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RB-TRG boosts navigation success for wheeled-legged robots in challenging terrains by 17%, addressing critical body-terrain interactions often overlooked in traditional planning methods.
This method boosts inspection success rates in robotic applications by optimizing joint configurations for better visibility in challenging environments.
Navigate sprawling, multi-floor environments without drowning in grid maps: osmAG-Nav slashes planning latency by up to 7816x using a hierarchical semantic approach.