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6th Conference on robot learning (CoRL)

作者： Vosylius, Vitalis Johns, Edward Imperial Coll London Robot Learning Lab London England

robot learning provides a number of ways to teach robots simple skills, such as grasping. However, these skills are usually trained in open, clutter-free environments, and therefore would likely cause undesirable collisions in more complex, cluttered environments. In this work, we introduce an affordance model based on a graph representation of an environment, which is optimised during deployment to find suitable robot configurations to start a skill from, such that the skill can be executed without any collisions. We demonstrate that our method can generalise a priori acquired skills to previously unseen cluttered and constrained environments, in simulation and in the real world, for both a grasping and a placing task. Videos are available on our project webpage at https://***/where-to-start

关键词： robot manipulation planning obstacle avoidance

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Demonstrate Once, Imitate Immediately (DOME): learning Visual Servoing for One-Shot Imitation learning

Demonstrate Once, Imitate Immediately (DOME): Learning Visua...

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Valassakis, Eugene Papagiannis, Georgios Di Palo, Norman Johns, Edward Imperial Coll London Robot Learning Lab London England

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

We present DOME, a novel method for one-shot imitation learning, where a task can be learned from just a single demonstration and then be deployed immediately, without any further data collection or training. DOME does not require prior task or object knowledge, and can perform the task in novel object configurations and with distractors. At its core, DOME uses an image-conditioned object segmentation network followed by a learned visual servoing network, to move the robot's end-effector to the same relative pose to the object as during the demonstration, after which the task can be completed by replaying the demonstration's end-effector velocities. We show that DOME achieves near 100% success rate on 7 real-world everyday tasks, and we perform several studies to thoroughly understand each individual component of DOME. Videos and supplementary material are available at: https://***/dome.

关键词： Training learning systems Object segmentation Data collection Visual servoing End effectors Task analysis

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learning Mechanical Impulse Response for Understanding Surface Characteristics 18th

Learning Mechanical Impulse Response for Understanding Surfa...

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18th International Conference on Intelligent Autonomous Systems (IAS)

作者： Lee, Joohyun Ryu, Semin Kim, Seung-Chan Sungkyunkwan Univ Dept Sport Interact Sci Machine Learning Syst Lab Suwon South Korea Hallym Univ Intelligent Robot Lab Chunchon South Korea

This paper proposes an intelligent system that identifies the nature of a surface by knocking on it and interpreting the resultant vibrations to comprehend the complexity.

ISBN: (纸本)9783031449802;9783031449819

This paper proposes an intelligent system that identifies the nature of a surface by knocking on it and interpreting the resultant vibrations to comprehend the complexity.

关键词： Machine learning Vibration Vibrational communication

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Large-Scale Autonomous Flight With Real-Time Semantic SLAM Under Dense Forest Canopy

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IEEE robotICS AND AUTOMATION LETTERS 2022年第2期7卷 5512-5519页

作者： Liu, Xu Nardari, Guilherme, V Ojeda, Fernando Cladera Tao, Yuezhan Zhou, Alex Donnelly, Thomas Qu, Chao Chen, Steven W. Romero, Roseli A. F. Taylor, Camillo J. Kumar, Vijay Univ Penn GRASP Lab Philadelphia PA 19104 USA Univ Sao Paulo Robot Learning Lab Sao Paulo SP Brazil

Semantic maps represent the environment using a set of semantically meaningful objects. This representation is storage-efficient, less ambiguous, and more informative, thus facilitating large-scale autonomy and the acquisition of actionable information in highly unstructured, GPS-denied environments. In this letter, we propose an integrated system that can perform large-scale autonomous flights and real-time semantic mapping in challenging under-canopy environments. We detect and model tree trunks and ground planes from LiDAR data, which are associated across scans and used to constrain robot poses as well as tree trunk models. The autonomous navigation module utilizes a multi-level planning and mapping framework and computes dynamically feasible trajectories that lead the UAV to build a semantic map of the user-defined region of interest in a computationally and storage efficient manner. A drift-compensation mechanism is designed to minimize the odometry drift using semantic SLAM outputs in real time, while maintaining planner optimality and controller stability. This leads the UAV to execute its mission accurately and safely at scale.

