Long user history is highly valuable signal for recommendation systems, but effectively incorporating it often comes with high cost in terms of data center power consumption and GPU. In this work, we chose offline embedding over end-to-end sequence length optimization methods to enable extremely long user sequence modeling as a cost-effective solution, and propose a new user embedding learning strategy, multi-slicing and summarization, that generates highly generalizable user representation of user's long-term stable interest. History length we encoded in this embedding is up to 70,000 and on average 40,000. This embedding, named as DV365, is proven highly incremental on top of advanced attentive user sequence models deployed in Instagram. Produced by a single upstream foundational model, it is launched in 15 different models across Instagram and Threads with significant impact, and has been production battle-proven for >1 year since our first launch.

Human Robot collaborative task achievement requires adapted tools and algorithms for both decision making and motion computation. The human presence as well as its behavior must be considered and actively monitored at the decisional level for the robot to produce synchronized and adapted behavior. Additionally, having a human within the robot range of action introduces security constraints as well as comfort considerations which must be taken into account at the motion planning and control level. This paper presents a robotic architecture adapted to human robot interaction and focuses on two tools: a human aware manipulation planner and a supervision system dedicated to collaborative task achievement.

Human-robot collaborative task achievement requires the robot to reason not only about its current beliefs but also about the ones of its human partner. In this paper, we introduce a framework to manage shared knowledge for a robotic system dedicated to interactive task achievement with a human. In a first part, we define which beliefs should be taken into account ; we then explain a manner to achieve them using communication schemes. Several examples are presented to illustrate the purpose of beliefs management including a real experiment demonstrating a "give object" task between the Jido robotic platform and a human.

This paper addresses high-level robot planning issues for an interactive cognitive robot that has to act in presence or in collaboration with a human partner. We describe a task planner called HATP (for human aware task planner). HATP is especially designed to handle a set of human-centered constraints in order to provide "socially acceptable" plans that are oriented toward collaborative task achievement. We provide an overall description of HATP and discuss its main structure and algorithmic features.