Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why

• URL: http://arxiv.org/abs/2507.05906v2
• Date: Tue, 15 Jul 2025 11:07:33 GMT
• Title: Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why
• Authors: Chenhao Li, Marco Hutter, Andreas Krause,
• Abstract summary: This survey provides a comparative analysis of feature-based and GAN-based approaches to learning from demonstrations.<n>We argue that the dichotomy between feature-based and GAN-based methods is increasingly nuanced.
• Score: 50.191655141020505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This survey provides a comparative analysis of feature-based and GAN-based approaches to learning from demonstrations, with a focus on the structure of reward functions and their implications for policy learning. Feature-based methods offer dense, interpretable rewards that excel at high-fidelity motion imitation, yet often require sophisticated representations of references and struggle with generalization in unstructured settings. GAN-based methods, in contrast, use implicit, distributional supervision that enables scalability and adaptation flexibility, but are prone to training instability and coarse reward signals. Recent advancements in both paradigms converge on the importance of structured motion representations, which enable smoother transitions, controllable synthesis, and improved task integration. We argue that the dichotomy between feature-based and GAN-based methods is increasingly nuanced: rather than one paradigm dominating the other, the choice should be guided by task-specific priorities such as fidelity, diversity, interpretability, and adaptability. This work outlines the algorithmic trade-offs and design considerations that underlie method selection, offering a framework for principled decision-making in learning from demonstrations.

Related papers

• AURORA: Augmented Understanding via Structured Reasoning and Reinforcement Learning for Reference Audio-Visual Segmentation [113.75682363364004]
  AURORA is a framework designed to enhance genuine reasoning and language comprehension in reference audio-visual segmentation.<n>AURORA achieves state-of-the-art performance on Ref-AVS benchmarks and generalizes effectively to unreferenced segmentation.
  arXiv  Detail & Related papers  (2025-08-04T07:47:38Z)
• DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer [50.64531021352504]
  Large language model-based agents, empowered by in-context learning (ICL), have demonstrated strong capabilities in complex reasoning and tool-use tasks.<n>Existing approaches typically rely on example selection, including in some agentic or multi-step settings.<n>We propose DICE, a theoretically grounded ICL framework for agentic tasks that selects the most relevant demonstrations at each step of reasoning.
  arXiv  Detail & Related papers  (2025-07-31T13:42:14Z)
• Learning Temporal Abstractions via Variational Homomorphisms in Option-Induced Abstract MDPs [17.335266921332092]
  Large Language Models (LLMs) have shown remarkable reasoning ability through explicit Chain-of-Thought prompting.<n>We develop a framework for efficient, implicit reasoning, where the model "thinks" in a latent space without generating explicit text for every step.
  arXiv  Detail & Related papers  (2025-07-22T11:22:58Z)
• Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID [82.12123628480371]
  Unsupervised person re-identification (USL-VI-ReID) seeks to match pedestrian images of the same individual across different modalities without human annotations for model learning.<n>Previous methods unify pseudo-labels of cross-modality images through label association algorithms and then design contrastive learning framework for global feature learning.<n>We propose a Semantic-Aligned Learning with Collaborative Refinement (SALCR) framework, which builds up objective for specific fine-grained patterns emphasized by each modality.
  arXiv  Detail & Related papers  (2025-04-27T13:58:12Z)
• Demonstration Selection for In-Context Learning via Reinforcement Learning [16.103533806505403]
  Relevance-Diversity Enhanced Selection (RDES) is an innovative approach to optimize the selection of diverse reference demonstrations.<n>RDES employs frameworks like Q-learning and a PPO-based variant to dynamically identify demonstrations that maximize diversity.<n>We demonstrate that RDES significantly enhances performance compared to ten established baselines.
  arXiv  Detail & Related papers  (2024-12-05T08:33:52Z)
• Independence Constrained Disentangled Representation Learning from Epistemological Perspective [13.51102815877287]
  Disentangled Representation Learning aims to improve the explainability of deep learning methods by training a data encoder that identifies semantically meaningful latent variables in the data generation process. There is no consensus regarding the objective of disentangled representation learning. We propose a novel method for disentangled representation learning by employing an integration of mutual information constraint and independence constraint.
  arXiv  Detail & Related papers  (2024-09-04T13:00:59Z)
• Hierarchical Decision Making Based on Structural Information Principles [19.82391136775341]
  We propose a novel Structural Information principles-based framework, namely SIDM, for hierarchical Decision Making.<n>We present an abstraction mechanism that processes historical state-action trajectories to construct abstract representations of states and actions.<n>We develop a skill-based learning method for single-agent scenarios and a role-based collaboration method for multi-agent scenarios, both of which can flexibly integrate various underlying algorithms for enhanced performance.
  arXiv  Detail & Related papers  (2024-04-15T13:02:00Z)
• A Probabilistic Model Behind Self-Supervised Learning [53.64989127914936]
  In self-supervised learning (SSL), representations are learned via an auxiliary task without annotated labels. We present a generative latent variable model for self-supervised learning. We show that several families of discriminative SSL, including contrastive methods, induce a comparable distribution over representations.
  arXiv  Detail & Related papers  (2024-02-02T13:31:17Z)
• Flow Factorized Representation Learning [109.51947536586677]
  We introduce a generative model which specifies a distinct set of latent probability paths that define different input transformations. We show that our model achieves higher likelihoods on standard representation learning benchmarks while simultaneously being closer to approximately equivariant models.
  arXiv  Detail & Related papers  (2023-09-22T20:15:37Z)
• Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction [166.87111665908333]
  We propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL) Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks.
  arXiv  Detail & Related papers  (2020-04-29T02:32:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.