Reinforcement Learning with Lie Group Orientations for Robotics
Handling orientations of robots and objects is a crucial aspect of many applications. Yet, ever so often, there is a lack of mathematical correctness when dealing with orientations, especially in learning pipelines involving, for example, artificial neural networks. In this paper, we investigate reinforcement learning with orientations and propose a simple modification of the network's input and output that adheres to the Lie group structure of orientations. As a result, we obtain an easy and efficient implementation that is directly usable with existing learning libraries and achieves significantly better performance than other common orientation representations. We briefly introduce Lie theory specifically for orientations in robotics to motivate and outline our approach. Subsequently, a thorough empirical evaluation of different combinations of orientation representations for states and actions demonstrates the superior performance of our proposed approach in different scenarios, including: direct orientation control, end effector orientation control, and pick-and-place tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
reinforcement-learningReinforcement LearningSimilar Papers 제목 키워드 기반
Learning and Optimization with 3D Orientations
There exist numerous ways of representing 3D orientations. Each representation has both limitations and unique features. Choosing the best representation for one task is often a difficult chore, and there exist conflicti…
Reinforcement LearningLearning Stable Vector Fields on Lie Groups
Learning robot motions from demonstration requires models able to specify vector fields for the full robot pose when the task is defined in operational space. Recent advances in reactive motion generation have shown that…
Motion GenerationSuper-Fibonacci Spirals: Fast, Low-Discrepancy Sampling of SO(3)
Super-Fibonacci spirals are an extension of Fibonacci spirals, enabling fast generation of an arbitrary but fixed number of 3D orientations. The algorithm is simple and fast. A comprehensive evaluation comparing to o…
Learning Rotation-Equivariant Features for Visual Correspondence
Extracting discriminative local features that are invariant to imaging variations is an integral part of establishing correspondences between images. In this work, we introduce a self-supervised learning framework to ext…
Camera Pose EstimationPose EstimationSelf-Supervised LearningReinforcement Learning Approaches in Social Robotics
This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to di…
Decision Makingreinforcement-learningReinforcement LearningReinforcement Learning (RL)