2023
DOI: 10.1126/scirobotics.adf6991
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Navigating to objects in the real world

Abstract: Semantic navigation is necessary to deploy mobile robots in uncontrolled environments such as homes or hospitals. Many learning-based approaches have been proposed in response to the lack of semantic understanding of the classical pipeline for spatial navigation, which builds a geometric map using depth sensors and plans to reach point goals. Broadly, end-to-end learning approaches reactively map sensor inputs to actions with deep neural networks, whereas modular learning approaches enrich the classical pipeli… Show more

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Cited by 36 publications
(3 citation statements)
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“…Importantly, modules allow the combination of differentiated learning paradigms, such as a basal ganglia model with reinforcement learning connected to a cerebellar model with supervised learning. Implementation-wise, as in modular deep learning, components can thus be optimized while retaining their overall context to ensure that solutions meet global task requirements as well as constraints imposed by other components [ 12 ]. This is highly efficient and generalizes better than end-to-end optimization of the full model [ 13 ].…”
Section: Modular-integrative Modelingmentioning
confidence: 99%
“…Importantly, modules allow the combination of differentiated learning paradigms, such as a basal ganglia model with reinforcement learning connected to a cerebellar model with supervised learning. Implementation-wise, as in modular deep learning, components can thus be optimized while retaining their overall context to ensure that solutions meet global task requirements as well as constraints imposed by other components [ 12 ]. This is highly efficient and generalizes better than end-to-end optimization of the full model [ 13 ].…”
Section: Modular-integrative Modelingmentioning
confidence: 99%
“…RL has garnered significant interest in both the research and industrial sectors, particularly when combined with deep learning methods in a framework known as deep reinforcement learning 5 , 5 . It finds applications in various fields related to human intelligence, such as gaming 5 12 , autonomous driving 13 , 14 , robotics 15 , 16 , software testing 17 , 18 , quantum control 19 – 21 , and more. However, existing deep reinforcement learning algorithms often require large amounts of data and millions of training steps to achieve human-level performance, which is considered a challenge due to their low sample efficiency.…”
Section: Introductionmentioning
confidence: 99%
“…The overall goal of this field of AI is to deliver robotic agents that carry out tasks, manipulate objects, and can serve people in their daily routines. There are multitudes of challenges arising from this field, such as navigation (Gervet et al, 2022), natural language processing (NLP) (Shridhar et al, 2020a), simulation (Weihs et al, 2020), computer vision (Anderson et al, 2018), motion and task planning (Garrett et al, 2021), manipulation challenges (Xie et al, 2019), challenges involving all of these (Bohren et al, 2011), and even hardware limitations, just to name a few. However, another perhaps often undervalued challenge is breaking down the inherently complex language commands into simpler, more manageable, sub-commands or "sub-tasks. "…”
Section: Introductionmentioning
confidence: 99%