SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation

Abstract: Humans demonstrate remarkable skill in transferring manipulation abilities across objects of varying shapes, poses, and appearances, a capability rooted in their understanding of semantic correspondences between different instances. To equip robots with a similar high-level comprehension, we present SparseDFF, a novel DFF for 3D scenes utilizing large 2D vision models to extract semantic features from sparse RGBD images, a domain where research is limited despite its relevance to many tasks with fixed-camera setups. SparseDFF generates view-consistent 3D DFFs, enabling efficient one-shot learning of dexterous manipulations by mapping image features to a 3D point cloud. Central to SparseDFF is a feature refinement network, optimized with a contrastive loss between views and a point-pruning mechanism for feature continuity. This facilitates the minimization of feature discrepancies w.r.t. end-effector parameters, bridging demonstrations and target manipulations. Validated in real-world scenarios with a dexterous hand, SparseDFF proves effective in manipulating both rigid and deformable objects, demonstrating significant generalization capabilities across object and scene variations.

• The paper introduces SparseDFF, a framework that maps sparse RGBD features into consistent 3D fields for one-shot dexterous manipulation.
• It employs a feature refinement network with contrastive learning and a point-pruning mechanism to ensure precise skill transfer.
• Experimental results show high success rates, achieving 100% on select rigid objects and demonstrating strong potential for real-world applications.

SparseDFF: A New Approach for One-Shot Dexterous Manipulation

The paper presents SparseDFF, a novel framework aimed at enhancing the capability of robots to perform dexterous manipulations through a one-shot learning paradigm. This research addresses the complexities involved in enabling robots to replicate human-like manipulation skills across a variety of objects and scenarios, which is an enduring challenge in robotics.

SparseDFF leverages the concept of Distilled Feature Fields (DFF) in 3D scenes, utilizing large pretrained 2D vision models to extract semantic features from sparse RGBD images. Unlike traditional methods which are dependent on dense and comprehensive camera views, SparseDFF can work efficiently with sparse inputs. This ability is pivotal for practical applications in environments where the camera setup is constrained. The core innovation of SparseDFF lies in its ability to create view-consistent 3D feature fields that facilitate the mapping of image features to 3D point clouds, enabling one-shot learning of dexterous manipulations.

Methodology Overview

The SparseDFF approach includes:

• Feature Refinement Network: This network is optimized using contrastive learning to refine features extracted from sparse RGBD inputs, enhancing the feature consistency across different views. Such refinement is crucial for ensuring that the feature discrepancies between the source and target scenes are minimized when transferring manipulation skills.
• Point-Pruning Mechanism: This mechanism ensures continuity and consistency of features within the local regions of the 3D point cloud, addressing challenges in scenarios with limited view data.
• Energy Function for End-Effector Optimization: The paper introduces an energy function framework that utilizes the refined 3D feature fields to optimize the pose of the robotic end-effector. This is particularly significant for enabling seamless transfer of demonstrated skills to new settings with different object poses, deformations, and backgrounds.

Experimental Validation

The paper validates SparseDFF through experiments involving a real-world dexterous hand, such as the Shadow Dexterous Hand, in various manipulation tasks. These tasks include interacting with both rigid and deformable objects. The method demonstrated strong generalization across differing object categories and complex scene contexts. SparseDFF achieved high success rates, such as 100% on certain rigid objects like the Cheez-It box in the YCB dataset, and proved adaptable when transferring skills between different objects and categories.

Implications and Future Prospects

SparseDFF's ability to work with sparse views represents a significant stride in robotics, especially in terms of reducing the prerequisites for dense spatial information during manipulation. This not only makes it feasible for deployment in real-world scenarios with limited sensory setups but also paves the way for more adaptive and generalizable robotic manipulation systems.

From a theoretical perspective, this approach expands the application of DFFs by incorporating semantic understanding into 3D feature spaces, supporting more comprehensive and context-aware interaction models. Practically, it offers a scalable framework for learning manipulation tasks with minimal data, reducing the dependency on exhaustive and cumbersome data collection processes.

Looking ahead, the framework could be extended to incorporate feedback from additional sensors such as tactile inputs, enabling more nuanced interaction models that can further enhance robotic abilities to handle intricate and delicate tasks. The current advancements present compelling opportunities for integrating SparseDFF into wider robotic and AI systems, potentially improving autonomy and efficiency in industrial and domestic applications.