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Begriffe
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Multimodal Imitation Learning

Learning paradigm where the agent learns from demonstrations presenting multiple valid solutions for the same task, capturing the diversity of expert behaviors.

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Multimodal Policy Distribution

Probabilistic representation of a policy containing multiple distinct modes, each corresponding to a different resolution strategy to achieve the same objective.

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Action Mode

Specific behavioral configuration within a multimodal distribution, representing a distinct and coherent way of executing a task.

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Demonstration Aggregation

Process of intelligently combining learning trajectories from different strategies to build a unified policy preserving multimodality.

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Gaussian Mixture for Policies

Parametric model representing a multimodal policy as a weighted combination of multiple Gaussian distributions, each Gaussian corresponding to a behavioral mode.

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Trajectory Posterior

Conditional distribution on possible trajectories given an initial state and the objective, capturing uncertainty about the different ways to reach the target.

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Behavioral Latent Space

Compressed representation where each point encodes a distinct behavioral strategy, allowing navigation and interpolation between different action modes.

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Demonstration Clustering

Automatic grouping technique of similar trajectories to identify and isolate the different behavioral modes present in imitation data.

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Modality-Action Decoupling

Architecture where the selection of the behavioral mode and the generation of specific actions are handled by separate components for better multimodal modeling.

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Mode Inference

Process of identifying the appropriate behavioral mode to use in a given situation, based on the context and the agent's preferences.

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Conditional Policy

Policy that generates actions conditioned on both the current state and a selected mode identifier, allowing for explicit control of the adopted strategy.

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Optimal Solution Selection

Mechanism that evaluates and chooses the best solution among several valid alternatives based on criteria such as efficiency, robustness, or contextual constraints.

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Behavioral Variance

Statistical measure quantifying the dispersion of actions or trajectories around each mode, distinguishing intra-mode variability from multimodality.

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Mode Fusion

Technique combining several behavioral modes to create new hybrid strategies, leveraging the strengths of each individual mode.

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Sub-policy Learning

Approach where each mode is modeled as a specialized sub-policy, learned independently before being integrated into a global multimodal architecture.

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Solution Hierarchy

Organizational structure of different valid solutions, often based on their complexity, efficiency, or optimal application context.

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Mode-guided Exploration

Exploration strategy that maintains consistency with a selected behavioral mode while discovering new valid variations within that mode.

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Inter-modal Transfer

Ability to transfer knowledge acquired in one behavioral mode to improve learning or performance in another similar mode.

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Multimodal Evaluation

Evaluation metric that considers not only the average performance but also the coverage and quality of the different learned behavioral modes.

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