Uncertainty-aware Planning with Inaccurate Models for Robotized Liquid Handling

2025-07-28 · Marco Faroni, Carlo Odesco, Andrea Zanchettin, Paolo Rocco arxiv

Physics-based simulations and learning-based models are vital for complex robotics tasks like deformable object manipulation and liquid handling. However, these models often struggle with accuracy due to epistemic uncertainty or the sim-to-real gap. For instance, accurately pouring liquid from one container to another poses challenges, particularly when models are trained on limited demonstrations and may perform poorly in novel situations. This paper proposes an uncertainty-aware Monte Carlo Tree Search (MCTS) algorithm designed to mitigate these inaccuracies. By incorporating estimates of model uncertainty, the proposed MCTS strategy biases the search towards actions with lower predicted uncertainty. This approach enhances the reliability of planning under uncertain conditions. Applied to a liquid pouring task, our method demonstrates improved success rates even with models trained on minimal data, outperforming traditional methods and showcasing its potential for robust decision-making in robotics.

📄 PDF Abstract BibTeX arXiv:2507.20861

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Bounding-Box Inference for Error-Aware Model-Based Reinforcement Learning

2024-06-23 · Erin J. Talvitie, Zilei Shao, Huiying Li, Jinghan Hu 외

In model-based reinforcement learning, simulated experiences from the learned model are often treated as equivalent to experience from the real environment. However, when the model is inaccurate, it can catastrophically …

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Look Before Leap: Look-Ahead Planning with Uncertainty in Reinforcement Learning

2025-03-26 · Yongshuai Liu, Xin Liu

Model-based reinforcement learning (MBRL) has demonstrated superior sample efficiency compared to model-free reinforcement learning (MFRL). However, the presence of inaccurate models can introduce biases during policy le…

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DiffusionRollout: Uncertainty-Aware Rollout Planning in Long-Horizon PDE Solving

2026-02-14 · Seungwoo Yoo, Juil Koo, Daehyeon Choi, Minhyuk Sung arxiv

We propose DiffusionRollout, a novel selective rollout planning strategy for autoregressive diffusion models, aimed at mitigating error accumulation in long-horizon predictions of physical systems governed by partial dif…

A Systematic Literature Review of Computer Vision Applications in Robotized Wire Harness Assembly

2023-09-24 · Hao Wang, Omkar Salunkhe, Walter Quadrini, Dan Lämkull 외

This article provides a systematic literature review of computer vision applications in robotized wire harness assembly.

Systematic Literature Review

Overview of Computer Vision Techniques in Robotized Wire Harness Assembly: Current State and Future Opportunities