Jen Jen Chung

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Chung

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Jen Jen

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0000-0001-7828-0741

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Publications1 - 10 of 49

Informative Path Planning for Active Field Mapping under Localization Uncertainty
Popović, Marija; Vidal-Calleja, Teresa; Chung, Jen Jen; et al. (2020)
2020 IEEE International Conference on Robotics and Automation (ICRA)
Information gathering algorithms play a key role in unlocking the potential of robots for efficient data collection in a wide range of applications. However, most existing strategies neglect the fundamental problem of the robot pose uncertainty, which is an implicit requirement for creating robust, high-quality maps. To address this issue, we introduce an informative planning framework for active mapping that explicitly accounts for the pose uncertainty in both the mapping and planning tasks. Our strategy exploits a Gaussian Process (GP) model to capture a target environmental field given the uncertainty on its inputs. For planning, we formulate a new utility function that couples the localization and field mapping objectives in GP-based mapping scenarios in a principled way, without relying on manually-tuned parameters. Extensive simulations show that our approach outperforms existing strategies, reducing mean pose uncertainty and map error. We present a proof of concept in an indoor temperature mapping scenario. © 2020 IEEE.
FlowBot: Flow-based Modeling for Robot Navigation
Dugas, Daniel; Cai, Kuanqi; Andersson, Olov; et al. (2022)
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Autonomous navigation among people is a complex problem that also exhibits considerable variation depending on the type of environment and people involved. Here we consider navigation among crowds that exhibit flow-like behavior like people moving through a train station. We propose a novel pseudo-fluid model of crowd flow for such problems. These have an intuitive physical interpretation and do not require much tuning. We further formalize an observation model to infer flow properties from discrete sensor observations, including support for partial observability, and pair it with a flow-aware planner. We demonstrate the potential of the approach in simulated navigation scenarios. We achieve state of the art results on the CrowdBot navigation benchmark, and also compare favorably against a standard ROS planner on a partially observable environment, demonstrating that the flow-aware planner successfully estimates and plans around counterflows in the crowd in real time. We conclude that flow-based planning shows great promise for crowded environments that may exhibit such flow-like behavior.
An informative path planning framework for UAV-based terrain monitoring
Popović, Marija; Vidal-Calleja, Teresa; Hitz, Gregory; et al. (2020)
Autonomous Robots
Unmanned aerial vehicles represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general informative path planning framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori. The approach is capable of learning and focusing on regions of interest via adaptation to map either discrete or continuous variables on the terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a coarse grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and a proof of concept for an agricultural monitoring task.
Robust Sampling-Based Control of Mobile Manipulators for Interaction With Articulated Objects
Rizzi, Giuseppe; Chung, Jen Jen; Gawel, Abel Roman; et al. (2023)
IEEE Transactions on Robotics
In this article, we investigate and deploy sampling-based control techniques for the challenging task of the mobile manipulation of articulated objects. By their nature, manipulation tasks necessitate environment interactions, which require the handling of nondifferentiable switching contact dynamics. These dynamics represent a strong limitation for traditional gradient-based optimization methods, such as model-predictive control and differential dynamic programming, which often rely on heuristics for trajectory generation. Sampling-based techniques alleviate these constraints but do not ensure robots' stability and input/state constraints either. On the other hand, real-world applications in human environments require safety and robustness to unexpected events. For this reason, we propose a novel framework for safe robotic manipulation of movable articulated objects. The framework combines sampling-based control together with control barrier functions and passivity theory that, thanks to formal stability guarantees, enhance the safety and robustness of the method. We also provide the practical insights that enable robust deployment of stochastic control using a conventional central processing unit. We deploy the algorithm on a ten-degree-of-freedom mobile manipulator robot. Finally, we open source our generic and multithreaded implementation.
Accurate Mapping and Planning for Autonomous Racing
Andresen, Leiv; Brandemuehl, Adrian; Hönger, Alex; et al. (2020)
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
This paper presents the perception, mapping, and planning pipeline implemented on an autonomous race car. It was developed by the 2019 AMZ driverless team for the Formula Student Germany (FSG) 2019 driverless competition, where it won 1st place overall. The presented solution combines early fusion of camera and LiDAR data, a layered mapping approach, and a planning approach that uses Bayesian filtering to achieve high-speed driving on unknown race tracks while creating accurate maps. We benchmark the method against our team's previous solution, which won FSG 2018, and show improved accuracy when driving at the same speeds. Furthermore, the new pipeline makes it possible to reliably raise the maximum driving speed in unknown environments from 3~m/s to 12~m/s while still mapping with an acceptable RMSE of 0.29~m.
NeuSurfEmb: A Complete Pipeline for Dense Correspondence-based 6D Object Pose Estimation without CAD Models
Milano, Francesco; Chung, Jen Jen; Blum, Hermann; et al. (2024)
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
