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.

翻译：精确建模机器人动力学对于基于模型的控制至关重要，但在分布偏移和实时约束下仍具挑战性。本文将系统辨识表述为上下文内元学习问题，并比较确定性序列模型与生成序列模型在前向动力学预测中的表现。我们以Transformer为基础的元模型作为强确定性基线，并引入两种互补的基于扩散的方法： (i) 填充式扩散模型，学习联合输入-观测分布； (ii) 条件扩散模型（CNN与Transformer），基于控制输入生成未来观测。通过大规模随机仿真，我们分析了模型在分布内与分布外场景下的性能，以及与控制相关的计算权衡。实验表明，扩散模型在分布偏移下显著提升鲁棒性，其中填充式扩散模型取得最优性能。最后，我们证实热启动采样使扩散模型能够在实时约束下运行，使其适用于控制应用。这些结果凸显了生成式元模型作为机器人鲁棒系统辨识方向的潜力。