lakshmipathy, Arjun Terzopoulos, Demetri Univ Calif Los Angeles Comp Sci Dept Los Angeles CA 90095 USA

We present a biomimetic framework for human neuromuscular and visuomotor control that promises to be of value to researchers developing humanoid robots. Our framework features a biomechanically simulated human musculoskeletal model, actuated by numerous skeletal muscles, with realistic eyes driven by extraocular and intraocular muscles, whose optic organs refract light, and whose retinas have many nonuniformly distributed photoreceptors. The humanoid's visuomotor control system comprises 24 trained deep neural networks (DNNs)-10 DNNs in its vision subsystem and 14 DNNs in its motor subsystem-plus an additional 4 trained shallow neural networks (SNNs) that control the irises and lenses of the eyes. Of the motor DNNs, a pair control the extraocular muscles, 6 per eye, responsible for eye movements, 2 control the 216 neck muscles of the cervicocephalic biomechanical complex, producing natural head movements, 2 control the 443 core muscles of the torso, and 2 control each limb;i.e., the 29 muscles of each arm and 39 muscles of each leg. Directly from the foveated retinal photoreceptor responses, a pair of foveation DNNs drive eye, head, and torso movements, while 8 limb vision DNNs extract the visual information needed to direct arm and leg actions. By synthesizing its own training data, our humanoid automatically learns efficient, online, active visuomotor control of its eyes, head, torso, and limbs in order to perform nontrivial tasks involving the foveation and visual pursuit of moving target objects coupled with visually-guided limb-reaching actions to intercept them. We also demonstrate that it can balance itself in an upright stance, take steps, and perform certain simulated sports activities.

关键词： Biomimetics simulation and animation deep learning in robotics and automation visual learning humanoid robots

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