State-of-the-art approaches for 6D object pose estimation assume the availability of CAD models and require the user to manually set up physically-based rendering (PBR) pipelines for synthetic training data generation. Both factors limit the application of these methods in real-world scenarios. In this work, we present a pipeline that does not require CAD models and allows training a state-of-the-art pose estimator requiring only a small set of real images as input. Our method is based on a NeuS2 [1] object representation, that we learn through a semi-automated procedure based on Structure-from-Motion (SfM) and object-agnostic segmentation. We exploit the novel-view synthesis ability of NeuS2 and simple cut-and-paste augmentation to automatically generate photorealistic object renderings, which we use to train the correspondence-based SurfEmb [2] pose estimator. We evaluate our method on the LINEMOD-Occlusion dataset, extensively studying the impact of its individual components and showing competitive performance with respect to approaches based on CAD models and PBR data. We additionally demonstrate the ease of use and effectiveness of our pipeline on self-collected real-world objects, showing that our method outperforms state-of-the-art CAD-model-free approaches, with better accuracy and robustness to mild occlusions. To allow the robotics community to benefit from this system, we will publicly release it at https://www.github.com/ethz-asl/neusurfemb.
NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments
Dugas, Daniel; Nieto, Juan; Siegwart, Roland; et al. (2021)
2021 IEEE International Conference on Robotics and Automation (ICRA)
Robot navigation is a task where reinforcement learning approaches are still unable to compete with traditional path planning. State-of-the-art methods differ in small ways, and do not all provide reproducible, openly available implementations. This makes comparing methods a challenge. Recent research has shown that unsupervised learning methods can scale impressively, and be leveraged to solve difficult problems. In this work, we design ways in which unsupervised learning can be used to assist reinforcement learning for robot navigation. We train two end-to-end, and 18 unsupervised-learning-based architectures, and compare them, along with existing approaches, in unseen test cases. We demonstrate our approach working on a real life robot. Our results show that unsupervised learning methods are competitive with end-to-end methods. We also highlight the importance of various components such as input representation, predictive unsupervised learning, and latent features. We make all our models publicly available, as well as training and testing environments, and tools 1 . This release also includes OpenAI-gym-compatible environments designed to emulate the training conditions described by other papers, with as much fidelity as possible. Our hope is that this helps in bringing together the field of RL for robot navigation, and allows meaningful comparisons across state-of-the-art methods.
MultiPoint: Cross-spectral registration of thermal and optical aerial imagery
Achermann, Florian; Kolobov, Andrey; Dey, Debadeepta; et al. (2020)
Proceedings of Machine Learning Research ~ Proceedings of the 2020 Conference on Robot Learning
While optical cameras are ubiquitous in robotics, some robots can sense the world in several sections of the electromagnetic spectrum simultaneously, which can extend their capabilities in fundamental ways. For instance, many fixed-wing UAVs carry both optical and thermal imaging cameras, potentially allowing them to detect temperature difference-induced atmospheric updrafts, map their locations, and adjust their flight path accordingly to increase their time aloft. A key step for unlocking the potential offered by multi-spectral data is generating consistent, multi-spectral maps of the environment. In this work, we introduce MultiPoint, a novel data-driven method for generating interest points and associated descriptors for registering optical and thermal image pairs without knowledge of the relative camera viewpoints. Existing pixel-based alignment methods are accurate but too slow to work in near-real time, while feature-based methods such as SuperPoint are fast but produce poor-quality cross-spectral matches due to interest point instability in thermal images. MultiPoint capitalizes on the strengths of both approaches. An offline mutual information-based procedure is used to align cross-spectral image pairs from a training set, which are then processed by our generalized multi-spectral homographic adaptation stage to generate highly repeatable interest points that are invariant across viewpoint changes in both spectra. These are used to train a MultiPoint deep neural network by exposing this model to both same-spectrum and cross-spectral image pairs. This model is then deployed for fast and accurate online interest point detection. We show that MultiPoint outperforms existing techniques for feature-based image alignment using a dataset of real-world thermal-optical imagery captured by a UAV during flights in different conditions and release this dataset, the first of its kind.
Neuroevolution of a Modular Memory-Augmented Neural Network for Deep Memory Problems
Khadka, Shauharda; Chung, Jen Jen; Tumer, Kagan (2019)
Evolutionary Computation
IAN: Multi-behavior navigation planning for robots in real, crowded environments
Dugas, Daniel; Nieto, Juan; Siegwart, Roland; et al. (2020)
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
State-of-the-art approaches for robot navigation among humans are typically restricted to planar movement actions. This work addresses the question of whether it can be beneficial to use interaction actions, such as saying, touching, and gesturing, for the sake of allowing robots to navigate in unstructured, crowded environments. To do so, we first identify challenging scenarios to traditional motion planning methods. Based on the hypothesis that the variation in modality for these scenarios calls for significantly different planning policies, we design specific navigation behaviors as interaction planners for actuated, mobile robots. We further propose a high level planning algorithm for multi-behavior navigation, named Interaction Actions for Navigation (IAN). Through both real-world and simulated experiments, we validate the selected behaviors and the high-level planning algorithm, and discuss the impact of our obtained results on our stated assumptions. © 2020 IEEE

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