LINGO-Space: Language-Conditioned Incremental Grounding for Space (2402.01183v1)

Abstract: We aim to solve the problem of spatially localizing composite instructions referring to space: space grounding. Compared to current instance grounding, space grounding is challenging due to the ill-posedness of identifying locations referred to by discrete expressions and the compositional ambiguity of referring expressions. Therefore, we propose a novel probabilistic space-grounding methodology (LINGO-Space) that accurately identifies a probabilistic distribution of space being referred to and incrementally updates it, given subsequent referring expressions leveraging configurable polar distributions. Our evaluations show that the estimation using polar distributions enables a robot to ground locations successfully through $20$ table-top manipulation benchmark tests. We also show that updating the distribution helps the grounding method accurately narrow the referring space. We finally demonstrate the robustness of the space grounding with simulated manipulation and real quadruped robot navigation tasks. Code and videos are available at https://lingo-space.github.io.

• The paper introduces LINGO-Space, a method that incrementally grounds complex spatial language using probabilistic reasoning.
• It combines a scene-graph generator, semantic parser, and spatial-distribution estimator to address the ambiguity in composite instructions.
• Experimental results show that LINGO-Space outperforms existing methods in tasks like table-top manipulation, enhancing robotic navigation and interaction.

An Examination of LINGO-Space: Language-Conditioned Incremental Grounding for Space

The paper "LINGO-Space: Language-Conditioned Incremental Grounding for Space" addresses the complex problem of space grounding in robotics. This domain extends the grounding problem by focusing on spatially localizing composite instructions, encompassing spatial relations within a given instruction. The central premise of the work is the need for robotics to not only identify objects but also understand instructions related to space, critical for tasks like manipulation or navigation where spatial cues dictate object placement or location identification.

Probabilistic Space-Grounding Methodology

The authors propose LINGO-Space, a methodology that leverages probabilistic reasoning to localize spaces referred to in composite natural language instructions. This approach departs from conventional instance grounding techniques focusing solely on objects and instead tackles the inherent ambiguity of spatial references. The LINGO-Space framework operates by updating a probabilistic distribution of space incrementally, processing referring expressions sequentially, a task which is non-trivial due to the compositional nature and ambiguity of spatial language.

Key to this method is the use of configurable polar distributions, which allow for the representation of uncertainty in spatial terms. By leveraging LLM-guided semantic parsers, the proposed method enhances its ability to interpret complex referring expressions accurately. Through evaluations, it is demonstrated that this approach effectively grounds space through tasks like table-top manipulation, showcasing robustness across simulations and real robotic scenarios.

Methodology Overview and Technical Components

The architecture of LINGO-Space comprises three main components: a scene-graph generator, a semantic parser, and a spatial-distribution estimator.

1. Scene-Graph Generator: This module creates a representation of the physical scene, capturing objects and their spatial relationships. Each object's position, bounding box, and visual features are encoded and linked with their spatial relations to other objects, forming a comprehensive graph.
2. Semantic Parser: This component employs a LLM to decompose natural language instructions into a structured format, identifying actions and spatial relations sequentially, rather than concurrently. This decomposition allows the system to handle composite instructions, parsing them into simpler, manageable units for further processing.
3. Spatial-Distribution Estimator: At the heart of the LINGO-Space, this component models the spatial probability distribution using a mixture of polar distributions, which are updated incrementally. This process allows the estimator to refine location estimates iteratively, accommodating new information from parsed instructions.

Experimental Validation and Performance

The experimental results presented in the paper indicate that LINGO-Space outperforms existing baseline methods in space grounding tasks, especially when dealing with composite instructions featuring multiple spatial relations. The paper utilizes several benchmark tests, including newly introduced ones, to evaluate the accuracy of space grounding when new predicates are involved. The system's ability to produce high success scores across diverse scenarios underscores its robustness and adaptability.

The paper also discusses real-world applications of LINGO-Space, particularly in robotic navigation and manipulation tasks. The practical applicability of the method is demonstrated through the integration of LINGO-Space into robotic frameworks, where it effectively guides a quadruped robot by interpreting and acting upon complex spatial instructions.

Theoretical and Practical Implications

From a theoretical standpoint, LINGO-Space advances the understanding of spatial grounding by introducing a probabilistic framework that is capable of handling the nuanced complexity of natural language instructions. Practically, the integration of LINGO-Space into robotic systems paves the way for more advanced human-robot interaction capabilities, where robots can intuitively understand and execute tasks in compliance with spatial descriptions given by humans.

Future Developments

The research opens several avenues for future exploration, particularly in enhancing the accuracy and versatility of probabilistic distributions used for spatial reasoning. Further refinements could focus on improving the semantic parsing of instructions to handle even more complex linguistic structures, potentially leveraging advances in LLMs. Additionally, integrating LINGO-Space with more sophisticated sensor technologies could enhance its robustness in diverse environments.

In conclusion, LINGO-Space presents a significant contribution to the field of robotics, specifically in the nuanced area of spatial instruction grounding. With further development, this approach holds promise for expanding the capabilities of robots in complex operational environments where understanding space and spatial relations plays a critical role.