GenSim2: Scaling Robot Data Generation with Multi-modal and Reasoning LLMs (2410.03645v1)

Abstract: Robotic simulation today remains challenging to scale up due to the human efforts required to create diverse simulation tasks and scenes. Simulation-trained policies also face scalability issues as many sim-to-real methods focus on a single task. To address these challenges, this work proposes GenSim2, a scalable framework that leverages coding LLMs with multi-modal and reasoning capabilities for complex and realistic simulation task creation, including long-horizon tasks with articulated objects. To automatically generate demonstration data for these tasks at scale, we propose planning and RL solvers that generalize within object categories. The pipeline can generate data for up to 100 articulated tasks with 200 objects and reduce the required human efforts. To utilize such data, we propose an effective multi-task language-conditioned policy architecture, dubbed proprioceptive point-cloud transformer (PPT), that learns from the generated demonstrations and exhibits strong sim-to-real zero-shot transfer. Combining the proposed pipeline and the policy architecture, we show a promising usage of GenSim2 that the generated data can be used for zero-shot transfer or co-train with real-world collected data, which enhances the policy performance by 20% compared with training exclusively on limited real data.

Overview of GenSim2: Scaling Robot Data Generation through Multi-modal and Reasoning LLMs

Introduction

The paper presents GenSim2, an advanced framework for robot data generation that leverages LLMs with multi-modal and reasoning capabilities. GenSim2 is designed to address the challenges of scaling complex robotic simulation tasks, including long-horizon tasks with articulated objects. The framework is underscored by three key processes: task and data generation, multi-task training, and sim-to-real transfer.

Key Contributions

1. Task and Data Generation: GenSim2 introduces a scalable approach to generating diverse robotic tasks using multi-modal LLMs. The system capitalizes on semantic and compositional capabilities of LLMs like GPT-4V to design intricate tasks involving 6-DOF movements. This methodology enables the creation of over 100 articulated tasks, where the task generation pipeline alone improves success rates by 25% over previous works.
2. Policy Architecture: The proprioceptive point-cloud transformer (PPT) architecture is a novel contribution for sim-to-real transfer. Handling point-cloud observations and language conditioning, PPT is optimized to manage the sim-to-real gap. The architecture supports various policy heads such as MLPs, transformers, and diffusion models, contributing to its adaptability and efficiency.
3. Zero-Shot and Co-Training Capability: When integrating generated data with the designed PPT policy, the framework shows potential for zero-shot sim-to-real transfer and co-training applications. The paper highlights a 20% performance enhancement compared to training exclusively on real-world data, evidencing the efficacy of combining simulated and real data.

Experimental Results

The experiments conducted demonstrate that GenSim2 can efficiently generate over 100 complex tasks and demonstrate a 3% performance drop on unseen instances, exhibiting strong generalization. Real-world experiments further validate the framework's ability to execute real-world tasks with a notable success rate improvement through the co-training strategy.

Implications and Future Directions

The implications of GenSim2 are significant both practically and theoretically. Practically, the framework reduces the reliance on real-world data, lowering the costs and increasing the feasibility of training versatile robotic policies. Theoretically, it opens avenues for exploring the intersection of LLMs and robotics, particularly in generating and solving complex real-world tasks.

Future research could explore enhanced task complexity and agent interactions using more advanced multi-modal agents and 3D asset generation techniques. Additionally, improvements in the vision language foundation models could reduce current limitations in spatial understanding and task coding.

Conclusion

GenSim2 represents a substantial advancement in the field of robotic simulation and learning. By leveraging multi-modal foundation models, it offers a scalable solution for generating high-quality simulation data that effectively transfers to real-world applications. This framework paves the way for future innovations in robotics, where simulations increasingly play a central role in training and policy development.