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pro vyhledávání: '"Behavior planning"'
Enabling humanoid robots to perform autonomously loco-manipulation in unstructured environments is crucial and highly challenging for achieving embodied intelligence. This involves robots being able to plan their actions and behaviors in long-horizon
Externí odkaz:
http://arxiv.org/abs/2408.08282
Robots executing tasks following human instructions in domestic or industrial environments essentially require both adaptability and reliability. Behavior Tree (BT) emerges as an appropriate control architecture for these scenarios due to its modular
Externí odkaz:
http://arxiv.org/abs/2405.07474
Enabling robots to autonomously perform hybrid motions in diverse environments can be beneficial for long-horizon tasks such as material handling, household chores, and work assistance. This requires extensive exploitation of intrinsic motion capabil
Externí odkaz:
http://arxiv.org/abs/2406.14655
The integration of autonomous vehicles into urban and highway environments necessitates the development of robust and adaptable behavior planning systems. This study presents an innovative approach to address this challenge by utilizing a Monte-Carlo
Externí odkaz:
http://arxiv.org/abs/2310.12075
Akademický článek
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Autor:
Vermaelen, Jan, Holvoet, Tom
Publikováno v:
EPTCS 391, 2023, pp. 26-41
The safe operation of an autonomous system is a complex endeavor, one pivotal element being its decision-making. Decision-making logic can formally be analyzed using model checking or other formal verification approaches. Yet, the non-deterministic n
Externí odkaz:
http://arxiv.org/abs/2310.02339
Autor:
Puphal, Tim, Eggert, Julian
We consider the problem of group interactions in urban driving. State-of-the-art behavior planners for self-driving cars mostly consider each single agent-to-agent interaction separately in a cost function in order to find an optimal behavior for the
Externí odkaz:
http://arxiv.org/abs/2307.10714
Reinforcement learning has received high research interest for developing planning approaches in automated driving. Most prior works consider the end-to-end planning task that yields direct control commands and rarely deploy their algorithm to real v
Externí odkaz:
http://arxiv.org/abs/2304.08280
The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such as processed LIDAR and camera data from other vehicles. In this work, we design an integrated i
Externí odkaz:
http://arxiv.org/abs/2302.04321
Making safe and human-like decisions is an essential capability of autonomous driving systems, and learning-based behavior planning presents a promising pathway toward achieving this objective. Distinguished from existing learning-based methods that
Externí odkaz:
http://arxiv.org/abs/2212.08787