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Motion Planner Augmented Reinforcement learning for robot Manipulation in Obstructed Environments 4

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Motion Planner Augmented Reinforcement Learning for Robot Ma...

4th Conference on robot learning (CoRL)

作者： Yamada, Jun Lee, Youngwoon Salhotra, Gautam Pertsch, Karl Pflueger, Max Sukhatme, Gaurav S. Lim, Joseph J. Englert, Peter Cognit Learning Vision & Robot Lab Manchester Lancs England Univ Southern Calif Robot Embedded Syst Lab Dept Comp Sci Los Angeles CA USA

Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in environments with many obstacles that complicate exploration. In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment. To combine the benefits of both approaches, we propose motion planner augmented RL (MoPA-RL) which augments the action space of an RL agent with the long-horizon planning capabilities of motion planners. Based on the magnitude of the action, our approach smoothly transitions between directly executing the action and invoking a motion planner. We evaluate our approach on various simulated manipulation tasks and compare it to alternative action spaces in terms of learning efficiency and safety. The experiments demonstrate that MoPA-RL increases learning efficiency, leads to a faster exploration, and results in safer policies that avoid collisions with the environment. Videos and code are available at https://***/mopa-rl

关键词： Reinforcement learning Motion Planning robot Manipulation

Target-Aware Implicit Mapping for Agricultural Crop Inspection

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Target-Aware Implicit Mapping for Agricultural Crop Inspecti...

IEEE International Conference on robotics and Automation (ICRA)

作者： Kelly, Shane Riccardi, Alessandro Marks, Elias Magistri, Federico Guadagnino, Tiziano Chli, Margarita Stachniss, Cyrill Swiss Fed Inst Technol Vision Robot Lab Zurich Switzerland Univ Bonn Bonn Germany Univ Oxford Dept Engn Sci Oxford England Lamarr Inst Machine Learning & Artificial Intelli Munich Germany

ISBN: (纸本)9798350323658

Crop inspection is a critical part of modern agricultural practices that helps farmers assess the current status of a field and then make crop management decisions. Current crop inspection methods are labour-intensive tasks, which makes them rather slow and expensive to apply. In this paper, we exploit recent advancements in implicit mapping to tackle the challenging context of agricultural environments to create dense maps of crop rows with high enough fidelity to be useful for automated crop inspection. Specifically, we map strawberry and sweet pepper crop rows using RGB images captured by a wheeled mobile field robot inside a greenhouse and then use this data to build 3D maps to document the development of plants and fruits. Our Target-Aware Implicit Mapping system (TAIM) uses a SLAM-based pose initialization strategy for robust pose convergence, an efficient information-guided training sample selection framework for faster loss reduction, and focuses on exploiting training samples for fruit regions of the scene, which are critical for crop inspection tasks, to create more accurate maps in less time.

关键词： Crops

Automated Planning Domain Inference for Task and Motion Planning

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arXiv 2024年

作者： Huang, Jinbang Tao, Allen Marco, Rozilyn Bogdanovic, Miroslav Kelly, Jonathan Shkurti, Florian Space and Terrestrial Autonomous Systems Lab Canada Robot Vision and Learning Lab University of Toronto Robotics Institute Canada

Task and motion planning (TAMP) frameworks address long and complex planning problems by integrating high-level task planners with low-level motion planners. However, existing TAMP methods rely heavily on the manual design of planning domains that specify the preconditions and postconditions of all high-level actions. This paper proposes a method to automate planning domain inference from a handful of test-time trajectory demonstrations, reducing the reliance on human design. Our approach incorporates a deep learning-based estimator that predicts the appropriate components of a domain for a new task and a search algorithm that refines this prediction, reducing the size and ensuring the utility of the inferred domain. Our method is able to generate new domains from minimal demonstrations at test time, enabling robots to handle complex tasks more efficiently. We demonstrate that our approach outperforms behavior cloning baselines, which directly imitate planner behavior, in terms of planning performance and generalization across a variety of tasks. Additionally, our method reduces computational costs and data amount requirements at test time for inferring new planning domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

vision-Based Goal-Conditioned policies for underwater navigation in the presence of obstacles

