Language-Grounded Dynamic Scene Graphs for Interactive Object Search with Mobile Manipulation (2403.08605v4)

Abstract: To fully leverage the capabilities of mobile manipulation robots, it is imperative that they are able to autonomously execute long-horizon tasks in large unexplored environments. While LLMs have shown emergent reasoning skills on arbitrary tasks, existing work primarily concentrates on explored environments, typically focusing on either navigation or manipulation tasks in isolation. In this work, we propose MoMa-LLM, a novel approach that grounds LLMs within structured representations derived from open-vocabulary scene graphs, dynamically updated as the environment is explored. We tightly interleave these representations with an object-centric action space. Given object detections, the resulting approach is zero-shot, open-vocabulary, and readily extendable to a spectrum of mobile manipulation and household robotic tasks. We demonstrate the effectiveness of MoMa-LLM in a novel semantic interactive search task in large realistic indoor environments. In extensive experiments in both simulation and the real world, we show substantially improved search efficiency compared to conventional baselines and state-of-the-art approaches, as well as its applicability to more abstract tasks. We make the code publicly available at http://moma-LLM.cs.uni-freiburg.de.

Analysis of Language-Grounded Dynamic Scene Graphs for Interactive Object Search with Mobile Manipulation

The paper proposes MoMa-LLM, an innovative approach to augment autonomous capabilities of mobile manipulation robots in large, unexplored environments by integrating LLMs with dynamic scene graphs. The paper introduces an architecture that intelligently combines the reasoning abilities of LLMs with dynamic, language-grounded scene representations, catering specifically to interactive and complex household tasks.

Methodological Developments

MoMa-LLM builds upon the intersection of cognitive robotics and natural language processing by dynamically linking LLMs to scene graphs constructed from sensory inputs. The scene graphs are enriched with open-vocabulary semantics and integrate both room and object-centric structures, facilitating navigation and manipulation tasks through an object-centric action space. By leveraging structured textual representations generated from the scene graphs, the approach enables efficient, zero-shot planning across diverse tasks.

Key components of the MoMa-LLM system include:

• Hierarchical 3D Scene Graphs: These graphs capture the environment's spatial and semantic details, incorporating complexity through Voronoi graphs for navigation. They are dynamically updated as the robot explores, maintaining an evolving understanding of its surroundings.
• Structured Language Encoding: An LLM is grounded via structured language inputs extracted from scene graphs, which provide contextual information essential for high-level reasoning in unexplored settings. This grounding is crucial for robustness against hallucinations and maintaining the relevance of decision trajectories.
• Exploration and Task Execution: The system incorporates specific exploration strategies and a history mechanism for tracking interaction sequences, optimizing the exploration-exploitation balance key to solving long-horizon tasks.

Experimental Results

The paper presents extensive empirical evaluations in both simulation and real-world setups. In simulated environments, MoMa-LLM demonstrates significantly improved search efficiency and success rates in comparison to baseline methods, including heuristic, learning, and zero-shot strategies. The analysis employs novel metrics such as the AUC-E, which reflects efficiency across exploration time budgets more comprehensively than traditional success weighted path-length metrics.

Real-world applications reveal the successful integration of MoMa-LLM with physical systems, highlighting its adaptability to complex, real-world environments. Notably, MoMa-LLM's performance was robust despite the dynamic and unpredictable nature of real-world tasks.

Implications and Future Trajectories

The paper's contributions resonate strongly within autonomous robotics and AI research. MoMa-LLM's integration of language grounding with dynamic scene graph updating marks a notable advancement, presenting a scalable solution that extends beyond mere navigation or manipulation into true interactive engagement with complex environments.

Future research directions are anticipated to involve:

• Enhanced Perception and Scene Understanding: Further development could incorporate more sophisticated perception systems to improve object recognition in dynamic environments and bolster the robustness of scene graph segmentation.
• Expanded Task Domains: While the current focus is on indoor household environments, future iterations could expand to outdoor or industrial settings, testing the transferability and scalability of MoMa-LLM.
• Integration with More Complex Language Understanding: Advancements in LLM capabilities could lead to more nuanced language-grounded tasks, facilitating more complex interaction scenarios and richer environmental understandings.

Overall, MoMa-LLM addresses critical limitations in existing robotic systems, making significant strides towards more autonomous, intelligent, and context-aware robotic systems capable of executing complex tasks with minimal human intervention.