1 citations · 2 across the 8 of their papers we have counts for
8 papers
Constraint-Aware Reinforcement Learning via Adaptive Action Scaling
Murad Dawood, Usama Ahmed Siddiquie, Shahram Khorshidi +1
Safe reinforcement learning (RL) seeks to mitigate unsafe behaviors that arise from exploration during training by reducing constraint violations while maintaining task performance…
A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks
Murad Dawood, Ahmed Shokry, Maren Bennewitz
Reinforcement learning (RL) has been successfully applied to a variety of robotics applications, where it outperforms classical methods. However, the safety aspect of RL and the tr…
Context-Based Meta Reinforcement Learning for Robust and Adaptable Peg-in-Hole Assembly Tasks
Ahmed Shokry, Walid Gomaa, Tobias Zaenker +5
Autonomous assembly is an essential capability for industrial and service robots, with Peg-in-Hole (PiH) insertion being one of the core tasks. However, PiH assembly in unknown env…
Physically-Consistent Parameter Identification of Robots in Contact
Shahram Khorshidi, Murad Dawood, Benno Nederkorn +2
Accurate inertial parameter identification is crucial for the simulation and control of robots encountering intermittent contact with the environment. Classically, robots' inertial…
Centroidal State Estimation based on the Koopman Embedding for Dynamic Legged Locomotion
Shahram Khorshidi, Murad Dawood, Maren Bennewitz
In this paper, we introduce a novel approach to centroidal state estimation, which plays a crucial role in predictive model-based control strategies for dynamic legged locomotion.…
Safe Multi-Agent Reinforcement Learning for Behavior-Based Cooperative Navigation
Murad Dawood, Sicong Pan, Nils Dengler +3
In this paper, we address the problem of behavior-based cooperative navigation of mobile robots using safe multi-agent reinforcement learning~(MARL). Our work is the first to focus…
Viewpoint Push Planning for Mapping of Unknown Confined Spaces
Nils Dengler, Sicong Pan, Vamsi Kalagaturu +3
Viewpoint planning is an important task in any application where objects or scenes need to be viewed from different angles to achieve sufficient coverage. The mapping of confined s…
Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control
Murad Dawood, Nils Dengler, Jorge de Heuvel +1
Reinforcement learning (RL) has recently proven great success in various domains. Yet, the design of the reward function requires detailed domain expertise and tedious fine-tuning…