Google DeepMind researchers present Mobility VLA: Multimodal instruction navigation with long-context VLMs and topological graphs

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

MIN covers a wide range of activities, including exploring the surroundings and following navigation instructions. However, the use of a demonstration film covering the entire region allows exploration to be avoided altogether.

A Google DeepMind study introduces and investigates a class of tasks called Multimodal Instructional Navigation with Tours (MINT). MINT uses demonstration tours and deals with carrying out multimodal instructions to the user. The remarkable capabilities of massive vision and language models (VLMs) in language and image interpretation and common sense reasoning have recently shown great promise in tackling MINT. On the other hand, VLMs alone are not up to the task of solving MINT for the following reasons:

1. Many VLMs have a very limited number of input photos due to context length limits. For this reason, accurate understanding of huge environments is quite limited.
2. Computed robot actions are necessary to solve MINT. The queries used to request such activities from robots are typically independent of the distribution that VLMs are (pre)trained to handle. Consequently, the performance of shotless navigation could be improved.

To address the MINT problem, the team provides Mobility VLA, a hierarchical Vision-Language-Action (VLA) navigation policy that integrates environment awareness and the ability to intuitively reason from long-context VLMs with a robust low-level navigation policy built on topological networks. The high-level VLM uses the demonstration tour video and the user’s multimodal guidance to locate the desired frame in the tour movie. A conventional low-level policy then takes the target frame and builds an offline topological graph from the tour frames at each time step. This graph is then used to create robot actions, also called waypoints. The fidelity problem with environment understanding was addressed by employing long-context VLMs, and the topological graph connected the VLM training distribution to the robot actions required to solve the MINT problem.

Tests conducted by the Mobility VLA team in a realistic office environment (836 m2) 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 baseline techniques. These findings reassure us about Mobility VLA’s capabilities in real-world scenarios.

Instead of exploring its surroundings autonomously, the current version of Mobility VLA relies on a demonstration ride. Moreover, the demonstration ride offers a great opportunity to incorporate pre-existing exploration methods, such as boundary exploration or diffusion-based exploration.

The researchers highlight that unnatural user interactions are hampered by VLMs’ long inference times. Users have to endure awkward waiting times for responses from robots due to the inference time of high-level VLMs, which is approximately 10 to 30 seconds. Caching the demo walkthrough, which uses about 99.9 percent of the input tokens, can greatly improve the inference speed.

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

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