Training-free Task-oriented Grasp Generation

TL;DR

This work introduces TOG, a training-free pipeline that fuses pre-trained grasp-generation models with vision-language models to produce task-oriented grasps without additional training. It formalizes task-conditioned grasp generation as $P(G^* \\mid X, T)$ and employs a two-stage strategy: sample diverse candidate grasps from a pre-trained model, filter for feasibility, then use a VLM to select the grasp that best satisfies the task. Across simulation and real-world experiments, TOG demonstrates robust gains in task compliance and overall success, with the best performance achieved when a powerful VLM is allowed to freely indicate grasp points (CPG) or when a constrained, diverse set of grasps is filtered for VLM evaluation depending on VLM strength. The study highlights the potential of integrating foundation-model reasoning with traditional grasp generation to flexibly adapt to varied tasks, while identifying challenges in grasp diversity, query ambiguity, and spatial reasoning that point to future improvements such as view-based ambiguity reduction. This approach offers a scalable, training-free pathway to enhance task-oriented manipulation in robotics, enabling faster adaptation to new objects and tasks through modal model collaboration.

Abstract

This paper presents a training-free pipeline for task-oriented grasp generation that combines pre-trained grasp generation models with vision-language models (VLMs). Unlike traditional approaches that focus solely on stable grasps, our method incorporates task-specific requirements by leveraging the semantic reasoning capabilities of VLMs. We evaluate five querying strategies, each utilizing different visual representations of candidate grasps, and demonstrate significant improvements over a baseline method in both grasp success and task compliance rates, with absolute gains of up to 36.9\% in overall success rate. Our results underline the potential of VLMs to enhance task-oriented manipulation, providing insights for future research in robotic grasping and human-robot interaction.

Figures (8)

• Figure 1: Given a specific task, TOG can generate feasible grasps according to the task specifications by leveraging the combined capabilities of (CGN) and a Vision-Language Model (VLM).
• Figure 2: Overview of the system. The depth image is unprojected into a point cloud, which is processed by a grasp generation model (e.g., ) to produce a set of unconditional grasps. The top K grasps are selected based on confidence and further refined by a motion planner to ensure trajectory feasibility. These filtered grasps are then evaluated using a vision-language model (VLM) to select the best grasp for the task.
• Figure 3: Illustration of using K-means clustering and scores to select diverse grasps while taking into account the quality of each grasp.
• Figure 4: Comparison of grasp success, task success, and combined success across different evaluation metrics.
• Figure 5: Common failure cases: (a) Insufficient grasp diversity due to clustering. (b) Ambiguous query image makes grasp identification difficult. (c) VLM selects an unstable grasp. (d) VLM assigns an incorrect contact point (the blue dot)