GenSim: Generating Robotic Simulation Tasks via Large Language Models (2310.01361v2)

Abstract: Collecting large amounts of real-world interaction data to train general robotic policies is often prohibitively expensive, thus motivating the use of simulation data. However, existing methods for data generation have generally focused on scene-level diversity (e.g., object instances and poses) rather than task-level diversity, due to the human effort required to come up with and verify novel tasks. This has made it challenging for policies trained on simulation data to demonstrate significant task-level generalization. In this paper, we propose to automatically generate rich simulation environments and expert demonstrations by exploiting a LLMs' (LLM) grounding and coding ability. Our approach, dubbed GenSim, has two modes: goal-directed generation, wherein a target task is given to the LLM and the LLM proposes a task curriculum to solve the target task, and exploratory generation, wherein the LLM bootstraps from previous tasks and iteratively proposes novel tasks that would be helpful in solving more complex tasks. We use GPT4 to expand the existing benchmark by ten times to over 100 tasks, on which we conduct supervised finetuning and evaluate several LLMs including finetuned GPTs and Code Llama on code generation for robotic simulation tasks. Furthermore, we observe that LLMs-generated simulation programs can enhance task-level generalization significantly when used for multitask policy training. We further find that with minimal sim-to-real adaptation, the multitask policies pretrained on GPT4-generated simulation tasks exhibit stronger transfer to unseen long-horizon tasks in the real world and outperform baselines by 25%. See the project website (https://liruiw.github.io/gensim) for code, demos, and videos.

Essay: GenSim: Generating Robotic Simulation Tasks via LLMs

The research paper titled "GenSim: Generating Robotic Simulation Tasks via LLMs" presents a novel framework leveraging LLMs to automatically generate diverse robotic simulation tasks. The central premise of this work is to mitigate the challenges associated with human-curated simulation data, which often lack task-level diversity due to the extensive human effort required to design such tasks. GenSim proposes an innovative approach to address this limitation by exploiting the grounding and coding capabilities of LLMs to create simulation environments vastly surpassing existing benchmarks in task variety.

Framework Overview

GenSim framework introduces two modes: goal-directed generation and exploratory generation. In the goal-directed mode, a target task is specified, and the LLM proposes a curriculum to achieve it. Conversely, the exploratory mode bootstraps from existing tasks, iteratively generating novel tasks to tackle more complex challenges. The framework uses GPT-4 to scale the existing benchmark from 10 to over 100 tasks, significantly broadening the task space for robotic simulations.

The framework comprises three core components:

1. Task Creator: Utilizes prompting mechanisms to propose task descriptions and code implementations, enabling the generation of scene structures and demonstrations rooted in motion primitives.
2. Task Library: Acts as a memory component, storing high-quality generated tasks for retrieval and finetuning purposes. This library serves as a foundational dataset for multitask policy training.
3. LLM Supervised Multitask Policy Training: Translates the synthesized tasks into expert demonstration data for policy learning, facilitating significant task-level generalization improvements.

Empirical Evaluation

The paper provides a rigorous empirical evaluation of GenSim. It demonstrates that LLM-generated simulation tasks can substantially enhance task-level generalization in policy learning, validated through improved performance metrics:

• Simulation Task Generation: The paper compares the efficacy of various LLMs, such as GPT-3.5 and Code-Llama, in generating simulation tasks. Finetuning on GPT-4-generated data shows superior results, emphasizing the impact of domain-specific task finetuning on code generation models.
• Policy Generalization: By training on augmented datasets from LLM-generated tasks, policies display improved performance in both in-domain generalization and zero-shot generalization to unseen tasks.
• Sim-to-Real Transfer: Demonstrating the practical implications, the research shows that policies pretrained on LLM-generated data exhibit better transfer to real-world tasks, outperforming baselines by up to 25% after minimal sim-to-real adaptation.

Implications and Future Directions

The GenSim framework highlights the transformative potential of LLMs in generating robotic simulation tasks, offering a scalable solution to enhance task diversity and generalization in robotic policy learning. This research carries significant theoretical implications, suggesting new pathways for integrating LLMs in simulation environments to automate and enhance training datasets.

Looking forward, future work could explore the extension of this framework to more complex, dexterous robotic tasks encompassing a wider range of physical interactions and constraints. Additionally, addressing current limitations such as code hallucinations and improving LLM's grounding in task-specific physical contexts could further enhance the robustness and applicability of GenSim. Furthermore, incorporating self-refinement strategies, leveraging self-instruct mechanisms, or deploying larger-scale retrieval-augmented generation could potentially yield even more diverse and high-quality task scenarios.

In summary, the GenSim framework stands as a promising avenue for advancing the landscape of robotic simulation tasks, offering new opportunities for leveraging LLMs to drive innovation in robotic policy training and task generalization.