30.07.202014:45-15:15Rustam Galljamov, M.Sc. Intermediate Presentation: Sample-efficient learning-based controller for bipedal walking in robotic systems
30.07.202014:00-14:45Takayuki Osa (Kyushu U), Invited Talk: Motion Planning by Learning Latent Representations
28.07.202011:00-12:00Max Welling (Uni Amsterdam), Invited Talk: Graph Nets and Equivariance: The Next Generation
23.07.202014:00-14:30Eike Mentzendorff, M.Sc. Thesis Defense: Multi-Objective Deep Reinforcement Learning through Manifold Optimization
16.07.202014:00-14:30Johannes Silberbauer, M.Sc. Thesis Defense: A Differentiable Newton Euler Algorithm for Multi-body Model Learning
15.07.202014:00-16:00IP Intermediate Presentation's Zoom Meeting 0alFVaGIwQT09
9.07.202014:00-14:45Jens Kober (TU Delft), Invited Talk: Robots Learning [Through] Interactions
2.07.202014:00-14:30Claas Voelcker, Honor Thesis Defense: Sequential Monte Carlo Input Inference for Control
25.06.202014:45-15:15Vini Marconie Tengang, M.Sc. Thesis Defense: 3D Pose Estimation for Robot Mikado
25.06.202014:00-14:45Philip Becker-Ehmck, Research Talk: Deep Onboard Quadcopter Control via Model-Based Reinforcement Learning
18.06.202017:45-18:30Simon Guist, Research Talk: Hindsight State Manipulation
18.06.202017:00-17:45Roberto Calandra (Facebook), Invited Talk: Rethinking Model-based Reinforcement Learning
18.06.202016:00-17:00Yijiang Huang (MIT), Invited Talk: Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusion
11.06.202015:30-16:00Qin Li, Research Talk: Human Motion Prediction Based on Graph Convolutional Network
11.06.202015:00-15:30Stephane Tekam, M.Sc. Thesis defense: Policy Optimization via Gaussian Mixture Model
11.06.202014:00-15:00Herke van Hoof (UVA), Invited Talk: Learning heuristics for combinatorial optimization
4.06.202014:00-14:30Yang Weng (U. Tokyo), Invited Talk: Reinforcement Learning Based Underwater Wireless Optical Communication Alignment for Multiple Autonomous Underwater Vehicles
28.05.202014:00-14:45Christoph Zimmer (BCAI), Invited Talk: Safe active learning for time series modeling with gaussian processes


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