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![ ](./photos/emre.png width="210px" align=left) [Emre Ugur](https://colors.cmpe.boun.edu.tr/) is an Associate Professor in Dept. of Computer Engineering, Bogazici University, the chair of the Cognitive Science MA Program, the vice-chair of the Dept. of Computer Engineering, and the head of the Cognition, Learning and Robotics (CoLoRs) lab. He received his BS, MSc, and Ph.D. degrees in Computer Engineering from Middle East Technical University (METU, Turkey). He was a research assistant in KOVAN Lab. METU (2003-2009); worked as a research scientist at ATR, Japan (2009-2013); visited Osaka University as a specially appointed Assist.&Assoc. Professor (2015 & 2016); and worked as a senior researcher at the University of Innsbruck (2013-2016). He is interested in robotics, robot learning, and cognitive robotics. **Title:** Safe robot learning and control with Conditional Neural Processes **Abstract:** The robots learning and acting in environment interactions need to take into account the physical and social constraints of the environments and the tasks. In the first half of this talk, I will present our work ACNMP, namely Adaptive Conditional Neural Movement Primitives, that allows safe and efficient policy improvement in novel environments. Following a learning from demonstration phase, our model enables policy improvement by simultaneous training of our model with supervised learning (SL) Reinforcement Learning (RL). ACNMP enables the system to safely extrapolate to situations where pure LfD and RL fail. In the second part of my talk, I will present our learning social navigation framework, which learns global and local social controllers of the mobile robot from observations. We leverage a state-of-the-art, deep prediction mechanism to detect situations not similar to the trained ones, where reactive controllers step in to ensure safe navigation.