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pro vyhledávání: '"Xu, Junhong"'
Model-free reinforcement learning has emerged as a powerful method for developing robust robot control policies capable of navigating through complex and unstructured terrains. The effectiveness of these methods hinges on two essential elements: (1)
Externí odkaz:
http://arxiv.org/abs/2410.10766
Bounded rational agents often make decisions by evaluating a finite selection of choices, typically derived from a reference point termed the $`$default policy,' based on previous experience. However, the inherent rigidity of the static default polic
Externí odkaz:
http://arxiv.org/abs/2409.11604
Autor:
Booher, Jonathan, Rohanimanesh, Khashayar, Xu, Junhong, Isenbaev, Vladislav, Balakrishna, Ashwin, Gupta, Ishan, Liu, Wei, Petiushko, Aleksandr
Modern approaches to autonomous driving rely heavily on learned components trained with large amounts of human driving data via imitation learning. However, these methods require large amounts of expensive data collection and even then face challenge
Externí odkaz:
http://arxiv.org/abs/2406.08878
Navigation safety is critical for many autonomous systems such as self-driving vehicles in an urban environment. It requires an explicit consideration of boundary constraints that describe the borders of any infeasible, non-navigable, or unsafe regio
Externí odkaz:
http://arxiv.org/abs/2403.14956
Biological agents, such as humans and animals, are capable of making decisions out of a very large number of choices in a limited time. They can do so because they use their prior knowledge to find a solution that is not necessarily optimal but good
Externí odkaz:
http://arxiv.org/abs/2307.15798
The classical Model Predictive Path Integral (MPPI) control framework lacks reliable safety guarantees since it relies on a risk-neutral trajectory evaluation technique, which can present challenges for safety-critical applications such as autonomous
Externí odkaz:
http://arxiv.org/abs/2306.12369
Robot data collected in complex real-world scenarios are often biased due to safety concerns, human preferences, and mission or platform constraints. Consequently, robot learning from such observational data poses great challenges for accurate parame
Externí odkaz:
http://arxiv.org/abs/2210.08679
When robots share the same workspace with other intelligent agents (e.g., other robots or humans), they must be able to reason about the behaviors of their neighboring agents while accomplishing the designated tasks. In practice, frequently, agents d
Externí odkaz:
http://arxiv.org/abs/2210.08672
Publikováno v:
In Analytical Biochemistry September 2024 692
Autor:
Ma, Qiujuan, Liu, Shuangyu, Xu, Junhong, Mao, Guojiang, Wang, Gege, Hou, Shuqi, Ma, Yijie, Lian, Yujie
Publikováno v:
In Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 15 December 2024 323