当前位置: X-MOL 学术IEEE Robot. Automation Lett. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Heteroscedastic Uncertainty for Robust Generative Latent Dynamics
IEEE Robotics and Automation Letters ( IF 5.2 ) Pub Date : 2020-10-01 , DOI: 10.1109/lra.2020.3015449
Oliver Limoyo , Bryan Chan , Filip Maric , Brandon Wagstaff , A. Rupam Mahmood , Jonathan Kelly

Learning or identifying dynamics from a sequence of high-dimensional observations is a difficult challenge in many domains, including reinforcement learning, and control. The problem has recently been studied from a generative perspective through latent dynamics: high-dimensional observations are embedded into a lower-dimensional space in which the dynamics can be learned. Despite some successes, latent dynamics models have not yet been applied to real-world robotic systems where learned representations must be robust to a variety of perceptual confounds, and noise sources not seen during training. In this letter, we present a method to jointly learn a latent state representation, and the associated dynamics that is amenable for long-term planning, and closed-loop control under perceptually difficult conditions. As our main contribution, we describe how our representation is able to capture a notion of heteroscedastic or input-specific uncertainty at test time by detecting novel or out-of-distribution (OOD) inputs. We present results from prediction, and control experiments on two image-based tasks: a simulated pendulum balancing task, and a real-world robotic manipulator reaching task. We demonstrate that our model produces significantly more accurate predictions, and exhibits improved control performance, compared to a model that assumes homoscedastic uncertainty only, in the presence of varying degrees of input degradation.

中文翻译:

鲁棒生成潜在动力学的异方差不确定性

从一系列高维观察中学习或识别动态是许多领域的一项艰巨挑战,包括强化学习和控制。最近通过潜在动力学从生成的角度研究了这个问题:高维观察被嵌入到可以学习动力学的低维空间中。尽管取得了一些成功,但潜在动力学模型尚未应用于现实世界的机器人系统,在这些系统中,学习到的表示必须对各种感知混淆和训练过程中看不到的噪声源具有鲁棒性。在这封信中,我们提出了一种联合学习潜在状态表示的方法,以及适用于长期规划的相关动力学,以及感知困难条件下的闭环控制。作为我们的主要贡献,我们描述了我们的表示如何通过检测新的或分布外 (OOD) 输入来在测试时捕获异方差或特定于输入的不确定性的概念。我们展示了两个基于图像的任务的预测和控制实验的结果:模拟摆平衡任务和真实世界的机器人机械手到达任务。我们证明,在存在不同程度的输入退化的情况下,与仅假设同方差不确定性的模型相比,我们的模型产生了更准确的预测,并展示了改进的控制性能。模拟摆平衡任务和真实世界的机器人机械手到达任务。我们证明,在存在不同程度的输入退化的情况下,与仅假设同方差不确定性的模型相比,我们的模型产生了更准确的预测,并展示了改进的控制性能。模拟摆平衡任务和真实世界的机器人机械手到达任务。我们证明,在存在不同程度的输入退化的情况下,与仅假设同方差不确定性的模型相比,我们的模型产生了更准确的预测,并展示了改进的控制性能。
更新日期:2020-10-01
down
wechat
bug