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检索条件"机构=The Robot Learning Lab"

共 847 条记录，以下是71-80 订阅

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

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

引用

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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Crossing the Gap: A Deep Dive into Zero-Shot Sim-to-Real Transfer for Dynamics

Crossing the Gap: A Deep Dive into Zero-Shot Sim-to-Real Tra...

引用

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

作者： Valassakis, Eugene Ding, Zihan Johns, Edward Imperial Coll London Robot Learning Lab London England

ISBN: (纸本)9781728162126

Zero-shot sim-to-real transfer of tasks with complex dynamics is a highly challenging and unsolved problem. A number of solutions have been proposed in recent years, but we have found that many works do not present a thorough evaluation in the real world, or underplay the significant engineering effort and task-specific fine tuning that is required to achieve the published results. In this paper, we dive deeper into the sim-to-real transfer challenge, investigate why this is such a difficult problem, and present objective evaluations of a number of transfer methods across a range of real-world tasks. Surprisingly, we found that a method which simply injects random forces into the simulation performs just as well as more complex methods, such as those which randomise the simulator's dynamics parameters, or adapt a policy online using recurrent network architectures.

关键词： Network architecture

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Reactive Phase and Task Space Adaptation for Robust Motion Execution

Reactive Phase and Task Space Adaptation for Robust Motion E...

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

作者： Englert, Peter Toussaint, Marc Univ Stuttgart Machine Learning & Robot Lab Stuttgart Germany

ISBN: (纸本)9781479969340

An essential aspect for making robots succeed in real- world environments is to give them the ability to robustly perform motions in continuously changing situations. Classical motion planning methods usually create plans for static environments. The direct execution of such plans in dynamic environments often becomes problematic. We present an approach that adapts motion plans by feeding changes of the environment into a transformation of the plan in task space. Furthermore, the progress in the plan is defined with a phase variable that is updated adaptively according to the actual task progress. This phase variable releases the strict time compliance that many motion planning methods bring along. The main benefit of our approach is the ability to do this adaptation in a computational efficient manner during the execution of the motion. Thus, the gap between the motion planning and motion execution stage is bridged by continuously transforming geometric and dynamic features of a reference plan to the current situation. We evaluate the performance of our approach by comparing it to alternative methods such as dynamic motion primitives and continuous replanning on several simulated benchmark tasks. Moreover, we demonstrate the real robot applicability on a PR2 robot platform.

关键词： Aerospace electronics Dynamics Equations Planning robot kinematics Trajectory

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Hybrid ICP

Hybrid ICP

引用

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

作者： Dreczkowski, Kamil Johns, Edward Imperial Coll London Robot Learning Lab London England

ISBN: (纸本)9781665417143

ICP algorithms typically involve a fixed choice of data association method and a fixed choice of error metric. In this paper, we propose Hybrid ICP, a novel and flexible ICP variant which dynamically optimises both the data association method and error metric based on the live image of an object and the current ICP estimate. We show that when used for object pose estimation, Hybrid ICP is more accurate and more robust to noise than other commonly used ICP variants. We also consider the setting where ICP is applied sequentially with a moving camera, and we study the trade-off between the accuracy of each ICP estimate and the number of ICP estimates available within a fixed amount of time.

关键词： Measurement Heuristic algorithms Pose estimation Cameras Intelligent robots Convergence

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learning Multi-Stage Tasks with One Demonstration via Self-Replay 5

Learning Multi-Stage Tasks with One Demonstration via Self-R...

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5th Conference on robot learning (CoRL)

作者： Di Palo, Norman Jonhs, Edward Imperial Coll London Robot Learning Lab London England

In this work, we introduce a novel method to learn everyday-like multi-stage tasks from a single human demonstration, without requiring any prior object knowledge. Inspired by the recent Coarse-to-Fine Imitation learning method, we model imitation learning as a learned object reaching phase followed by an open-loop replay of the demonstrator's actions. We build upon this for multi-stage tasks where, following the human demonstration, the robot can autonomously collect image data for the entire multi-stage task, by reaching the next object in the sequence and then replaying the demonstration, and then repeating in a loop for all stages of the task. We evaluate with real-world experiments on a set of everyday-like multi-stage tasks, which we show that our method can solve from a single demonstration. Videos and supplementary material can be found at this webpage.

