Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Pushp, Durgakant"'
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
Mapless navigation has emerged as a promising approach for enabling autonomous robots to navigate in environments where pre-existing maps may be inaccurate, outdated, or unavailable. In this work, we propose an image-based local representation of the
Externí odkaz:
http://arxiv.org/abs/2310.14065
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
Traversability prediction is a fundamental perception capability for autonomous navigation. Deep neural networks (DNNs) have been widely used to predict traversability during the last decade. The performance of DNNs is significantly boosted by exploi
Externí odkaz:
http://arxiv.org/abs/2306.14370
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
We propose a novel hybrid system (both hardware and software) of an Unmanned Aerial Vehicle (UAV) carrying a miniature Unmanned Ground Vehicle (miniUGV) to perform a complex search and manipulation task. This system leverages heterogeneous robots to
Externí odkaz:
http://arxiv.org/abs/2209.11704
Traversability prediction is a fundamental perception capability for autonomous navigation. The diversity of data in different domains imposes significant gaps to the prediction performance of the perception model. In this work, we make efforts to re
Externí odkaz:
http://arxiv.org/abs/2204.09617