Value Refinement Network (VRN)

no code implementations • 29 Sep 2021 • Jan Wöhlke, Felix Schmitt, Herke van Hoof

Combining the benefits of planning and learning values, we propose the Value Refinement Network (VRN), an architecture that locally refines a plan in a (simpler) state space abstraction, represented by a pre-computed value function, with respect to the full agent state.

Q-Learning Reinforcement Learning (RL)

Hierarchies of Planning and Reinforcement Learning for Robot Navigation

no code implementations • 23 Sep 2021 • Jan Wöhlke, Felix Schmitt, Herke van Hoof

In simulated robotic navigation tasks, VI-RL results in consistent strong improvement over vanilla RL, is on par with vanilla hierarchal RL on single layouts but more broadly applicable to multiple layouts, and is on par with trainable HL path planning baselines except for a parking task with difficult non-holonomic dynamics where it shows marked improvements.

reinforcement-learning Reinforcement Learning (RL) +1

Model-based Hand Pose Estimation for Generalized Hand Shape with Appearance Normalization

no code implementations • 2 Jul 2018 • Jan Wöhlke, Shile Li, Dongheui Lee

In this work, we extend the kinematic layer to make the hand shape parameters learnable.

Hand Pose Estimation