Pavlichenko, Dmytro
RoboCup 2023 Humanoid AdultSize Winner NimbRo: NimbRoNet3 Visual Perception and Responsive Gait with Waveform In-walk Kicks
Pavlichenko, Dmytro, Ficht, Grzegorz, Villar-Corrales, Angel, Denninger, Luis, Brocker, Julia, Sinen, Tim, Schreiber, Michael, Behnke, Sven
The RoboCup Humanoid League holds annual soccer robot world championships towards the long-term objective of winning against the FIFA world champions by 2050. The participating teams continuously improve their systems. This paper presents the upgrades to our humanoid soccer system, leading our team NimbRo to win the Soccer Tournament in the Humanoid AdultSize League at RoboCup 2023 in Bordeaux, France. The mentioned upgrades consist of: an updated model architecture for visual perception, extended fused angles feedback mechanisms and an additional COM-ZMP controller for walking robustness, and parametric in-walk kicks through waveforms.
Deep Reinforcement Learning of Dexterous Pre-grasp Manipulation for Human-like Functional Categorical Grasping
Pavlichenko, Dmytro, Behnke, Sven
Abstract-- Many objects such as tools and household items can be used only if grasped in a very specific way--grasped functionally. Often, a direct functional grasp is not possible, though. We propose a method for learning a dexterous pregrasp manipulation policy to achieve human-like functional grasps using deep reinforcement learning. We introduce a dense multi-component reward function that enables learning a single policy, capable of dexterous pre-grasp manipulation of novel instances of several known object categories with an anthropomorphic hand. The policy is learned purely by means of reinforcement learning from scratch, without any expert demonstrations, and implicitly learns to reposition and reorient objects of complex shapes to achieve given functional grasps. Learning is done on a single GPU in less than three hours.
RoboCup 2022 AdultSize Winner NimbRo: Upgraded Perception, Capture Steps Gait and Phase-based In-walk Kicks
Pavlichenko, Dmytro, Ficht, Grzegorz, Amini, Arash, Hosseini, Mojtaba, Memmesheimer, Raphael, Villar-Corrales, Angel, Schulz, Stefan M., Missura, Marcell, Bennewitz, Maren, Behnke, Sven
Beating the human world champions by 2050 is an ambitious goal of the Humanoid League that provides a strong incentive for RoboCup teams to further improve and develop their systems. In this paper, we present upgrades of our system which enabled our team NimbRo to win the Soccer Tournament, the Drop-in Games, and the Technical Challenges in the Humanoid AdultSize League of RoboCup 2022. Strong performance in these competitions resulted in the Best Humanoid award in the Humanoid League. The mentioned upgrades include: hardware upgrade of the vision module, balanced walking with Capture Steps, and the introduction of phase-based in-walk kicks.