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arXiv 2020年

作者： Manderson, Travis Gamboa, Juan Camilo Wapnick, Stefan Tremblay, Jean-François Shkurti, Florian Meger, Dave Dudek, Gregory Mobile Robotics Laboratory School of Computer Science McGill University Montreal Canada Robot Vision & Learning Lab Department of Computer Science University of Toronto Canada

We present Nav2Goal, a data-efficient and end-to-end learning method for goal-conditioned visual navigation. Our technique is used to train a navigation policy that enables a robot to navigate close to sparse geographic waypoints provided by a user without any prior map, all while avoiding obstacles and choosing paths that cover user-informed regions of interest. Our approach is based on recent advances in conditional imitation learning. General-purpose safe and informative actions are demonstrated by a human expert. The learned policy is subsequently extended to be goal-conditioned by training with hindsight relabelling, guided by the robot’s relative localization system, which requires no additional manual annotation. We deployed our method on an underwater vehicle in the open ocean to collect scientifically relevant data of coral reefs, which allowed our robot to operate safely and autonomously, even at very close proximity to the coral. Our field deployments have demonstrated over a kilometer of autonomous visual navigation, where the robot reaches on the order of 40 waypoints, while collecting scientifically relevant data. This is done while travelling within 0.5 m altitude from sensitive corals and exhibiting significant learned agility to overcome turbulent ocean conditions and to actively avoid collisions. Copyright © 2020, The Authors. All rights reserved.

关键词： Navigation

Revealing Preference in Popular Music Through Familiarity and Brain Response

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IEEE SENSORS JOURNAL 2021年第13期21卷 14931-14940页

作者： Sangnark, Soravitt Autthasan, Phairot Ponglertnapakorn, Puntawat Chalekarn, Phudit Sudhawiyangkul, Thapanun Trakulruangroj, Manatsanan Songsermsawad, Sarita Assabumrungrat, Rawin Amplod, Supalak Ounjai, Kajornvut Wilaiprasitporn, Theerawit Vidyasirimedhi Inst Sci & Technol Sch Informat Sci & Technol Bio Inspired Robot & Neural Engn Lab Rayong 21210 Thailand Vidyasirimedhi Inst Sci & Technol Sch Informat Sci & Technol Vision & Learning Lab Rayong 21210 Thailand Thammasat Univ Sirindhorn Int Inst Technol Fac Elect Engn Bangkok 10200 Thailand Vidyasirimedhi Inst ofScience & Technol Sch Energy Sci & Engn Rayong 21210 Thailand Tohoku Univ Sch Engn Sendai Miyagi 9808577 Japan King Mongkuts Univ Technol Thonburi Biol Engn Program Fac Engn Bangkok 10140 Thailand King Mongkuts Univ Technol Thonburi Neurosci Ctr Res & Innovat Learning Inst Bangkok 10140 Thailand

Music preference was reported as a factor, which could elicit innermost music emotion, entailing accurate ground-truth data and music therapy efficiency. This study executes statistical analysis to investigate the distinction of music preference through familiarity scores, response times (response rates), and brain response (EEG). Twenty participants did self-assessment after listening to two types of popular music's chorus section: music without lyrics (Melody) and music with lyrics (Song). We then conduct a music preference classification using a support vector machine, random forest, and k-nearest neighbors with the familiarity scores, the response rates, and EEG as the feature vectors. The statistical analysis and F1-score of EEG are congruent, which says that the brain's right side outperformed its left side in classification performance. Finally, these behavioral and brain studies support that preference, familiarity, and response rates can contribute to the music emotion experiment's design to understand music, emotion, and listener. Not only to the music industry, the biomedical and healthcare industry can also exploit this experiment to collect data from patients to improve the efficiency of healing by music.

