Zero-Shot Transferable Solution Method for Parametric Optimal Control Problems
This paper presents a transferable solution method for optimal control problems with varying objectives using function encoder (FE) policies. Traditional optimization-based approaches must be re-solved whenever objectives change, resulting in prohibitive computational costs for applications requiring frequent evaluation and adaptation. The proposed method learns a reusable set of neural basis functions that spans the control policy space, enabling efficient zero-shot adaptation to new tasks through either projection from data or direct mapping from problem specifications. The key idea is an offline-online decomposition: basis functions are learned once during offline imitation learning, while online adaptation requires only lightweight coefficient estimation. Numerical experiments across diverse dynamics, dimensions, and cost structures show our method delivers near-optimal performance with minimal overhead when generalizing across tasks, enabling semi-global feedback policies suitable for real-time deployment.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Parametric Memory Decoding for Zero-Shot Routing in LoRA-Based External Parametric Memory
With the rise of parametric memory, LoRA-based External Parametric Memory (EPM) has emerged as a modular solution, but existing routing methods often introduce additional training, deployment, and maintenance overhead. T…
Jump Operator Planning: Goal-Conditioned Policy Ensembles and Zero-Shot Transfer
In Hierarchical Control, compositionality, abstraction, and task-transfer are crucial for designing versatile algorithms which can solve a variety of problems with maximal representational reuse. We propose a novel hiera…
Transferable End-to-end Room Layout Estimation via Implicit Encoding
We study the problem of estimating room layouts from a single panorama image. Most former works have two stages: feature extraction and parametric model fitting. Here we propose an end-to-end method that directly predict…
Room Layout EstimationZemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks
Although large language models have achieved impressive zero-shot ability, the huge model size generally incurs high cost. Recently, semi-parametric language models, which augment a smaller language model with an externa…
Language ModelingLanguage ModellingRetrievalText Augmentation+1Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning
Zero-shot stance detection is challenging because it requires detecting the stance of previously unseen targets in the inference phase. The ability to learn transferable target-invariant features is critical for zero-sho…
Contrastive LearningStance DetectionZero-Shot Stance Detection