ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robot Navigation (2409.13682v1)

Abstract: Navigating and understanding complex environments over extended periods of time is a significant challenge for robots. People interacting with the robot may want to ask questions like where something happened, when it occurred, or how long ago it took place, which would require the robot to reason over a long history of their deployment. To address this problem, we introduce a Retrieval-augmented Memory for Embodied Robots, or ReMEmbR, a system designed for long-horizon video question answering for robot navigation. To evaluate ReMEmbR, we introduce the NaVQA dataset where we annotate spatial, temporal, and descriptive questions to long-horizon robot navigation videos. ReMEmbR employs a structured approach involving a memory building and a querying phase, leveraging temporal information, spatial information, and images to efficiently handle continuously growing robot histories. Our experiments demonstrate that ReMEmbR outperforms LLM and VLM baselines, allowing ReMEmbR to achieve effective long-horizon reasoning with low latency. Additionally, we deploy ReMEmbR on a robot and show that our approach can handle diverse queries. The dataset, code, videos, and other material can be found at the following link: https://nvidia-ai-iot.github.io/remembr

• The paper presents ReMEmbR, a system that builds and queries long-horizon spatio-temporal memory to improve robot navigation in complex environments.
• It leverages VLM-based video captioning combined with an LLM agent to efficiently retrieve and integrate spatial, temporal, and descriptive data.
• Evaluated on the NaVQA dataset, ReMEmbR outperforms baselines by achieving superior positional and temporal accuracy in extended sequences.

ReMEmbR: Long-Horizon Spatio-Temporal Memory for Robot Navigation

The paper "ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robot Navigation" presents a method for enhancing robotic navigation by incorporating long-term memory systems capable of handling spatio-temporal information. Written by Abrar Anwar et al., it introduces the ReMEmbR system that addresses long-horizon video question answering (QA) specifically tailored for robots navigating through complex environments.

Problem Statement and Motivation

The challenge addressed in this paper relates to the inherent limitations of existing robotic systems in navigating and understanding vast environments over extended durations. Robots deployed in varied settings like buildings or warehouses encounter dynamic events and objects which are not encapsulated adequately by conventional metric or semantic maps. Previous approaches to spatio-temporal video memory are constrained to short time spans, typically around 1-2 minutes. Moreover, efforts to extend memory through large context windows of LLMs (e.g., 1M length context window) are impractical due to scalability issues. Hence, the need emerges for a system that efficiently builds and queries long-horizon memory for robots, allowing them to answer detailed spatio-temporal questions over arbitrarily long histories.

Approach

The ReMEmbR system introduces a memory structure composed of a memory-building phase and a querying phase, leveraging both temporal and spatial information. The system uses a VLM for video captioning to aggregate descriptive and positional data into a vector database. It then employs an LLM agent for querying this database, effectively managing extensive histories. Key components of ReMEmbR include:

1. Memory Building Phase:
   • Uses VILA for video captioning to generate captions for temporal segments of the robot's experience.
   • Embeds these captions along with the positional and temporal data into a vector database. This enables efficient indexing and retrieval of specific spatio-temporal segments.
2. Querying Phase:
   • Utilizes an LLM agent to formulate and execute retrieval-based queries.
   • The agent iteratively retrieves and processes relevant segments from the vector database, relying on three types of function calls: text, position, and time-based queries.
   • The retrieved information is assessed and combined to form responses to user queries.

Dataset and Evaluation

A significant contribution of the paper is the introduction of the NaVQA dataset, designed to validate the performance of systems like ReMEmbR in long-horizon QA tasks. The dataset includes lengthy sequences annotated with questions demanding spatial, temporal, and descriptive answers. Evaluations are categorized into short (<2 minutes), medium (2-7 minutes), and long (>7 minutes) segments, enabling comprehensive performance reviews.

Results

The results indicate that ReMEmbR outperforms baseline models, particularly in longer sequences. It maintains higher correctness as video length increases, showcasing its robustness in extended deployments. In comparisons:

• ReMEmbR achieves superior positional and temporal accuracy compared to methods that process all frames or captions simultaneously.
• The iterative retrieval process is shown to enhance performance, especially in complex scenarios requiring multi-step reasoning.
• The system demonstrates low latency in answering queries, a critical factor for real-time applications.

Implications and Future Directions

ReMEmbR's ability to handle expansive robot histories has significant both theoretical and practical implications. Theoretically, it proposes a scalable solution for incorporating spatio-temporal memory in embodied agents, paving the way for further research into efficient memory management and retrieval mechanisms. Practically, it suggests potential enhancements in robotic navigation, inspection, and interaction tasks across diverse operational settings.

Future work can explore:

• Integrating additional memory types such as scene graphs or semantic maps to enrich contextual understanding.
• Enhancing the memory-building phase to incorporate selective aggregation, reducing redundancy and storage overhead.
• Addressing challenges in real-world deployments where ambiguous questions may necessitate sophisticated disambiguation techniques.

Conclusion

In summary, ReMEmbR represents a viable approach to extend the operational capabilities of robots through effective long-horizon spatio-temporal memory. By marrying VLM-based memory building with an LLM-agent querying phase, it achieves scalable, low-latency performance, making it a valuable contribution to the field of robot navigation and autonomy.