Long-Horizon Prediction and Uncertainty Propagation with Residual Point Contact Learners
Long-Horizon Prediction and Uncertainty Propagation with Residual Point Contact Learners
复制标题
残差点接触学习器的长视野预测和不确定性传播
DOI：
10.1109/icra40945.2020.9196511
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Alberto Rodriguez
中科院分区：
文献类型：
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作者：
Nima Fazeli;Anurag Ajay;Alberto Rodriguez
模拟和预测接触结果的能力对许多机器人任务的成功执行至关重要。模拟器是设计机器人及其行为的有力工具，但它们的预测与观测数据之间的差异限制了它们的可用性。在本文中，我们提出了一种用于刚体模拟器的自监督残差模型学习方法，该方法利用接触模型的校正来改进预测性能并传播不确定性。我们通过预测平面掷骰子的结果对该框架进行了经验评估，并将其性能与最先进的技术进行了比较。
The ability to simulate and predict the outcome of contacts is paramount to the successful execution of many robotic tasks. Simulators are powerful tools for the design of robots and their behaviors, yet the discrepancy between their predictions and observed data limit their usability. In this paper, we propose a self-supervised approach to learning residual models for rigid-body simulators that exploits corrections of contact models to refine predictive performance and propagate uncertainty. We empirically evaluate the framework by predicting the outcomes of planar dice rolls and compare it’s performance to state-of-the-art techniques.
DOI：
10.15607/rss.2017.xiii.040
发表时间：
2017-05
期刊：
ArXiv
影响因子：
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作者：
Jiaji Zhou;J. Bagnell;M. T. Mason
通讯作者：
Jiaji Zhou;J. Bagnell;M. T. Mason