Generalization capabilities, or rather a lack thereof, is one of the most important unsolved problems in the field of robot learning, and while several large scale efforts have set out to tackle this problem, unsolved it remains. In this paper, we hypothesize that learning temporal action abstractions using latent variable models (LVMs), which learn to map data to a compressed latent space and back, is a promising direction towards low-level skills that can readily be used for new tasks. Although several works have attempted to show this, they have generally been limited by architectures that do not faithfully capture shareable representations. To address this we present Quantized Skill Transformer (QueST), which learns a larger and more flexible latent encoding that is more capable of modeling the breadth of low-level skills necessary for a variety of tasks. To make use of this extra flexibility, QueST imparts causal inductive bias from the action sequence data into the latent space, leading to more semantically useful and transferable representations. We compare to state-of-the-art imitation learning and LVM baselines and see that QueST's architecture leads to strong performance on several multitask and few-shot learning benchmarks. Further results and videos are available at https://quest-model.github.io/
Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning
Intelligent Autonomous Systems, TU Darmstadt, Germany（图腾达姆施塔特大学智能自主系统研究所）；German Research Center for AI, Germany（德国人工智能研究中心）；Hessian Centre for Artificial Intelligence, Germany（黑森州人工智能中心）；Honda Research Institute EU, Germany（本田欧洲研究院）
ARP: Enhancing Quantized Skill Abstractions via Visual Alignment and Iterative Refinement for Robotic Manipulation
School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）；Key Laboratory of General Artificial Intelligence and Large Models in Provincial Universities, Soochow University（苏州大学省高校通用人工智能与大模型重点实验室）；College of Mechanical and Electronic Engineering, China Jiliang University（中国计量大学机电工程学院）；College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Leju Robotics（乐聚机器人）
IROSA: Interactive Robot Skill Adaptation using Natural Language
German Aerospace Center (DLR), Institute of Robotics and Mechatronics (RMC)（德国航空航天中心（DLR）机器人与机电研究所）；School of Computation, Information and Technology (CIT), Technical University of Munich (TUM)（计算、信息与技术学院（CIT），慕尼黑技术大学）
Uni-Skill: Building Self-Evolving Skill Repository for Generalizable Robotic Manipulation
Key Laboratory of AI Safety of CAS（中国科学院人工智能安全重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
