Dynamic Robot Tool Use with Vision Language Models (2505.01399v1)

Abstract: Tool use enhances a robot's task capabilities. Recent advances in vision-LLMs (VLMs) have equipped robots with sophisticated cognitive capabilities for tool-use applications. However, existing methodologies focus on elementary quasi-static tool manipulations or high-level tool selection while neglecting the critical aspect of task-appropriate tool grasping. To address this limitation, we introduce inverse Tool-Use Planning (iTUP), a novel VLM-driven framework that enables grounded fine-grained planning for versatile robotic tool use. Through an integrated pipeline of VLM-based tool and contact point grounding, position-velocity trajectory planning, and physics-informed grasp generation and selection, iTUP demonstrates versatility across (1) quasi-static and more challenging (2) dynamic and (3) cluster tool-use tasks. To ensure robust planning, our framework integrates stable and safe task-aware grasping by reasoning over semantic affordances and physical constraints. We evaluate iTUP and baselines on a comprehensive range of realistic tool use tasks including precision hammering, object scooping, and cluster sweeping. Experimental results demonstrate that iTUP ensures a thorough grounding of cognition and planning for challenging robot tool use across diverse environments.

Dynamic Robot Tool Use with Vision LLMs

In the paper "Dynamic Robot Tool Use with Vision LLMs," the authors present a novel framework, inverse Tool-Use Planning (iTUP), which leverages Vision LLMs (VLMs) to enhance robotic tool use. This research addresses the limitations of previous methodologies that focused primarily on tool selection or basic static manipulations, neglecting the nuanced requirements of dynamic tool use and task-specific grasping.

Overview of iTUP Framework

The iTUP framework integrates several components:

1. VLM-based Tool and Contact Point Grounding: This module identifies the appropriate tools and objects in a scene while recognizing the optimal contact points for interaction.
2. Position-Velocity Trajectory Planning: It plans motion trajectories that account for dynamic interactions between tools and objects, ensuring alignment with intended task directions and follow-through distances.
3. Physics-informed Grasp Generation and Selection: Through the Stable Dynamic Grasp Network (SDG-Net), iTUP evaluates grasp poses to ensure stability during dynamic tool interactions. This module synthesizes multimodal inputs from geometric, force, and torque data to predict slip and alignment penalties, emphasizing the physics constraints inherent in dynamic tasks.

Key Contributions and Findings

The authors highlight several contributions through their paper:

• A comprehensive analysis and implementation of dynamic tool use scenarios in robotic applications, filling a gap left by previous quasi-static frameworks.
• The introduction of iTUP as a unified framework supporting both open-vocabulary cognition and physics-aware planning.
• Demonstrated superior performance in tool-use cognition and manipulation tasks compared to existing baselines through rigorous simulations and real-world evaluations.

The SDG-Net provides exceptional physical grounding, significantly reducing net torques experienced during dynamic interactions, which translates to more stable grasps and improved task success rates across diverse scenarios.

Implications and Future Directions

Practically, the iTUP framework enables robots to perform complex tasks involving dynamic interactions with higher accuracy and stability, increasing their versatility in real-world applications. This is particularly significant in scenarios where precise tool manipulation is critical, such as assembly operations, surgical assistance, or disaster response.

Theoretically, this work advances the discussion on integrating high-level cognition with nuanced physical task planning, emphasizing the role of VLMs in understanding and reasoning about novel environments and tool use contexts.

Further research could explore the integration of end-to-end, vision-language-action models, potentially reducing modular dependencies and computational overhead while enhancing robustness and adaptability. Improved spatial reasoning within VLMs could further refine trajectory planning, especially in cluttered or complex environments.

In summary, this paper contributes a substantial step forward in the evolution of robotic tool use, expanding the capabilities of autonomous systems within dynamic interaction domains through innovative AI applications.