Christopher Mower — accepted AI-conference papers

2026

Trust Region Inverse Reinforcement Learning

Anish Abhijit Diwan, Davide Tateo, Christopher Mower, Haitham Bou Ammar, Jan Peters, Oleg Arenz

ICML 2026poster

Inverse reinforcement learning (IRL) is typically formulated as maximizing entropy subject to matching the distribution of expert trajectories. Classical (dual-ascent) IRL guarantees monotonic performance improvement but requires fully solving an RL problem each iteration to compute dual gradients. …

Cited by 0 Source ↗Scholar ↗

2022

ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction

Christopher Mower, Theodoros Stouraitis, João Moura, Christian Rauch, Lei Yan, Nazanin Zamani Behabadi +4

CoRL 2022poster

Reliable contact simulation plays a key role in the development of (semi-)autonomous robots, especially when dealing with contact-rich manipulation scenarios, an active robotics research topic. Besides simulation, components such as sensing, perception, data collection, robot hardware control, human…

Cited by 22 Source ↗code ↗Scholar ↗