关键词： Multi-Stage Imitation learning Manipulation

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Kinematic Morphing Networks for Manipulation Skill Transfer

Kinematic Morphing Networks for Manipulation Skill Transfer

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25th IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Englert, Peter Toussaint, Marc Univ Stuttgart Machine Learning & Robot Lab Stuttgart Germany

ISBN: (纸本)9781538680940

The transfer of a robot skill between different geometric environments is non-trivial since a wide variety of environments exists, sensor observations as well as robot motions are high-dimensional, and the environment might only be partially observed. We consider the problem of extracting a low-dimensional description of the manipulated environment in form of a kinematic model. This allows us to transfer a skill by defining a policy on a prototype model and morphing the observed environment to this prototype. A deep neural network is used to map depth image observations of the environment to morphing parameter, which include transformations and configurations of the prototype model. Using the concatenation property of affine transformations and the ability to convert point clouds to depth images allows to apply the network in an iterative manner. The network is trained on data generated in a simulator and on augmented data that is created with its own predictions. The algorithm is evaluated on different tasks, where it is shown that iterative predictions lead to a higher accuracy than one-step predictions.

关键词： Kinematics Three-dimensional displays robot sensing systems Prototypes Neural networks Solid modeling

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Active learning with Query Paths for Tactile Object Shape Exploration

Active Learning with Query Paths for Tactile Object Shape Ex...

引用

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

作者： Driess, Danny Englert, Peter Toussaint, Marc Univ Stuttgart Machine Learning & Robot Lab Stuttgart Germany

ISBN: (纸本)9781538626825

In the present work, we propose an active learning framework based on optimal query paths to efficiently address the problem of tactile object shape exploration. Most previous approaches perform active touch probing at discrete query points, which leads to inefficient touch-and-retract motions. In contrast, in this paper we propose to query information efficient sliding paths instead of only touch locations. This is realized by three components: A Gaussian process implicit surface model represents the shape and uncertainty of the object. A compliant task/force controller framework fuses the information of this GP model into the parameterization of its tasks, which enables the robot to slide over the unknown object safely and robustly. Thirdly, we develop two strategies to solve the proposed active path querying learning problem. Sliding along those query paths not only creates more dense data than touch probing, but additionally greatly reduces the uncertainty of the object. We demonstrate the effectiveness of our proposed framework both in simulation and on the PR2 robot platform. Furthermore, it is shown that our methodology can be extended to other learning tasks, such as finding a desired surface normal on an unknown object, e.g. for pushing.

关键词： Surface treatment Shape robot sensing systems Uncertainty Data models Gaussian processes

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BiGym: A Demo-Driven Mobile Bi-Manual Manipulation Benchmark 8

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

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8th Conference on robot learning, CoRL 2024

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

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/. © 2024 Proceedings of Machine learning Research.

关键词： Adversarial machine learning

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One-Shot Imitation learning: A Pose Estimation Perspective 7

One-Shot Imitation Learning: A Pose Estimation Perspective

引用

7th Conference on robot learning (CoRL)

作者： Vitiello, Pietro Dreczkowski, Kamil Johns, Edward Imperial Coll London Robot Learning Lab London England

In this paper, we study imitation learning under the challenging setting of: (1) only a single demonstration, (2) no further data collection, and (3) no prior task or object knowledge. We show how, with these constraints, imitation learning can be formulated as a combination of trajectory transfer and unseen object pose estimation. To explore this idea, we provide an in-depth study on how state-of-the-art unseen object pose estimators perform for one-shot imitation learning on ten real-world tasks, and we take a deep dive into the effects that camera calibration, pose estimation error, and spatial generalisation have on task success rates. For videos, please visit ***/pose-estimation-perspective.

关键词： One-Shot Imitation learning Unseen Object Pose Estimation robot Manipulation

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Continuous Control with Coarse-to-fine Reinforcement learning 8

Continuous Control with Coarse-to-fine Reinforcement Learnin...

引用

8th Conference on robot learning, CoRL 2024

作者： Seo, Younggyo Uruç, Jafar James, Stephen Dyson Robot Learning Lab United Kingdom

Despite recent advances in improving the sample-efficiency of reinforcement learning (RL) algorithms, designing an RL algorithm that can be practically deployed in real-world environments remains a challenge. In this paper, we present Coarse-to-fine Reinforcement learning (CRL), a framework that trains RL agents to zoom-into a continuous action space in a coarse-to-fine manner, enabling the use of stable, sample-efficient value-based RL algorithms for fine-grained continuous control tasks. Our key idea is to train agents that output actions by iterating the procedure of (i) discretizing the continuous action space into multiple intervals and (ii) selecting the interval with the highest Q-value to further discretize at the next level. We then introduce a concrete, value-based algorithm within the CRL framework called Coarse-to-fine Q-Network (CQN). Our experiments demonstrate that CQN significantly outperforms RL and behavior cloning baselines on 20 sparsely-rewarded RLBench manipulation tasks with a modest number of environment interactions and expert demonstrations. We also show that CQN robustly learns to solve real-world manipulation tasks within a few minutes of online training. Project website: ***/cqn. © 2024 Proceedings of Machine learning Research.

关键词： Reinforcement learning

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