关键词： Aerial systems: perception and autonomy field robotics robotics and automation in agriculture and forestry SLAM

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Differentiable Physics Simulation of Dynamics-Augmented Neural Objects

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IEEE robotICS AND AUTOMATION LETTERS 2023年第5期8卷 2780-2787页

作者： Le Cleac'h, Simon Yu, Hong-Xing Guo, Michelle Howell, Taylor Gao, Ruohan Wu, Jiajun Manchester, Zachary Schwager, Mac Stanford Univ Multirobot Syst Lab Stanford CA 94305 USA Carnegie Mellon Univ Robot Explorat Lab Pittsburgh PA 15213 USA Stanford Univ Stanford Vis & Learning Lab Stanford CA 94305 USA

We present a differentiable pipeline for simulating the motion of objects that represent their geometry as a continuous density field parameterized as a deep network. This includes Neural Radiance Fields (NeRFs), and other related models. From the density field, we estimate the dynamical properties of the object, including its mass, center of mass, and inertia matrix. We then introduce a differentiable contact model based on the density field for computing normal and friction forces resulting from collisions. This allows a robot to autonomously build object models that are visually and dynamically accurate from still images and videos of objects in motion. The resulting Dynamics-Augmented Neural Objects (DANOs) are simulated with an existing differentiable simulation engine, Dojo, interacting with other standard simulation objects, such as spheres, planes, and robots specified as URDFs. A robot can use this simulation to optimize grasps and manipulation trajectories of neural objects, or to improve the neural object models through gradient-based real-to-simulation transfer. We demonstrate the pipeline to learn the coefficient of friction of a bar of soap from a real video of the soap sliding on a table. We also learn the coefficient of friction and mass of a Stanford bunny through interactions with a Panda robot arm from synthetic data, and we optimize trajectories in simulation for the Panda arm to push the bunny to a goal location.

关键词： Simulation and animation contact modeling neural object representations differentiable contact simulation real-to-sim transfer

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Sensorimotor learning With Stability Guarantees via Autonomous Neural Dynamic Policies

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IEEE robotICS AND AUTOMATION LETTERS 2025年第2期10卷 1760-1767页

作者： Totsila, Dionis Chatzilygeroudis, Konstantinos Modugno, Valerio Hadjivelichkov, Denis Kanoulas, Dimitrios Univ Lorraine Inria CNRS Loria F-54000 Nancy France Univ Patras Dept Math Computat Intelligence Lab CILab Patras 26504 Greece Univ Patras Dept Elect & Comp Engn Lab Automat & Robot LAR Patras 26504 Greece UCL Dept Comp Sci Robot Percept & Learning Lab RPL Lab London WC1E 6BT England Archimedes Athena RC Maroussi 15125 Greece

State-of-the-art sensorimotor learning algorithms, either in the context of reinforcement learning or imitation learning, offer policies that can often produce unstable behaviors, damaging the robot and/or the environment. Moreover, it is very difficult to interpret the optimized controller and analyze its behavior and/or performance. Traditional robot learning, on the contrary, relies on dynamical system-based policies that can be analyzed for stability/safety. Such policies, however, are neither flexible nor generic and usually work only with proprioceptive sensor states. In this work, we bridge the gap between generic neural network policies and dynamical system-based policies, and we introduce Autonomous Neural Dynamic Policies (ANDPs) that: (a) are based on autonomous dynamical systems, (b) always produce asymptotically stable behaviors, and (c) are more flexible than traditional stable dynamical system-based policies. ANDPs are fully differentiable, flexible generic-policies that accept any observation input, while ensuring asymptotic stability. Through several experiments, we explore the flexibility and capacity of ANDPs in several imitation learning tasks including experiments with image observations. The results show that ANDPs combine the benefits of both neural network-based and dynamical system-based methods.

关键词： robots Artificial neural networks Dynamical systems Stability analysis Asymptotic stability Aerospace electronics robot learning Imitation learning Trajectory Heuristic algorithms learning from demonstration machine learning for robot control sensorimotor learning

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Hierarchical Task Model Predictive Control for Sequential Mobile Manipulation Tasks

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IEEE robotICS AND AUTOMATION LETTERS 2024年第2期9卷 1270-1277页

作者： Du, Xintong Zhou, Siqi Schoellig, Angela P. Tech Univ Munich Learning Syst & Robot Lab D-80333 Munich Germany Univ Toronto Inst Aerosp Studies Toronto ON M5S 1A1 Canada Univ Toronto Munich Inst Robot & Machine Intelligence MIRMI Vector Inst Artificial Intelligence Robot Inst Toronto ON M5S 1A4 Canada

