潜在不规范领域支持的启发式适应用于随机动态系统中的似然自由推断
Posterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation
AI总结：
本文提出三种启发式方法，用于改进随机动态系统中似然自由推断的支持适应，提升参数推断和策略学习的鲁棒性。
摘要：
在机器人领域，似然自由推断（LFI）可以提供适应学习代理在参数集部署条件下的领域分布。LFI假设任意支持用于采样，该支持在初始通用先验被迭代细化为更描述性后验的过程中保持不变。然而，潜在不规范的支持可能导致次优但错误确定的后验。为了解决这个问题，我们提出了三种启发式LFI变体：EDGE、MODE和CENTRE。每种都以自己独特的方式解释推断步骤中的后验模式位移，并且当整合到LFI步骤中时，会同时适应支持和后验推断。我们首先揭示了支持不规范问题，并使用随机动态基准评估我们的启发式方法。然后评估启发式支持适应对参数推断和策略学习在动态变形线性物体（DLO）操控任务中的影响。推断结果在参数集DLOs的长度和刚度分类上更精细。当由此产生的后验用作基于模拟的策略学习的领域分布时，它们导致更稳健的以对象为中心的智能体性能。
英文摘要：
Likelihood-free inference (LFI) enables system identification in complex tasks via black-box modelling, abstracting nonlinearity and stochasticity, and infers a domain distribution for adapting agents to parametric deployment conditions. LFI assumes an arbitrary support for sampling, which remains fixed as the initial generic prior is refined to increasingly descriptive posteriors. Misspecified support can therefore yield suboptimal yet overconfident posteriors. We address this issue by using the posterior of an inference step to guide the adaptation of the support using three illustrative heuristics: EDGE, MODE, and CENTRE. Each heuristic interprets the updated belief and enables support adaptation alongside posterior inference. For illustrative purposes, we first study misspecified support in LFI and evaluate the utility of our heuristics using stochastic dynamical benchmarks. We then evaluate posterior-driven heuristic support adaptation for parameter inference and policy learning in a dynamic deformable linear object (DLO) manipulation task. Inference results in a finer length and stiffness classification for a parametric set of DLOs. When the resulting posteriors are used as domain distributions for sim-based policy learning, they lead to more robust object-centric agent performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
