- The paper demonstrates a novel framework that uses LLMs to generate executable code for mapping multi-modal instructions to robotic actions.
- It integrates visual models (SAM and CLIP) for robust object segmentation and classification, enabling zero-shot adaptability.
- Experimental results show superior multi-step manipulation performance and enhanced task efficacy with combined modality inputs.
Instruct2Act: Mapping Multi-Modality Instructions to Robotic Actions with LLM
The paper presents "Instruct2Act," a framework leveraging LLMs to map multi-modality instructions into robotic actions, specifically targeting robotic manipulation tasks. This innovative framework employs advanced foundation models like the Segment Anything Model (SAM) and CLIP for object recognition and classification, integrating these functionalities via pre-defined APIs and LLM-generated Python programs. These allow precise perception, planning, and control loops essential for robotic operations.
Methodological Insights
Instruct2Act's primary contribution lies in its ability to harness the in-context learning capabilities of LLMs to generate programmatic policy codes that direct robotic actions based on multi-modal instructions. The framework integrates SAM for object segmentation and CLIP for classification, enabling comprehensive environmental perception. This perception culminates in the generation of executable code that governs robotic actions. Notably, this process occurs without fine-tuning, underscoring the practicality and zero-shot adaptability of using foundational models.
The system distinguishes itself by providing flexible modalities, capable of processing natural language and visual data to inform its operations. A unified modality instruction interface allows the framework to handle diverse tasks, accommodating both single language inputs and complex visual cues. The input modalities are seamlessly managed to maximize efficacy in understanding and executing tasks.
Experimental Validation
Empirical evaluations within tabletop manipulation domains demonstrate the robust performance of Instruct2Act. The framework's zero-shot capabilities outperform various state-of-the-art learning-based policies across multiple manipulation tasks, including object movement and complex scene rearrangement. Specifically, it exhibits notable success in six meta-tasks from VIMABench, highlighting superior performance in tasks that require multi-step reasoning, such as put-and-place and rearrangement operations.
The paper's analysis reveals that performance improves with multi-modal instructions versus uni-modal inputs, attributing this to enhanced context and reduced ambiguity in task comprehension. Processing modules, like image and mask pre-processing, further ameliorate segmentation outputs, enhancing task success rates.
Implications and Future Directions
The combination of LLMs with visual foundation models heralds significant implications for robotics, offering general-purpose systems that integrate perception and action without extensive retraining. This synergy between LLMs and multi-modal models can be expanded to complex, dynamic environments, pushing the boundaries of autonomous robotic systems.
Future work might explore the scalability of Instruct2Act in real-time and constrained computational settings, enhancing efficiency without sacrificing capabilities. Extending the framework to handle a broader range of robotic tasks and environments, possibly incorporating more advanced foundation models, could lead to more nuanced applications. Furthermore, experimental validation in real-world scenarios would provide critical insights into practical deployment challenges and refinements.
In conclusion, Instruct2Act represents a notable advancement in robotic manipulation, establishing a benchmark for integrating multi-modal instructions with LLMs. It underscores the potential transformative impact of foundational models in advancing robotic autonomy and adaptability.