447 citations · 905 across the 32 of their papers we have counts for
5 papers · 1 filter
Hybrid Residual Reinforcement Learning for Contact-Rich Robotic Book Insertion
Tianyuan Liu, Rutherford Agbeshi Patamia, Benjamin Champion +2
Placing a grasped book into a tight shelf is a compact but difficult contact-rich control problem: millimetre-scale pose error can turn a geometrically valid approach into jamming,…
Execution-Aware Pre-Execution Ranking for Grasp-Conditioned Robotic Placement
Tianyuan Liu, Rutherford Agbeshi Patamia, Benjamin Champion +2
A geometrically valid placement can still be difficult to execute because the selected grasp changes the required end-effector pose, collision geometry, and transport motion. Place…
OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning
Emil Mittag, Richard Dazeley, Peter Vamplew
Reliable reinforcement learning (RL) agents must maintain operational integrity amidst sensor malfunctions, dynamic disturbances, and slow environmental shifts. The detection of ou…
Memory Merge DQN: Sensitivity Weighted Target Updates for Stable Value Learning
Adrian Ly, Richard Dazeley, Peter Vamplew +2
Deep Q-networks use target networks to stabilise bootstrapped value learning, but the standard hard copy update also introduces a tradeoff. Holding the target network fixed, improv…
Learning the Value Systems of Societies with Preference-based Multi-objective Reinforcement Learning
Andrés Holgado-Sánchez, Peter Vamplew, Richard Dazeley +2
Value-aware AI should recognise human values and adapt to the value systems (value-based preferences) of different users. This requires operationalization of values, which can be p…