TY  - GEN
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
ID  - discovery10197138
AV  - public
EP  - 7500
N2  - Current reinforcement learning algorithms struggle in sparse and complex environments, most notably in long-horizon manipulation tasks entailing a plethora of different sequences. In this work, we propose the Intrinsically Guided Exploration from Large Language Models (IGE-LLMs) framework. By leveraging LLMs as an assistive intrinsic reward, IGE-LLMs guides the exploratory process in reinforcement learning to address intricate long-horizon with sparse rewards robotic manipulation tasks. We evaluate our framework and related intrinsic learning methods in an environment challenged with exploration, and a complex robotic manipulation task challenged by both exploration and long-horizons. Results show IGE-LLMs (i) exhibit notably higher performance over related intrinsic methods and the direct use of LLMs in decision-making, (ii) can be combined and complement existing learning methods highlighting its modularity, (iii) are fairly insensitive to different intrinsic scaling parameters, and (iv) maintain robustness against increased levels of uncertainty and horizons.
PB  - Institute of Electrical and Electronics Engineers (IEEE)
A1  - Triantafyllidis, E
A1  - Christianos, F
A1  - Li, Z
Y1  - 2024/08/08/
UR  - http://dx.doi.org/10.1109/icra57147.2024.10611483
SP  - 7493
TI  - Intrinsic Language-Guided Exploration for Complex Long-Horizon Robotic Manipulation Tasks
ER  -