95 citations · 184 across the 32 of their papers we have counts for
33 papers
Imitation Learning-based Direct Visual Servoing using the Large Projection Formulation
Sayantan Auddy, Antonio Paolillo, Justus Piater +1
Today robots must be safe, versatile, and user-friendly to operate in unstructured and human-populated environments. Dynamical system-based imitation learning enables robots to per…
Unsupervised Learning of Effective Actions in Robotics
Marko Zaric, Jakob Hollenstein, Justus Piater +1
Learning actions that are relevant to decision-making and can be executed effectively is a key problem in autonomous robotics. Current state-of-the-art action representations in ro…
Continual Domain Randomization
Josip Josifovski, Sayantan Auddy, Mohammadhossein Malmir +3
Domain Randomization (DR) is commonly used for sim2real transfer of reinforcement learning (RL) policies in robotics. Most DR approaches require a simulator with a fixed set of tun…
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…