Mobile manipulators are envisioned to serve more complex roles in people's everyday lives. With recent breakthroughs in large language models, task planners have become better at translating human verbal instructions into a sequence of tasks. However, there is still a need for a decision-making algorithm that can seamlessly interface with the high-level task planner to carry out the sequence of tasks efficiently. In this work, building on the idea of nonlinear lexicographic optimization, we propose a novel Hierarchical-Task Model Predictive Control framework that is able to complete sequential tasks with improved performance and reactivity by effectively leveraging the robot's redundancy. Compared to the state-of-the-art task-prioritized inverse kinematic control method, our approach has improved hierarchical trajectory tracking performance by 42% on average when facing task changes, robot singularity, and reference variations. Compared to a typical single-task architecture, our proposed hierarchical task control architecture enables the robot to traverse a shorter path in task space and achieves an execution time 2.3 times faster when executing a sequence of delivery tasks. We demonstrated the results with real-world experiments on a 9 degrees of freedom mobile manipulator.

关键词： Task analysis robots robot kinematics Redundancy Optimization Trajectory Planning Mobile manipulation redundant robots whole-body motion planning and control

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EnQuery: Ensemble Policies for Diverse Query-Generation in Preference Alignment of robot Navigation 33

EnQuery: Ensemble Policies for Diverse Query-Generation in P...

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33rd IEEE International Conference on robot and Human Interactive Communication (IEEE RO-MAN) - Embracing Human-Centered HRI

作者： de Heuvel, Jorge Seiler, Florian Bennewitz, Maren Univ Bonn Humanoid Robots Lab Bonn Germany Univ Bonn Ctr Robot Bonn Germany Lamarr Inst Machine Learning & Artificial Intelli Bonn Germany

ISBN: (纸本)9798350375039;9798350375022

To align mobile robot navigation policies with user preferences through reinforcement learning from human feedback (RLHF), reliable and behavior-diverse user queries are required. However, deterministic policies fail to generate a variety of navigation trajectory suggestions for a given navigation task. In this paper, we introduce EnQuery, a query generation approach using an ensemble of policies that achieve behavioral diversity through a regularization term. For a given navigation task, EnQuery produces multiple navigation trajectory suggestions, thereby optimizing the efficiency of preference data collection with fewer queries. Our methodology demonstrates superior performance in aligning navigation policies with user preferences in low-query regimes, offering enhanced policy convergence from sparse preference queries. The evaluation is complemented with a novel explainability representation, capturing full scene navigation behavior of the mobile robot in a single plot. Our code is available online at https://***/hrl-bonn/EnQuery.

关键词： Mobile robots

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Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task robotic Manipulation

arXiv

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arXiv 2024年

作者： Ma, Xiao Patidar, Sumit Haughton, Iain James, Stephen Dyson Robot Learning Lab

This paper introduces Hierarchical Diffusion Policy (HDP), a hierarchical agent for multi-task robotic manipulation. HDP factorises a manipulation policy into a hierarchical structure: a high-level task-planning agent which predicts a distant next-best end-effector pose (NBP), and a low-level goal-conditioned diffusion policy which generates optimal motion trajectories. The factorised policy representation allows HDP to tackle both long-horizon task planning while generating fine-grained low-level actions. To generate context-aware motion trajectories while satisfying robot kinematics constraints, we present a novel kinematics-aware goal-conditioned control agent, robot Kinematics Diffuser (RK-Diffuser). Specifically, RK-Diffuser learns to generate both the end-effector pose and joint position trajectories, and distill the accurate but kinematics-unaware end-effector pose diffuser to the kinematics-aware but less accurate joint position diffuser via differentiable kinematics. Empirically, we show that HDP achieves a significantly higher success rate than the state-of-the-art methods in both simulation and real-world.1 © 2024, CC BY.

关键词： Kinematics

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BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark

arXiv

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arXiv 2024年

作者： Chernyadev, Nikita Backshall, Nicholas Ma, Xiao Lu, Yunfan Seo, Younggyo James, Stephen Dyson Robot Learning Lab

We introduce BiGym, a new benchmark and learning environment for mobile bi-manual demo-driven robotic manipulation. BiGym features 40 diverse tasks set in home environments, ranging from simple target reaching to complex kitchen cleaning. To capture the real-world performance accurately, we provide human-collected demonstrations for each task, reflecting the diverse modalities found in real-world robot trajectories. BiGym supports a variety of observations, including proprioceptive data and visual inputs such as RGB, and depth from 3 camera views. To validate the usability of BiGym, we thoroughly benchmark the state-of-the-art imitation learning algorithms and demo-driven reinforcement learning algorithms within the environment and discuss the future opportunities. Project website: https://***/bigym/ Copyright © 2024, The Authors. All rights reserved.

关键词： learning algorithms

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