用不确定性感知的搜索提升机器人倒液任务的可靠性
Uncertainty-aware Planning with Inaccurate Models for Robotized Liquid Handling
- 引入模型不确定性的估计，指导蒙特卡洛树搜索选择更稳健动作
- 在少量训练数据下仍实现更高倒液成功率，优于传统方法
- 适合需要高可靠性的机器人操作场景，如实验室自动化
基于物理的仿真和学习模型对复杂机器人任务（如可变形物体操作和液体处理）至关重要。然而，由于认知不确定性或仿真到现实的差距，这些模型常存在精度问题。例如，在不同容器间精确倒液具有挑战性，尤其当模型仅基于有限演示训练时，可能在新情境下表现不佳。本文提出一种考虑不确定性的蒙特卡洛树搜索（MCTS）算法，通过引入模型不确定性的估计，使搜索过程倾向于选择预测不确定性更低的动作，从而增强在不确定条件下的规划可靠性。该方法应用于液体倒取任务，在仅使用少量数据训练的模型上也表现出更高的成功概率，显著优于传统方法，展现出在机器人决策中实现鲁棒性的潜力。
原文摘要 · Abstract (English)
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.
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