In this paper, we present a comprehensive dataset comprising 37.9 hours of sensor data collected from humanoid robots, including 18.3 hours of walking and 2,519 recorded falls. This extensive dataset is a valuable resource for various robotics and machine learning applications. Leveraging this data, we propose RePro-TCN, a Temporal Convolutional Network (TCN) enhanced with two novel extensions: Relaxed Loss Formulation and Progressive Forecasting. Predicting falls is a critical capability in humanoid robotics for implementing countermeasures such as lunging or stopping the walk. Thanks to the new dataset, we train RePro-TCN and demonstrate its superiority over previous approaches under real-world conditions that were previously unattainable.

Multi-robot navigation and dynamic obstacle avoidance are challenging problems in robot learning. Recent advancements in Deep Reinforcement Learning (DRL) have demonstrated great potential in this area. Nonetheless, they often face challenges related to low sample efficiency. To overcome this challenge, some research proposes simulators that incorporate hardware acceleration. Although these simulators improve efficiency, they often lack the flexibility to generate diverse learning scenarios as often needed in multi-robot scenarios, where the different environments have varying numbers of agents.In this paper, we introduce MuRoSim, a multi-robot simulation for lidar-based navigation specifically designed for DRL applications. Due to its high level of abstraction, complete implementation in C++, and rigorous thread pool utilization, MuRoSim achieves high computational performance. We apply MuRoSim for training navigation policies for omnidirectional mobile robots equipped with lidar sensors using DRL. Finally, we conduct extensive Sim-to-Real experiments to confirm the realism of the simulator, by deploying the learned policy for dynamic navigation with up to six robots in numerous of real- world experiments.

This paper introduces evoBOT, a novel robot platform for research on highly dynamic locomotion and human-machine interaction. evoBOT is capable of performing complex tasks such as handovers or manipulation while moving at high speeds. We provide an overview of the robot's core features and the underlying design decisions on both the mechanical and the electronic level. Moreover, we propose a reinforcement learning (RL) based control approach for training highly dynamic motions that is evaluated on a first set of robotic tasks, including robust balancing and dynamic locomotion. Lastly, we conduct extensive benchmarking on the adopted sim-to-real methods and present an initial sim-to-real pipeline for first transfer of the trained policies to the real robot. To accelerate robotics research in this direction, the full simulation model of the robot is released as open-source.