Diffusion Sequence Models for Generative In-Context Meta-Learning of Robot Dynamics

• URL: http://arxiv.org/abs/2604.13366v2
• Date: Mon, 20 Apr 2026 14:01:36 GMT
• Title: Diffusion Sequence Models for Generative In-Context Meta-Learning of Robot Dynamics
• Abstract summary: We formulate system identification as an in-context meta-learning problem.<n>We compare deterministic and generative sequence models for forward dynamics prediction.<n>Results highlight generative meta-models as a promising direction for robust system identification in robotics.
• Score: 1.6441639841508602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate modeling of robot dynamics is essential for model-based control, yet remains challenging under distributional shifts and real-time constraints. In this work, we formulate system identification as an in-context meta-learning problem and compare deterministic and generative sequence models for forward dynamics prediction. We take a Transformer-based meta-model, as a strong deterministic baseline, and introduce to this setting two complementary diffusion-based approaches: (i) inpainting diffusion (Diffuser), which learns the joint input-observation distribution, and (ii) conditioned diffusion models (CNN and Transformer), which generate future observations conditioned on control inputs. Through large-scale randomized simulations, we analyze performance across in-distribution and out-of-distribution regimes, as well as computational trade-offs relevant for control. We show that diffusion models significantly improve robustness under distribution shift, with inpainting diffusion achieving the best performance in our experiments. Finally, we demonstrate that warm-started sampling enables diffusion models to operate within real-time constraints, making them viable for control applications. These results highlight generative meta-models as a promising direction for robust system identification in robotics.

Related papers

• Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systems [1.9800280967139285]
  High-fidelity numerical simulations of chaotic, high dimensional nonlinear dynamical systems are computationally expensive.<n>Most surrogate models for such systems are deterministic, for example when neural operators are involved.<n>This work presents a surrogate modeling formulation that leverages generative machine learning.
  arXiv  Detail & Related papers  (2026-03-13T04:11:37Z)
• Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making [48.998030470623384]
  offline decision-making requires reliable behaviors from fixed datasets without further interaction.<n>We propose a compositional model-based diffusion framework consisting of: (i) a planner that generates diverse, task-aligned trajectories; (ii) a dynamics model that enforces consistency with the underlying system dynamics; and (iii) a ranker module that selects behaviors aligned with the task objectives.
  arXiv  Detail & Related papers  (2025-12-09T06:26:02Z)
• On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning [14.707830064594056]
  Diffusion autoencoders (DAs) use an input-dependent latent variable to capture representations alongside the diffusion process.<n>Better generative modelling is the primary goal of another class of diffusion models -- those that learn their forward (noising) process.
  arXiv  Detail & Related papers  (2025-05-30T18:14:09Z)
• Consistent World Models via Foresight Diffusion [56.45012929930605]
  We argue that a key bottleneck in learning consistent diffusion-based world models lies in the suboptimal predictive ability.<n>We propose Foresight Diffusion (ForeDiff), a diffusion-based world modeling framework that enhances consistency by decoupling condition understanding from target denoising.
  arXiv  Detail & Related papers  (2025-05-22T10:01:59Z)
• Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models [71.63194926457119]
  We introduce Dynamical Diffusion (DyDiff), a theoretically sound framework that incorporates temporally aware forward and reverse processes.<n>Experiments across scientifictemporal forecasting, video prediction, and time series forecasting demonstrate that Dynamical Diffusion consistently improves performance in temporal predictive tasks.
  arXiv  Detail & Related papers  (2025-03-02T16:10:32Z)
• FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation [15.64941169350615]
  Data assimilation (DA) integrates observations with a dynamical model to estimate states of PDE-governed systems.<n>FlowDAS is a generative DA framework that uses interpolants to learn state transition dynamics.<n>We show that FlowDAS surpasses model-driven methods, neural operators, and score-based baselines in accuracy and physical plausibility.
  arXiv  Detail & Related papers  (2025-01-13T05:03:41Z)
• Energy-Based Diffusion Language Models for Text Generation [126.23425882687195]
  Energy-based Diffusion Language Model (EDLM) is an energy-based model operating at the full sequence level for each diffusion step.<n>Our framework offers a 1.3$times$ sampling speedup over existing diffusion models.
  arXiv  Detail & Related papers  (2024-10-28T17:25:56Z)
• Data-Driven Stochastic Closure Modeling via Conditional Diffusion Model and Neural Operator [0.0]
  Closure models are widely used in simulating complex multiscale dynamical systems such as turbulence and the earth system.<n>For systems without a clear scale, generalization deterministic and local closure models often lack enough capability.<n>We propose a datadriven modeling framework for constructing neural operator and non-local closure models.
  arXiv  Detail & Related papers  (2024-08-06T05:21:31Z)
• Generative Modeling with Phase Stochastic Bridges [49.4474628881673]
  Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs. We introduce a novel generative modeling framework grounded in textbfphase space dynamics Our framework demonstrates the capability to generate realistic data points at an early stage of dynamics propagation.
  arXiv  Detail & Related papers  (2023-10-11T18:38:28Z)
• Diffusion Glancing Transformer for Parallel Sequence to Sequence Learning [52.72369034247396]
  We propose the diffusion glancing transformer, which employs a modality diffusion process and residual glancing sampling. DIFFGLAT achieves better generation accuracy while maintaining fast decoding speed compared with both autoregressive and non-autoregressive models.
  arXiv  Detail & Related papers  (2022-12-20T13:36:25Z)

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.