Imitation Learning from Observations: An Autoregressive Mixture of Experts Approach
This paper presents a novel approach to imitation learning from observations, where an autoregressive mixture of experts model is deployed to fit the underlying policy. The parameters of the model are learned via a two-stage framework. By leveraging the existing dynamics knowledge, the first stage of the framework estimates the control input sequences and hence reduces the problem complexity. At the second stage, the policy is learned by solving a regularized maximum-likelihood estimation problem using the estimated control input sequences. We further extend the learning procedure by incorporating a Lyapunov stability constraint to ensure asymptotic stability of the identified model, for accurate multi-step predictions. The effectiveness of the proposed framework is validated using two autonomous driving datasets collected from human demonstrations, demonstrating its practical applicability in modelling complex nonlinear dynamics.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingImitation LearningMixture-of-ExpertsSimilar Papers 제목 키워드 기반
ATG-MoE: Autoregressive trajectory generation with mixture-of-experts for assembly skill learning
Flexible manufacturing requires robot systems that can adapt to constantly changing tasks, objects, and environments. However, traditional robot programming is labor-intensive and inflexible, while existing learning-base…
Towards Faster Language Model Inference Using Mixture-of-Experts Flow Matching
Flow matching retains the generation quality of diffusion models while enabling substantially faster inference, making it a compelling paradigm for generative modeling. However, when applied to language modeling, it exhi…
Dynamic Mixture-of-Experts for Visual Autoregressive Model
Visual Autoregressive Models (VAR) offer efficient and high-quality image generation but suffer from computational redundancy due to repeated Transformer calls at increasing resolutions. We introduce a dynamic Mixture-of…
Image GenerationSoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs
Sparse Mixture-of-Experts (MoE) architectures enable scaling LLM parameters under a fixed inference budget by activating only a small subset of experts via top-$k$ routing. While this preserves causality and suits autore…
LAR-MoE: Latent-Aligned Routing for Mixture of Experts in Robotic Imitation Learning
Imitation learning enables robots to acquire manipulation skills from demonstrations, yet deploying a policy across tasks with heterogeneous dynamics remains challenging, as models tend to average over distinct behaviora…