5 papers
GemNav: Discrete-Token Visual Robot Navigation using a Multimodal Large Language Model
Peter Bohm, Saimunur Rahman, Abdelwahed Khamis +3
Visual navigation policies built on large pretrained models have so far followed a common recipe: a dedicated visual encoder, a bespoke action head, and training on thousands of ho…
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…
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…
Low-cost Real-world Implementation of the Swing-up Pendulum for Deep Reinforcement Learning Experiments
Peter Böhm, Pauline Pounds, Archie C. Chapman
Deep reinforcement learning (DRL) has had success in virtual and simulated domains, but due to key differences between simulated and real-world environments, DRL-trained policies h…
Training Directional Locomotion for Quadrupedal Low-Cost Robotic Systems via Deep Reinforcement Learning
Peter Böhm, Archie C. Chapman, Pauline Pounds
In this work we present Deep Reinforcement Learning (DRL) training of directional locomotion for low-cost quadrupedal robots in the real world. In particular, we exploit randomizat…