Google DeepMind Researchers Present Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs

Technological advancements in sensors, AI, and processing power have propelled robot navigation to new heights in the last several decades. To take robotics to the next level and make them a regular part of our lives, many studies suggest transferring the natural language space of ObjNav and VLN to the multimodal space so the robot can follow commands in both text and images at the same time. Researchers call this type of maritime activity Multimodal Instruction Navigation (MIN).

MIN encompasses a wide range of activities, including exploring the surroundings and following instructions for navigation. However, the use of a demonstration tour film that covers the entire region allows one to avoid investigation often altogether. 

A Google DeepMind study presents and investigates a class of tasks called Multimodal Instruction Navigation with Tours (MINT). MINT uses demonstration tours and is concerned with carrying out multimodal user instructions. The remarkable capabilities of massive Vision-Language Models (VLMs) in language and picture interpretation and common-sense reasoning have recently demonstrated considerable promise in addressing MINT. On the other hand, VLMs on their own aren’t up to the task of solving MINT because of the following reasons:

1. Many VLMs have a very limited quantity of input photos because of context-length limits. Because of this, an accurate understanding of huge environments is quite limited.
2. Computed robot actions are necessary for solving MINT. The queries used to request these kinds of activities from robots are usually separate from the distribution that VLMs are (pre)trained to handle. Consequently, zero-shot navigation performance could be better. 

To address MINT, the team provides Mobility VLA, a hierarchical Vision-Language-Action (VLA) navigation policy that integrates the knowledge of the surroundings and the ability to reason intuitively from long-context VLMs with a strong low-level navigation policy built on topological networks. The high-level VLM uses the demonstration tour video and multimodal user guidance to locate the desired frame in the tour film. Following this, a conventional low-level policy takes the goal frame and constructs a topological graph offline from the tour frames at each time step. This graph is then used to create robot actions, also called waypoints. The fidelity issue with environment understanding was tackled by employing long-context VLMs, and the topological graph connected the VLM training distribution to the robot actions needed to solve MINT.

The team’s testing of Mobility VLA in a realistic (836m2) office setting and a more residential one yielded promising results. On complex MINT problems requiring intricate thinking, Mobility VLA achieved success rates of 86% and 90%, respectively, which is significantly higher than the baseline techniques. These findings reassure us about the capabilities of Mobility VLA in real-world scenarios.

Rather than exploring its surroundings autonomously, the present version of Mobility VLA depends on a demonstration trip. On the other hand, the demonstration tour provides a great opportunity to incorporate preexisting exploration methods like frontier or diffusion-based exploration.

The researchers highlight that unnatural user interactions are hindered by long VLM inference times. Users have to endure uncomfortable waiting times for robot responses due to the inference time of high-level VLMs, which is approximately 10-30 seconds. Caching the demonstration tour—which uses up around 99.9 percent of the input tokens—can greatly enhance inference speed. 

Given the light onboard compute demand (VLMs run on clouds) and the requirement of only RGB camera observations, Mobility VLA can be implemented on numerous robot incarnations. This potential for widespread deployment of Mobility VLA is a cause for optimism and a step forward in the field of robotics and AI. 

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