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检索条件"机构=The Robot Learning Lab"
851 条 记 录,以下是81-90 订阅
排序:
Green Screen Augmentation Enables Scene Generalisation in robotic Manipulation
arXiv
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arXiv 2024年
作者: Teoh, Eugene Patidar, Sumit Ma, Xiao James, Stephen Dyson Robot Learning Lab United Kingdom
Generalising vision-based manipulation policies to novel environments remains a challenging area with limited exploration. Current practices involve collecting data in one location, training imitation learning or rein... 详细信息
来源: 评论
ST-RRT*: Asymptotically-Optimal Bidirectional Motion Planning through Space-Time  39
ST-RRT*: Asymptotically-Optimal Bidirectional Motion Plannin...
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IEEE International Conference on robotics and Automation (ICRA)
作者: Grothe, Francesco Hartmann, Valentin N. Orthey, Andreas Toussaint, Marc TU Berlin Learning & Intelligent Syst Grp Berlin Germany Univ Stuttgart Machine Learning & Robot Lab Stuttgart Germany
We present a motion planner for planning through space-time with dynamic obstacles, velocity constraints, and unknown arrival time. Our algorithm, Space-Time RRT* (ST-RRT*), is a probabilistically complete, bidirectio... 详细信息
来源: 评论
Real-time Mapping of Physical Scene Properties with an Autonomous robot Experimenter  6
Real-time Mapping of Physical Scene Properties with an Auton...
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6th Conference on robot learning (CoRL)
作者: Haughton, Iain Sucar, Edgar Mouton, Andre Johns, Edward Davison, Andrew J. Dyson Technol Ltd Malmesbury England Imperial Coll Dyson Robot Lab London England Imperial Coll Robot Learning Lab London England
Neural fields can be trained from scratch to represent the shape and appearance of 3D scenes efficiently. It has also been shown that they can densely map correlated properties such as semantics, via sparse interactio... 详细信息
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GPyro: uncertainty-aware temperature predictions for additive manufacturing
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JOURNAL OF INTELLIGENT MANUFACTURING 2023年 第1期34卷 243-259页
作者: Sideris, Iason Crivelli, Francesco Bambach, Markus Swiss Fed Inst Technol Adv Mfg Lab Zurich Ramistr 101 CH-8092 Zurich Switzerland CSEM SA Grp Robot & Machine Learning Grundlistr 1 CH-6055 Alpnach Switzerland
In additive manufacturing, process-induced temperature profiles are directly linked to part properties, and their prediction is crucial for achieving high-quality products. Temperature predictions require an accurate ... 详细信息
来源: 评论
Generative Image as Action Models
arXiv
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arXiv 2024年
作者: Shridhar, Mohit Lo, Yat Long James, Stephen Dyson Robot Learning Lab United Kingdom
Image-generation diffusion models have been fine-tuned to unlock new capabilities such as image-editing and novel view synthesis. Can we similarly unlock image-generation models for visuomotor control? We present GENI... 详细信息
来源: 评论
Adapting Skills to Novel Grasps: A Self-Supervised Approach
Adapting Skills to Novel Grasps: A Self-Supervised Approach
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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)
作者: Georgios Papagiannis Kamil Dreczkowski Vitalis Vosylius Edward Johns The Robot Learning Lab at Imperial College London
In this paper, we study the problem of adapting manipulation trajectories involving grasped objects (e.g. tools) defined for a single grasp pose to novel grasp poses. A common approach to address this is to define a n... 详细信息
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DINOBot: robot Manipulation via Retrieval and Alignment with Vision Foundation Models
DINOBot: Robot Manipulation via Retrieval and Alignment with...
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IEEE International Conference on robotics and Automation (ICRA)
作者: Norman Di Palo Edward Johns The Robot Learning Lab at Imperial College London
We propose DINOBot, a novel imitation learning framework for robot manipulation, which leverages the image-level and pixel-level capabilities of features extracted from Vision Transformers trained with DINO. When inte... 详细信息
来源: 评论
A learning-based Controller for Multi-Contact Grasps on Unknown Objects with a Dexterous Hand
A Learning-based Controller for Multi-Contact Grasps on Unkn...
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2024 International Conference on Intelligent robots and Systems
作者: Winkelbauer, Dominik Triebel, Rudolph Baeuml, Berthold DLR Inst Robot & Mechatron Wessling Germany Tech Univ Munich Learning AI Dextrous Robots Lab Munich Germany Karlsruhe Inst Technol KIT Karlsruhe Germany
Existing grasp controllers usually either only support finger-tip grasps or need explicit configuration of the inner forces. We propose a novel grasp controller that supports arbitrary grasp types, including power gra... 详细信息
来源: 评论
Continuous Control with Coarse-to-fine Reinforcement learning
arXiv
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arXiv 2024年
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 ... 详细信息
来源: 评论
Effective Streaming Low-Tubal-Rank Tensor Approximation via Frequent Directions
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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2024年 第1期35卷 1113-1126页
作者: Yi, Qianxin Wang, Chenhao Wang, Kaidong Wang, Yao Xi An Jiao Tong Univ Ctr Intelligent Decis Making & Machine Learning Sch Management Xian 710049 Peoples R China Chinese Acad Sci State Key Lab Robot Shenyang Inst Automat Shenyang 110016 Peoples R China
Low-tubal-rank tensor approximation has been proposed to analyze large-scale and multidimensional data. However, finding such an accurate approximation is challenging in the streaming setting, due to the limited compu... 详细信息
来源: 评论