Lift3D Foundation Policy: Lifting 2D Large-Scale Pretrained Models for Robust 3D Robotic Manipulation (2411.18623v2)

Abstract: 3D geometric information is essential for manipulation tasks, as robots need to perceive the 3D environment, reason about spatial relationships, and interact with intricate spatial configurations. Recent research has increasingly focused on the explicit extraction of 3D features, while still facing challenges such as the lack of large-scale robotic 3D data and the potential loss of spatial geometry. To address these limitations, we propose the Lift3D framework, which progressively enhances 2D foundation models with implicit and explicit 3D robotic representations to construct a robust 3D manipulation policy. Specifically, we first design a task-aware masked autoencoder that masks task-relevant affordance patches and reconstructs depth information, enhancing the 2D foundation model's implicit 3D robotic representation. After self-supervised fine-tuning, we introduce a 2D model-lifting strategy that establishes a positional mapping between the input 3D points and the positional embeddings of the 2D model. Based on the mapping, Lift3D utilizes the 2D foundation model to directly encode point cloud data, leveraging large-scale pretrained knowledge to construct explicit 3D robotic representations while minimizing spatial information loss. In experiments, Lift3D consistently outperforms previous state-of-the-art methods across several simulation benchmarks and real-world scenarios.

• The paper presents a two-part approach combining a task-aware masked autoencoder and a 2D model-lifting strategy for explicit 3D representation in robotic manipulation.
• It enhances 3D spatial understanding by reconstructing depth from masked affordance patches and mapping 3D points onto 2D embeddings.
• Experimental validation on simulation benchmarks and real-world tasks shows significant improvements in success rates and generalization.

Overview of the Lift3D Framework for 3D Robotic Manipulation

The paper "Lift3D Foundation Policy: Lifting 2D Large-Scale Pretrained Models for Robust 3D Robotic Manipulation" presents a methodical approach to enhancing 2D foundation models for improved performance in 3D robotic manipulation tasks. This research tackles the prevalent issues in robotic manipulation, particularly the extraction and utilization of 3D geometric information, which is essential for effective robotic interaction in complex environments. The Lift3D framework systematically enriches 2D models with implicit and explicit 3D representations, thereby constructing a robust 3D manipulation policy.

Key Contributions and Methodology

The contribution of the Lift3D framework lies in its twofold approach:

1. Task-aware Masked Autoencoder (MAE) for Implicit 3D Representation: The paper introduces a novel task-aware MAE that enhances the implicit 3D representation capabilities of 2D models. The method involves masking task-relevant affordance patches within 2D images and reconstructing depth information through self-supervised learning. By leveraging large-scale datasets from robotic manipulation, the model refines its understanding of 3D spatial relationships, a significant improvement over random masking strategies common in previous models.
2. 2D Model-Lifting Strategy for Explicit 3D Representation: This strategy is designed to directly encode 3D point cloud data using a modified 2D foundation model. By establishing a positional mapping between 3D points and the positional embeddings in the 2D model, Lift3D effectively transforms the ability of the 2D model to handle 3D data, minimizing spatial information loss. The explicit encoding is achieved without modality transformation, which has been a limiting factor in prior approaches.

Experimental Validation

The effectiveness of Lift3D is demonstrated through rigorous experimentation on both simulation benchmarks (MetaWorld, Adroit, RLBench) and real-world scenarios encompassing over 30 diverse robotic tasks. The framework consistently surpasses previous state-of-the-art methods across varying task difficulties, achieving notably higher success rates in both simulation and real-world settings. For instance, in the MetaWorld benchmark, Lift3D achieved improvements in mean success rates over top-performing 2D representation methods and previous 3D policy frameworks by significant margins.

The real-world experiments further validate the framework's practical applicability. Lift3D showcases its robustness by learning novel manipulation skills from as few as 30 training episodes per task. Additionally, the trials highlight the model's strong generalization capabilities, adapting effectively to different manipulated instances, background scenes, and lighting conditions.

Implications and Future Directions

The Lift3D framework represents a significant advancement in the intersection of 2D machine learning models and 3D robotic manipulation tasks. By advancing both implicit and explicit 3D robotic representation learning, it sets a precedent for future research in robotic manipulation, especially in environments where 3D spatial reasoning is critical.

Looking forward, the breakthrough in leveraging large-scale pretrained 2D foundation models for 3D tasks opens up several avenues for exploration. There is potential for further integration with multimodal models incorporating language and other sensory data, leading to more versatile and human-like robotic systems. Additionally, scaling the architecture and fine-tuning for additional real-world complexities could broaden application domains, from intricate manufacturing processes to autonomous service robots in dynamic human environments.

In conclusion, Lift3D alleviates current constraints in robotic manipulation by intelligently expanding the utility of existing large-scale 2D models, harnessing them to navigate the intricate demands of 3D space with surprising proficiency and adaptability.