Language Embedded Radiance Fields for Zero-Shot Task-Oriented Grasping (2309.07970v2)

Abstract: Grasping objects by a specific part is often crucial for safety and for executing downstream tasks. Yet, learning-based grasp planners lack this behavior unless they are trained on specific object part data, making it a significant challenge to scale object diversity. Instead, we propose LERF-TOGO, Language Embedded Radiance Fields for Task-Oriented Grasping of Objects, which uses vision-LLMs zero-shot to output a grasp distribution over an object given a natural language query. To accomplish this, we first reconstruct a LERF of the scene, which distills CLIP embeddings into a multi-scale 3D language field queryable with text. However, LERF has no sense of objectness, meaning its relevancy outputs often return incomplete activations over an object which are insufficient for subsequent part queries. LERF-TOGO mitigates this lack of spatial grouping by extracting a 3D object mask via DINO features and then conditionally querying LERF on this mask to obtain a semantic distribution over the object with which to rank grasps from an off-the-shelf grasp planner. We evaluate LERF-TOGO's ability to grasp task-oriented object parts on 31 different physical objects, and find it selects grasps on the correct part in 81% of all trials and grasps successfully in 69%. See the project website at: lerftogo.github.io

• The paper introduces LERF-TOGO, which integrates language semantics with 3D radiance fields to improve robotic grasping precision.
• It employs CLIP embeddings and DINO features to create detailed object masks, achieving an 81% success rate in target-part selection.
• The approach enables zero-shot grasping without prior affordance data, paving the way for safer, task-based robotic manipulation in diverse settings.

Language Embedded Radiance Fields for Zero-Shot Task-Oriented Grasping

The paper "Language Embedded Radiance Fields for Zero-Shot Task-Oriented Grasping" introduces an innovative approach termed LERF-TOGO, which aims to enhance robotic grasping systems through natural language processing. Traditionally, grasp planners prioritize geometric features to identify potential grasps. However, such systems often neglect semantic properties of objects, which could lead to harmful interactions with delicate or function-specific items. LERF-TOGO addresses this oversight by integrating semantic understanding, enabling task-oriented grasping without the need for prior affordance datasets.

LERF-TOGO builds upon Language Embedded Radiance Fields (LERF), employing CLIP embeddings and multi-scale 3D language fields to facilitate semantic queries of scenes. Despite LERF's capacity for nuanced language interpretation, its initial iteration failed to precisely associate semantic information with distinct object regions due to a lack of spatial grouping. To remedy this, LERF-TOGO employs DINO features to generate 3D object masks, enabling conditional querying on specified object parts. This strategic improvement empowers the system to rank grasps based on both semantic relevance and geometric viability when utilizing off-the-shelf grasp planners like GraspNet.

During experimentation, LERF-TOGO demonstrated substantial capability, achieving an 81% success rate in selecting grasps on target object parts and a 69% success rate in physical grasp execution. These results underline the potential of LERF-TOGO in enhancing the safety and functionality of robotic grasping endeavors, with its zero-shot methodology ensuring scalability and adaptability across various objects without the need for exhaustive training datasets. Additionally, integrating LERF-TOGO with LLMs offers promising avenues for real-time task-based manipulations, as demonstrated in the paper by automated selection of grasps for specified tasks and objects.

The implications of this research are manifold. Practically, LERF-TOGO can elevate the precision and safety of robotic systems handling delicate or critical object portions, potentially revolutionizing applications in fields such as manufacturing and healthcare. Theoretically, the synthesis of radiance fields and LLMs posits a new frontier for semantic scene understanding and manipulation in robotics. However, challenges remain, particularly concerning computational efficiency and the accurate differentiation of object parts amidst visually complex environments.

LERF-TOGO's contribution to the field is significant, outlining future potential for more advanced semantics-driven robotic systems. The integration with vision-LLMs for real-world applications remains a promising direction for subsequent research. Enhancing the system's computational velocity and object part differentiation are imperative next steps, aiming to render these systems applicable in time-sensitive and dynamically evolving contexts. Furthermore, exploring additional modalities and learning architectures might alleviate existing limitations and further refine the grasping paradigm established by LERF-TOGO.