Interactive Visual Grounding of Referring Expressions for Human-Robot Interaction (1806.03831v1)

Abstract: This paper presents INGRESS, a robot system that follows human natural language instructions to pick and place everyday objects. The core issue here is the grounding of referring expressions: infer objects and their relationships from input images and language expressions. INGRESS allows for unconstrained object categories and unconstrained language expressions. Further, it asks questions to disambiguate referring expressions interactively. To achieve these, we take the approach of grounding by generation and propose a two-stage neural network model for grounding. The first stage uses a neural network to generate visual descriptions of objects, compares them with the input language expression, and identifies a set of candidate objects. The second stage uses another neural network to examine all pairwise relations between the candidates and infers the most likely referred object. The same neural networks are used for both grounding and question generation for disambiguation. Experiments show that INGRESS outperformed a state-of-the-art method on the RefCOCO dataset and in robot experiments with humans.

• The paper introduces INGRESS, a two-stage neural framework that grounds natural language instructions to identify and disambiguate candidate objects.
• It uses LSTM networks for descriptive generation and relational analysis, enhancing precise object recognition in human-robot tasks.
• Empirical tests on RefCOCO and real-world experiments show INGRESS outperforms previous systems in handling ambiguities and complex expressions.

Interactive Visual Grounding of Referring Expressions for Human-Robot Interaction

The paper "Interactive Visual Grounding of Referring Expressions for Human-Robot Interaction" by Mohit Shridhar and David Hsu presents a novel framework, referred to as INGRESS, which aims to enhance human-robot interactions by enabling robots to comprehend and respond to natural language instructions when tasked with object manipulation. The cornerstone of this research is the grounding of referring expressions in visual data, enabling robots to interpret and act upon instructions involving complex, unconstrained language and object categories.

Grounding by Generation Approach

The paper introduces a two-stage neural network architecture that employs a "grounding by generation" strategy, drawing parallels to analysis by synthesis methodologies. The first stage utilizes a neural network to generate descriptions of candidate objects by comparing these descriptions with the input referring expressions, thus identifying potential matches. The second stage extends this capability by examining pairwise relationships between the identified candidates, allowing for relational understanding of the task at hand. Both stages are supported by a neural network architecture that leverages Long Short-Term Memory (LSTM) networks for language understanding and generation tasks.

Robustness and Interaction

An outstanding feature of the proposed system is its capability to interact with users by posing disambiguating questions when multiple potential object matches exist. The system is engineered to generate these queries using the same neural networks employed in the grounding process, ensuring a coherent integration of perception and language understanding. This capability reflects a significant advancement in mitigating ambiguities that arise naturally in human communication, enhancing the efficacy of robotics in collaborative settings.

Empirical Validation

The paper rigorously evaluates INGRESS using the RefCOCO dataset and in real-world experiments involving humans. Notably, INGRESS outperforms prior state-of-the-art systems, such as UMD Refexp, by demonstrating superior accuracy in grounding expressions and facilitating efficient disambiguation through interactive questioning. The evaluation demonstrates INGRESS’s robustness in handling diverse object categories and complex linguistic expressions without explicit pre-programmed constraints.

Implications and Future Directions

By enabling robots to process and understand unconstrained language expressions, this research contributes significantly to the domain of natural language processing and robotics, pushing the boundaries towards achieving superior human-robot collaboration. The approach laid out paves the way for future work to address current limitations such as handling complex relationships beyond binary relations and improving system robustness in cluttered environments. Future research could focus on scaling the relational understanding capabilities and integrating gestures and other non-verbal communication cues to complement linguistic inputs.

Moreover, the paper points towards the necessity of extending this framework to include the grounding of verbs, thereby expanding the repertoire of actions that robots can perform based on natural language instructions. Integrating these advancements will incrementally lead towards a more seamless and intuitive interaction paradigm between humans and robots, ultimately facilitating more sophisticated and adaptable robotic systems in varied operational environments.

In conclusion, the advancements presented in this paper provide a compelling direction for future research aimed at achieving a nuanced and shared understanding between humans and robots, a crucial step for the evolution of intelligent and autonomous robotic systems.