Learning to Act with Affordance-Aware Multimodal Neural SLAM

Abstract

Recent years have witnessed an emerging paradigm shift toward embodied artificial intelligence, in which an agent must learn to solve challenging tasks by interacting with its environment. There are several challenges in solving embodied multimodal tasks, including long-horizon planning, vision-and-language grounding, and efficient exploration. We focus on a critical bottleneck, namely the performance of planning and navigation. To tackle this challenge, we propose a Neural SLAM approach that, for the first time, utilizes several modalities for exploration, predicts an affordance-aware semantic map, and plans over it at the same time. This significantly improves exploration efficiency, leads to robust long-horizon planning, and enables effective vision-and-language grounding. With the proposed Affordance-aware Multimodal Neural SLAM (AMSLAM) approach, we obtain more than 40% improvement over prior published work on the ALFRED benchmark and set a new state-of-the-art generalization performance at a success rate of 23.48% on the test unseen scenes.

Multimodal Embodied Plan Prediction Augmented with Synthetic Embodied Dialogue

“…In simulated environments, Logeswaran et al (2022) propose a language-only finetuned GPT-2 model for task planning on ALFRED . Some end-to-end ALFRED models also have task planning as a component (Min et al, 2021;Jia et al, 2022;Blukis et al, 2022). However, this is a simpler dataset where task planning can be cast as a 7-way classification problem.…”

“…In such a system, the coffee task considered above would likely start by invoking a semantic navigation module to find the mug and a grasping module to pick it up. Some prior work has been on embodied AI benchmarks suggesting that more modular models can outperform monolithic models (Min et al, 2021;Jia et al, 2022;Zheng et al, 2022;Min et al, 2022). However, these do not evaluate and explore the limitations of individual modules.…”