.png){kind=icon}
.png){kind=icon}
Return
Generative Adversarial Self-Imitation Learning With Large Language Model Feedback for Robot Control and Navigation
.png){kind=icon}
PRE
AI
K
Z
E
Z
J
J
E
R
B
薛
G
DOI：10.1109/tro.2026.3710412.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Deep reinforcement learning (DRL) has achieved great success in many simulated and real-world robotic tasks. However, the difficulty of designing efficient and dense reward functions makes applying DRL to tackle complex long-horizon and open-world tasks a great challenge. Generative adversarial imitation learning (GAIL) can directly learn policies from the expert trajectories and generalize well in large and complex environments, but relies on high-quality demonstrations and can seldom surpass the performance of the demonstration. Recent work used additional human evaluative feedback to facilitate GAIL to learn faster and surpass the demonstrations, but still requires suboptimal demonstrations. Moreover, it is costly and difficult for human expert to provide relatively high-quality demonstrations and evaluative feedback for various tasks. To address the above issues, in this article, we propose generative adversarial self-imitation learning from demonstration and large language model (LLM) feedback (GASL<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>MF), since LLMs encode rich commonsense knowledge and can perform a variety of reasoning tasks. GASL<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>MF allows a robot to learn from poor demonstrations and gradually replace them with its own good trajectories evaluated by LLM feedback. Our results in four physics-based control tasks and a mobile robot navigation task show that, even with demonstrations of poor performance or not completing the task, GASL<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>MF can learn faster with close to optimal performance, and generalize well to different environments and the real world with sim-to-real adaptation. Further analysis shows that the overall distribution of LLM feedback closely resembles that of human feedback and remains closer to that of ground-truth rewards than human feedback. Finally, our GASL<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>MF method works regardless of the LLM employed, and the LLM feedback from different LLMs remain robust across tasks and even better consistency than human feedback for robot learning in some tasks. These results shed light on the potential of robot imitation learning from even poor or failed demonstrations and broaden its application to a wide range of real-world tasks.
Keywords:
Imitation learning (IL)
inverse reinforcement learning (IRL)
large language model (LLM)
learning from demonstration
mobile robot navigation
Journal
IF:
10.5
Papers:
3.3K
Citations:
2.8W
Organization
Cited Papers