ManiDreams: An Open-Source Library for Robust Object Manipulation via Uncertainty-aware Task-specific Intuitive Physics

Dynamics models, whether simulators or learned world models, have long been central to robotic manipulation, but most of these models focus on minimizing prediction error rather than confronting a more fundamental challenge: real-world manipulation is inherently uncertain. We argue that robust manipulation under uncertainty is fundamentally an integration problem: uncertainties must be represented, propagated, and constrained within the planning loop, not merely suppressed during training.

We present and open-source ManiDreams, a modular framework for uncertainty-aware manipulation planning over intuitive physics models that realizes this integration through composable abstractions for distributional state representation, backend-agnostic dynamics prediction, and declarative constraint specification for action optimization. The framework explicitly addresses three sources of uncertainty: perceptual, parametric, and structural. It wraps any base policy with a sample-predict-constrain loop that evaluates candidate actions against distributional outcomes, adding robustness without retraining. Experiments on default ManiSkill tasks show that ManiDreams maintains robust performance under various perturbations, where the RL baseline degrades significantly. Runnable examples on pushing, picking, catching, and real world deployment demonstrate flexibility for applications across different policies, optimizers, physics backends, and executors.