SGL: Symbolic Goal Learning in a Hybrid, Modular Framework for Human Instruction Following

TL;DR

This paper presents a hybrid framework combining symbolic and neural methods for robot instruction following, improving flexibility and robustness in ambiguous human requests through pretraining and modular design.

Contribution

It introduces a hybrid symbolic-neural approach that integrates deep learning with symbolic planning for improved instruction following in robotics.

Findings

Pretraining vision and language encoders enhances performance.

The framework demonstrates robustness in novel scenarios in simulation.

Hybrid approach outperforms purely neural or symbolic methods.

Abstract

This paper investigates robot manipulation based on human instruction with ambiguous requests. The intent is to compensate for imperfect natural language via visual observations. Early symbolic methods, based on manually defined symbols, built modular framework consist of semantic parsing and task planning for producing sequences of actions from natural language requests. Modern connectionist methods employ deep neural networks to automatically learn visual and linguistic features and map to a sequence of low-level actions, in an endto-end fashion. These two approaches are blended to create a hybrid, modular framework: it formulates instruction following as symbolic goal learning via deep neural networks followed by task planning via symbolic planners. Connectionist and symbolic modules are bridged with Planning Domain Definition Language. The vision-and-language learning network predicts its goal representation, which is sent to a planner for producing a task-completing action sequence. For improving the flexibility of natural language, we further incorporate implicit human intents with explicit human instructions. To learn generic features for vision and language, we propose to separately pretrain vision and language encoders on scene graph parsing and semantic textual similarity tasks. Benchmarking evaluates the impacts of different components of, or options for, the vision-and-language learning model and shows the effectiveness of pretraining strategies. Manipulation experiments conducted in the simulator AI2THOR show the robustness of the framework to novel scenarios.