works on

From the 1 of 7 linked papers with an AI index.

collaborators

7 papers

cs.RO2026

It's Not Just More Demos: Counterfactual Action Sensitivity Coverage for Data-Efficient Robust Robot Imitation

Giovanni D'urso, Kaushik Roy, Nicholas Lawrance +1

The paper introduces Counterfactual Nuisance Behaviour Cloning (CFNBC), an offline method that selects a small set of targeted demonstration data to improve the robustness of visuo…

cs.RO2026

AnchorVLA: Anchored Diffusion for Efficient End-to-End Mobile Manipulation

Jia Syuen Lim, Zhizhen Zhang, Peter Bohm +3

A central challenge in mobile manipulation is preserving multiple plausible action models while remaining reactive during execution. A bottle in a cluttered scene can often be appr…

cs.LG2026

SPREAD: Subspace Representation Distillation for Lifelong Imitation Learning

Kaushik Roy, Giovanni D'urso, Nicholas Lawrance +2

A key challenge in lifelong imitation learning (LIL) is enabling agents to acquire new skills from expert demonstrations while retaining prior knowledge. This requires preserving t…

cs.RO2026

RAPT: Model-Predictive Out-of-Distribution Detection and Failure Diagnosis for Sim-to-Real Humanoid Deployment

Humphrey Munn, Brendan Tidd, Peter Bohm +2

Deploying learned control policies is risky because policies that appear robust in simulation can confidently enter out-of-distribution (OOD) states after Sim-to-Real transfer, cau…

cs.RO2025

Scalable Multi-Objective Robot Reinforcement Learning through Gradient Conflict Resolution

Humphrey Munn, Brendan Tidd, Peter Böhm +2

Reinforcement Learning (RL) robot controllers usually aggregate many task objectives into one scalar reward. While large-scale proximal policy optimisation (PPO) has enabled impres…

cs.RO2025

Improving Generalization Ability of Robotic Imitation Learning by Resolving Causal Confusion in Observations

Yifei Chen, Yuzhe Zhang, Giovanni D'urso +2

Recent developments in imitation learning have considerably advanced robotic manipulation. However, current techniques in imitation learning can suffer from poor generalization, li…