4 papers
Can Context Bridge the Reality Gap? Sim-to-Real Transfer of Context-Aware Policies
Marco Iannotta, Yuxuan Yang, Johannes A. Stork +2
Sim-to-real transfer remains a major challenge in reinforcement learning (RL) for robotics, as policies trained in simulation often fail to generalize to the real world due to disc…
Inverse Optimization Latent Variable Models for Learning Costs Applied to Route Problems
Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork
Learning representations for solutions of constrained optimization problems (COPs) with unknown cost functions is challenging, as models like (Variational) Autoencoders struggle to…
DFW: A Novel Weighting Scheme for Covariate Balancing and Treatment Effect Estimation
Ahmad Saeed Khan, Erik Schaffernicht, Johannes Andreas Stork
Estimating causal effects from observational data is challenging due to selection bias, which leads to imbalanced covariate distributions across treatment groups. Propensity score-…
ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learning
Rahel Rickenbach, Alan A. Lahoud, Erik Schaffernicht +2
The computational burden of model predictive control (MPC) limits its application on real-time systems, such as robots, and often requires the use of short prediction horizons. Thi…