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Reactive control can achieve complex multi-objective behaviors previously thought impossible, simply by dynamically prioritizing and resolving conflicting objectives within a learned world model.
By coupling particle filters for graspability and movability, robots can now estimate affordances with up to 308% better precision, even in challenging lighting and cluttered environments.