When robots interact with humans in homes, roads, or factories the human's behavior often changes in response to the robot. Non-stationary humans are challenging for robot learners: actions the robot has learned to coordinate with the original human may fail after the human adapts to the robot. In this paper we introduce an algorithmic formalism that enables robots (i.e., ego agents) to co-adapt alongside dynamic humans (i.e., other agents) using only the robot's low-level states, actions, and rewards. A core challenge is that humans not only react to the robot's behavior, but the way in which humans react inevitably changes both over time and between users. To deal with this challenge, our insight is that -- instead of building an exact model of the human -- robots can learn and reason over high-level representations of the human's policy and policy dynamics. Applying this insight we develop RILI: Robustly Influencing Latent Intent. RILI first embeds low-level robot observations into predictions of the human's latent strategy and strategy dynamics. Next, RILI harnesses these predictions to select actions that influence the adaptive human towards advantageous, high reward behaviors over repeated interactions. We demonstrate that -- given RILI's measured performance with users sampled from an underlying distribution -- we can probabilistically bound RILI's expected performance across new humans sampled from the same distribution. Our simulated experiments compare RILI to state-of-the-art representation and reinforcement learning baselines, and show that RILI better learns to coordinate with imperfect, noisy, and time-varying agents. Finally, we conduct two user studies where RILI co-adapts alongside actual humans in a game of tag and a tower-building task. See videos of our user studies here: https://youtu.be/WYGO5amDXbQ
LEGO: Latent-space Exploration for Geometry-aware Optimization of Humanoid Kinematic Design
Department of Artificial Intelligence, Korea University（高丽大学人工智能系）；Contoro Robotics；Department of Mechanical Engineering, Massachusetts Institute of Technology（麻省理工学院机械工程系）；Department of Statistics, Korea University（高丽大学统计系）
LeVERB: Humanoid Whole-Body Control with Latent Vision-Language Instruction
University of California Berkeley（加州大学伯克利分校）；Norwegian University of Science and Technology（挪威科学与技术大学）；Simon Fraser University（西蒙弗雷泽大学）；Carnegie Mellon University（卡内基梅隆大学）
LAPT-Scale: Scaling Embodied Policies with Latent Action Pre-Training on Human Videos
Joy Future Academy, JD（京东探索研究院）；School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术学院）；Tsinghua SIGS, Tsinghua University（清华大学深圳国际研究生院）；Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能学院）
LaST-HD: Learning Latent Physical Reasoning from Scalable Human Data for Robot Manipulation
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室）；The Chinese University of Hong Kong（香港中文大学）；Simplexity Robotics；Aether Tech
Latent Representation in Human-Robot Interaction with Explicit Consideration of Periodic Dynamics
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Keio University（庆应义塾大学）；National Institute of Informatics（国立信息学研究所）；The Graduate University for Advanced Studies (SOKENDAI)（综合研究大学院大学）
ConLA: Contrastive Latent Action Learning from Human Videos for Robotic Manipulation
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；State Key Laboratory of Mobile Network and Mobile Multimedia Technology, Shenzhen（移动网络与移动多媒体技术国家重点实验室（深圳））；ZTE Corporation（中兴通讯）；Shanghai Jiao Tong University（上海交通大学）；Central South University（中南大学）
From Language to Locomotion: Retargeting-free Humanoid Control via Motion Latent Guidance
University of Sydney（悉尼大学）；BAAI（北京人工智能研究院）；Harbin Institute of Technology（哈尔滨工业大学）；Hong Kong University of Science and Technology（香港科学与技术大学）；Shanghai Jiao Tong University（上海交通大学）；Peking University（北京大学）
