Adaptive t-Momentum-based Optimization for Unknown Ratio of Outliers in Amateur Data in Imitation Learning
Behavioral cloning (BC) bears a high potential for safe and direct transfer of human skills to robots. However, demonstrations performed by human operators often contain noise or imperfect behaviors that can affect the efficiency of the imitator if left unchecked. In order to allow the imitators to effectively learn from imperfect demonstrations, we propose to employ the robust t-momentum optimization algorithm. This algorithm builds on the Student's t-distribution in order to deal with heavy-tailed data and reduce the effect of outlying observations. We extend the t-momentum algorithm to allow for an adaptive and automatic robustness and show empirically how the algorithm can be used to produce robust BC imitators against datasets with unknown heaviness. Indeed, the imitators trained with the t-momentum-based Adam optimizers displayed robustness to imperfect demonstrations on two different manipulation tasks with different robots and revealed the capability to take advantage of the additional data while reducing the adverse effect of non-optimal behaviors.
Code (0)
등록된 구현이 없습니다.
Tasks
Imitation LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Adaptive Optimization via Momentum on Variance-Normalized Gradients
We introduce MVN-Grad (Momentum on Variance-Normalized Gradients), an Adam-style optimizer that improves stability and performance by combining two complementary ideas: variance-based normalization and momentum applied a…
Image ClassificationOptimal Adaptive and Accelerated Stochastic Gradient Descent
Stochastic gradient descent (\textsc{Sgd}) methods are the most powerful optimization tools in training machine learning and deep learning models. Moreover, acceleration (a.k.a. momentum) methods and diagonal scaling (a.…
BIG-bench Machine LearningStochastic OptimizationTraining Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization
Heavy ball momentum is crucial in accelerating (stochastic) gradient-based optimization algorithms for machine learning. Existing heavy ball momentum is usually weighted by a uniform hyperparameter, which relies on exces…
BIG-bench Machine Learningimage-classificationImage ClassificationLanguage Modeling+3Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style Adaptive Momentum
Momentum plays a crucial role in stochastic gradient-based optimization algorithms for accelerating or improving training deep neural networks (DNNs). In deep learning practice, the momentum is usually weighted by a well…
Adversarial RobustnessAdaptive Momentum Coefficient for Neural Network Optimization
We propose a novel and efficient momentum-based first-order algorithm for optimizing neural networks which uses an adaptive coefficient for the momentum term. Our algorithm, called Adaptive Momentum Coefficient (AMoC), u…