Beyond Sight: Finetuning Generalist Robot Policies with Heterogeneous Sensors via Language Grounding

TL;DR

FuSe is a novel method that fine-tunes generalist robot policies across multiple sensory modalities using language grounding, enabling complex multimodal reasoning and manipulation in real-world scenarios with significant success improvements.

Contribution

Introduces FuSe, a new approach combining contrastive and language grounding losses to adapt generalist robot policies to heterogeneous sensors without large datasets.

Findings

Enables zero-shot multimodal reasoning in robot manipulation

Increases success rates by over 20% in real-world tests

Works with diverse generalist policies including diffusion-based and VLA models

Abstract

Interacting with the world is a multi-sensory experience: achieving effective general-purpose interaction requires making use of all available modalities -- including vision, touch, and audio -- to fill in gaps from partial observation. For example, when vision is occluded reaching into a bag, a robot should rely on its senses of touch and sound. However, state-of-the-art generalist robot policies are typically trained on large datasets to predict robot actions solely from visual and proprioceptive observations. In this work, we propose FuSe, a novel approach that enables finetuning visuomotor generalist policies on heterogeneous sensor modalities for which large datasets are not readily available by leveraging natural language as a common cross-modal grounding. We combine a multimodal contrastive loss with a sensory-grounded language generation loss to encode high-level semantics. In the context of robot manipulation, we show that FuSe enables performing challenging tasks that require reasoning jointly over modalities such as vision, touch, and sound in a zero-shot setting, such as multimodal prompting, compositional cross-modal prompting, and descriptions of objects it interacts with. We show that the same recipe is applicable to widely different generalist policies, including both diffusion-based generalist policies and large vision-language-action (VLA) models. Extensive experiments in the real world show that FuSeis able to increase success rates by over 20% compared to all considered baselines.