33 citations · 50 across the 5 of their papers we have counts for
5 papers · 1 filter
Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Critic
Lennart Röstel, Dominik Winkelbauer, Johannes Pitz +2
In-hand manipulation and grasping are fundamental yet often separately addressed tasks in robotics. For deriving in-hand manipulation policies, reinforcement learning has recently…
Learning Time-Optimal and Speed-Adjustable Tactile In-Hand Manipulation
Johannes Pitz, Lennart Röstel, Leon Sievers +1
In-hand manipulation with multi-fingered hands is a challenging problem that recently became feasible with the advent of deep reinforcement learning methods. While most contributio…
Learning a Shape-Conditioned Agent for Purely Tactile In-Hand Manipulation of Various Objects
Johannes Pitz, Lennart Röstel, Leon Sievers +2
Reorienting diverse objects with a multi-fingered hand is a challenging task. Current methods in robotic in-hand manipulation are either object-specific or require permanent superv…
Estimator-Coupled Reinforcement Learning for Robust Purely Tactile In-Hand Manipulation
Lennart Röstel, Johannes Pitz, Leon Sievers +1
This paper identifies and addresses the problems with naively combining (reinforcement) learning-based controllers and state estimators for robotic in-hand manipulation. Specifical…
Dextrous Tactile In-Hand Manipulation Using a Modular Reinforcement Learning Architecture
Johannes Pitz, Lennart Röstel, Leon Sievers +1
Dextrous in-hand manipulation with a multi-fingered robotic hand is a challenging task, esp. when performed with the hand oriented upside down, demanding permanent force-closure, a…