4 papers
Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics
Leonard Hinckeldey, Elliot Fosong, Rimvydas Rubavicius +6
As embodied autonomous systems capable of assisting humans in daily activities remain a major goal for robotics, efficient and appropriate reinforcement learning (RL) simulation te…
LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots
Dongge Han, Trevor McInroe, Adam Jelley +3
Large language models (LLMs) have shown significant potential for robotics applications, particularly task planning, by harnessing their language comprehension and text generation…
Multi-view Disentanglement for Reinforcement Learning with Multiple Cameras
Mhairi Dunion, Stefano V. Albrecht
The performance of image-based Reinforcement Learning (RL) agents can vary depending on the position of the camera used to capture the images. Training on multiple cameras simultan…
Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning
Trevor McInroe, Adam Jelley, Stefano V. Albrecht +1
Offline pretraining with a static dataset followed by online fine-tuning (offline-to-online, or OtO) is a paradigm well matched to a real-world RL deployment process. In this scena…