95 citations · 184 across the 32 of their papers we have counts for
6 papers · 1 filter
Effect of Optimizer, Initializer, and Architecture of Hypernetworks on Continual Learning from Demonstration
Sayantan Auddy, Sebastian Bergner, Justus Piater
In continual learning from demonstration (CLfD), a robot learns a sequence of real-world motion skills continually from human demonstrations. Recently, hypernetworks have been succ…
Regularity as Intrinsic Reward for Free Play
Cansu Sancaktar, Justus Piater, Georg Martius
We propose regularity as a novel reward signal for intrinsically-motivated reinforcement learning. Taking inspiration from child development, we postulate that striving for structu…
Colored Noise in PPO: Improved Exploration and Performance through Correlated Action Sampling
Jakob Hollenstein, Georg Martius, Justus Piater
Proximal Policy Optimization (PPO), a popular on-policy deep reinforcement learning method, employs a stochastic policy for exploration. In this paper, we propose a colored noise-b…
Constrained Equation Learner Networks for Precision-Preserving Extrapolation of Robotic Skills
Hector Perez-Villeda, Justus Piater, Matteo Saveriano
In Programming by Demonstration, the robot learns novel skills from human demonstrations. After learning, the robot should be able not only to reproduce the skill, but also to gene…
Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Model
Sayantan Auddy, Jakob Hollenstein, Matteo Saveriano +2
Robots capable of learning from demonstration (LfD) must exhibit stability while executing learned motion skills. To be effective in the real world, they should also remember multi…
Differentiable Forward Kinematics for TensorFlow 2
Lukas Mölschl, Jakob J. Hollenstein, Justus Piater
Robotic systems are often complex and depend on the integration of a large number of software components. One important component in robotic systems provides the calculation of for…