We propose a novel method for exploring the dynamics of physically based animated characters, and learning a task-agnostic action space that makes movement optimization easier. Like several previous papers, we parameterize actions as target states, and learn a short-horizon goal-conditioned low-level control policy that drives the agent's state towards the targets. Our novel contribution is that with our exploration data, we are able to learn the low-level policy in a generic manner and without any reference movement data. Trained once for each agent or simulation environment, the policy improves the efficiency of optimizing both trajectories and high-level policies across multiple tasks and optimization algorithms. We also contribute novel visualizations that show how using target states as actions makes optimized trajectories more robust to disturbances; this manifests as wider optima that are easy to find. Due to its simplicity and generality, our proposed approach should provide a building block that can improve a large variety of movement optimization methods and applications.
AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation
Dept. of Comp. Sci. and Tech.（计算机科学与技术系）；Institute for AI（人工智能研究院）；BNRist Center（BNRist中心）；THBI Lab（THBI实验室）；Tsinghua-Bosch Joint ML Center（清华大学-博世联合机器学习中心）；Tsinghua University（清华大学）
Action-Agnostic Point-Level Supervision for Temporal Action Detection
Visual Intelligence Research Laboratories, NEC Corporation（日本电气公司视觉智能研究所）；Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究科）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能研究中心）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究科）
Actor-agnostic Multi-label Action Recognition with Multi-modal Query
University of Surrey（萨里大学）；CVSSP（视觉、语音与信号处理中心）；Surrey Institute for People-Centred AI（萨里以人为本人工智能研究所）；iFlyTek-Surrey Joint Research Center on AI（科大讯飞-萨里人工智能联合研究中心）；School of Veterinary Medicine（兽医学院）
Model-agnostic Adversarial Attack and Defense for Vision-Language-Action Models
The University of Auckland（奥克兰大学）；King Abdullah University of Science and Technology（国王 Abdullah 科学与技术大学）；Tokyo University of Science（东京科学大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）
Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives
Idiap Research Institute（Idiap研究所）；Istituto Italiano di Tecnologia（意大利技术研究院）；Aalto University（阿尔托大学）；Bosch Center for Artificial Intelligence（博世人工智能中心）
Imitation and Adaptation Based on Consistency: A Quadruped Robot Imitates Animals from Videos Using Deep Reinforcement Learning
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人与智能制造创新研究院）；Westlake University（西湖大学）；University of Chinese Academy of Sciences（中国科学院大学）
Adversary Agnostic Robust Deep Reinforcement Learning
Nanyang Technological University（南洋理工大学）；Singapore Institute of Manufacturing Technology（新加坡制造技术研究院）；Agency for Science, Technology and Research（新加坡科技研究局）；Macquarie University（麦考瑞大学）
Physical Action Categorization using Signal Analysis and Machine Learning
College of E&ME, National University of Sciences and Technology（国立科技大学电气与机械工程学院）；Princess Nourah bint Abdulrahman University（努拉·宾特·阿卜杜勒拉赫曼公主大学）；National University of Modern Sciences（国立现代科学大学）
