LLM-based Interactive Imitation Learning for Robotic Manipulation (2504.21769v1)

Abstract: Recent advancements in machine learning provide methods to train autonomous agents capable of handling the increasing complexity of sequential decision-making in robotics. Imitation Learning (IL) is a prominent approach, where agents learn to control robots based on human demonstrations. However, IL commonly suffers from violating the independent and identically distributed (i.i.d) assumption in robotic tasks. Interactive Imitation Learning (IIL) achieves improved performance by allowing agents to learn from interactive feedback from human teachers. Despite these improvements, both approaches come with significant costs due to the necessity of human involvement. Leveraging the emergent capabilities of LLMs in reasoning and generating human-like responses, we introduce LLM-iTeach -- a novel IIL framework that utilizes an LLM as an interactive teacher to enhance agent performance while alleviating the dependence on human resources. Firstly, LLM-iTeach uses a hierarchical prompting strategy that guides the LLM in generating a policy in Python code. Then, with a designed similarity-based feedback mechanism, LLM-iTeach provides corrective and evaluative feedback interactively during the agent's training. We evaluate LLM-iTeach against baseline methods such as Behavior Cloning (BC), an IL method, and CEILing, a state-of-the-art IIL method using a human teacher, on various robotic manipulation tasks. Our results demonstrate that LLM-iTeach surpasses BC in the success rate and achieves or even outscores that of CEILing, highlighting the potential of LLMs as cost-effective, human-like teachers in interactive learning environments. We further demonstrate the method's potential for generalization by evaluating it on additional tasks. The code and prompts are provided at: https://github.com/Tubicor/LLM-iTeach.

• The paper introduces LLM-iTeach, a novel Interactive Imitation Learning framework that uses Large Language Models as interactive teachers for robotic manipulation.
• LLM-iTeach employs hierarchical prompting to generate code-based policies and provides similarity-based corrective or evaluative feedback to guide agent learning.
• Experiments show LLM-iTeach outperforms Behaviour Cloning and rivals human-based methods in simulation, demonstrating a cost-effective alternative without requiring human trainers.

Understanding LLM-based Interactive Imitation Learning for Robotic Manipulation

The paper "LLM-based Interactive Imitation Learning for Robotic Manipulation" addresses a pertinent question in robotic manipulation: How can autonomous agents be trained effectively without incurring substantial human resource costs? While imitation learning (IL) using human demonstrations is a well-established approach, its limitations in handling the i.i.d. assumption in sequential decision-making tasks can impede performance. The authors propose an alternative, a novel Interactive Imitation Learning (IIL) framework using LLMs, called LLM-iTeach, which aims to alleviate human resource dependency by offering emergent reasoning capabilities that mimic human-like feedback.

Proposed Approach

LLM-iTeach offers a innovative methodology in which LLMs serve as interactive teachers. The framework leverages the LLM's ability to generate policy in Python code using a hierarchical prompting strategy. This involves two layers of prompts: first, a planner prompt that breaks down the task into actionable steps, and then prompting the LLM to generate functions that calculate actions and verify step validity. This approach encapsulates reasoning into a CodePolicy, avoiding compounding errors typically associated with IL strategies.

During agent training, LLM-iTeach employs a similarity-based mechanism to provide feedback. Corrective feedback directly influences the agent’s action, overriding it with the teacher's perceived optimal action, whereas evaluative feedback endorses agent actions that align with the teacher’s policy. This bifurcation allows the agent to explore policy space while benefiting from the LLM’s corrective guidance, enhancing overall learning efficiency.

Experimental Validity

The authors tested LLM-iTeach against benchmark methods including Behavior Cloning (BC) and CEILing—a state-of-the-art IIL framework that incorporates human teachers. The experimental results demonstrate that LLM-iTeach surpasses BC consistently and rivals CEILing in performance across various simulated robotic manipulation tasks. Importantly, LLM-iTeach achieves a comparable success rate without human involvement, marking its efficacy as a cost-effective alternative.

When expanded to additional tasks, LLM-iTeach continued to exhibit strong performance, highlighting potential for generalization. The success of LLM-iTeach in completing tasks that demanded long-horizon planning and robust interaction dynamics indicates that hierarchical prompting combined with feedback can facilitate an effective learning trajectory for agents.

Implications and Future Prospects

This framework creates substantial implications for the domain of robotic manipulation and interactive learning. Practically, LLM-iTeach could reduce training costs and streamline model deployment in complex environments where human resources are scarce or expensive to employ. Theoretically, it paves the way for further exploration into LLMs as versatile agents capable of encoding reasoning and learning within code-based policies which interact with hardware agents.

Future research may focus on extending the observation capabilities of LLM teachers, incorporating real-time sensory data from the environment and expanding LLM-iTeach’s applicability to more nuanced real-world tasks. Furthermore, integrating LLMs with multimodal perception models might enhance their situational awareness and refine feedback accuracy. Exploration into more sophisticated action primitives may also unlock novel applications of LLM-iTeach in more complex robotic systems.

In conclusion, LLM-iTeach represents a significant step toward cost-effective, scalable autonomous agent training, challenging established paradigms and revealing intriguing avenues for both practical and fundamental advancements in AI-driven robotic manipulation.