论文标题
长匹马预测和不确定性传播与残留点联系人学习者
Long-Horizon Prediction and Uncertainty Propagation with Residual Point Contact Learners
论文作者
论文摘要
模拟和预测接触结果的能力对于成功执行许多机器人任务至关重要。模拟器是设计机器人及其行为的强大工具，但是其预测与观察到的数据之间的差异限制了其可用性。在本文中，我们提出了一种自我监督的方法，用于学习刚体模拟器的残差模型，以利用接触模型的校正来提高预测性能并传播不确定性。我们通过预测平面骰子卷的结果并将其性能与最新技术进行比较，从经验上评估了框架。
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.
