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PL-LIT achieves state-of-the-art performance in thermal SLAM, even in challenging environments where traditional methods falter.
Mistakes in human demonstrations can enhance robot learning when properly harnessed, revealing a new dimension of value estimation that traditional methods overlook.
Overcome communication bottlenecks in multi-agent RL by selectively communicating with reachable agents and predicting interference to optimize partner choice.
