38 citations · 88 across the 7 of their papers we have counts for
7 papers
Adaptive Rollout Truncation Based on Epistemic Uncertainty for Efficient Offline World Model Training
Nikodem Sebastian Zymla, Laurin Thiele, Johannes Pitz
Accurate neural world models are central to model-based robotics, where they enable robots to predict future states from previously observed trajectories. Multi-step autoregressive…
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…
Learning Purely Tactile In-Hand Manipulation with a Torque-Controlled Hand
Leon Sievers, Johannes Pitz, Berthold Bäuml
We show that a purely tactile dextrous in-hand manipulation task with continuous regrasping, requiring permanent force closure, can be learned from scratch and executed robustly on…