1 citations · 3 across the 5 of their papers we have counts for
5 papers
Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data
Yi Zhao, Aidan Scannell, Wenshuai Zhao +7
Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL). This paper expands the pool of usable data for offline-to-online…
Data-Efficient Online Learning of Ball Placement in Robot Table Tennis
Philip Tobuschat, Hao Ma, Dieter Büchler +2
We present an implementation of an online optimization algorithm for hitting a predefined target when returning ping-pong balls with a table tennis robot. The online algorithm opti…
Safe & Accurate at Speed with Tendons: A Robot Arm for Exploring Dynamic Motion
Simon Guist, Jan Schneider, Hao Ma +9
Operating robots precisely and at high speeds has been a long-standing goal of robotics research. Balancing these competing demands is key to enabling the seamless collaboration of…
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
Vaisakh Shaj, Dieter Buchler, Rohit Sonker +2
Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system identification. However, these models assume that the dynamic…
Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning
Vaisakh Shaj, Philipp Becker, Dieter Buchler +5
Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for sophisticated robots such as robots driven by hydraulics, artificial muscl…