关键词： Music Electroencephalography Software Sensors Protocols Statistical analysis Time factors Music preference EEG emotion recognition affective computing music listening

Policy-Guided Lazy Search with Feedback for Task and Motion Planning

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Policy-Guided Lazy Search with Feedback for Task and Motion ...

IEEE International Conference on robotics and Automation (ICRA)

作者： Mohamed Khodeir Atharv Sonwane Ruthrash Hari Florian Shkurti Robot Vision and Learning Lab University of Toronto Robotics Institute Dept. of Computer Science University of BITS Pilani Dept. of Mathematical and Computational Sciences University of Toronto

PDDLStream solvers have recently emerged as viable solutions for Task and Motion Planning (TAMP) problems, extending PDDL to problems with continuous action spaces. Prior work has shown how PDDLStream problems can be reduced to a sequence of PDDL planning problems, which can then be solved using off-the-shelf planners. However, this approach can suffer from long runtimes. In this paper we propose LAZY, a solver for PDDLStream problems that maintains a single integrated search over action skeletons, which gets progressively more geometrically informed, as samples of possible motions are lazily drawn during motion planning. We explore how learned models of goal-directed policies and current motion sampling data can be incorporated in LAZY to adaptively guide the task planner. We show that this leads to significant speed-ups in the search for a feasible solution evaluated over unseen test environments of varying numbers of objects, goals, and initial conditions. We evaluate our TAMP approach by comparing to existing solvers for PDDLStream problems on a range of simulated 7DoF rearrangement/manipulation problems. Code can be found at https://***/learning-based-tamp.

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Policy-Guided Lazy Search with Feedback for Task and Motion Planning

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arXiv 2022年

作者： Khodeir, Mohamed Sonwane, Atharv Hari, Ruthrash Shkurti, Florian Robot Vision and Learning Lab University of Toronto Robotics Institute Canada Dept. of Computer Science University of BITS Pilani India Dept. of Mathematical and Computational Sciences University of Toronto Canada

关键词： Motion planning

FusionPortableV2: A Unified Multi-Sensor Dataset for Generalized SLAM Across Diverse Platforms and Scalable Environments

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arXiv 2024年

作者： Wei, Hexiang Jiao, Jianhao Hu, Xiangcheng Yu, Jingwen Xie, Xupeng Wu, Jin Zhu, Yilong Liu, Yuxuan Wang, Lujia Liu, Ming Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Robot Perception and Learning Lab Department of Computer Science University College London United Kingdom Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology China Guangzhou China

Simultaneous Localization and Mapping (SLAM) technology has been widely applied in various robotic scenarios, from rescue operations to autonomous driving. However, the generalization of SLAM algorithms remains a significant challenge, as current datasets often lack scalability in terms of platforms and environments. To address this limitation, we present FusionPortableV2, a multi-sensor SLAM dataset featuring sensor diversity, varied motion patterns, and a wide range of environmental scenarios. Our dataset comprises 27 sequences, spanning over 2.5 hours and collected from four distinct platforms: a handheld suite, a legged robot, a unmanned ground vehicle (UGV), and a vehicle. These sequences cover diverse settings, including buildings, campuses, and urban areas, with a total length of 38.7km. Additionally, the dataset includes ground-truth (GT) trajectories and RGB point cloud maps covering approximately 0.3km2. To validate the utility of our dataset in advancing SLAM research, we assess several state-of-the-art (SOTA) SLAM algorithms. Furthermore, we demonstrate the dataset's broad application beyond traditional SLAM tasks by investigating its potential for monocular depth estimation. The completae dataset, including sensor data, GT, and calibration details, is accessible at https://***/dataset/fusionportable v2. Copyright © 2024, The Authors. All rights reserved.

关键词： SLAM robotics