Unified Human-Scene Interaction via Prompted Chain-of-Contacts

Core Concepts

UniHSI, a unified framework for human-scene interaction, supports diverse interactions through language commands by translating them into a structured Chain-of-Contacts representation and executing them with a versatile controller.

Abstract

The paper presents UniHSI, a unified framework for human-scene interaction (HSI) that supports versatile interaction control through language commands. The key contributions are: Interaction Formulation: UniHSI defines interaction as a "Chain of Contacts (CoC)", which represents the steps involving human joint-object part pairs. This structured formulation enables the alignment of language commands with precise interaction execution. LLM Planner: UniHSI leverages large language models (LLMs) to translate language commands into task plans in the form of CoC, harnessing prompt engineering techniques. Unified Controller: UniHSI's Unified Controller models whole-body joints and arbitrary object parts to enable fine-granularity control and multi-object interaction. It evaluates the completion of current steps and sequentially fetches the next step, enabling multi-round and long-horizon transition control. Annotation-free Training: UniHSI's training is interaction annotation-free, as it leverages the interaction knowledge of LLMs to generate interaction plans, significantly reducing the annotation requirements. The paper also introduces a novel dataset, ScenePlan, which encompasses thousands of interaction plans based on scenarios constructed from PartNet and ScanNet datasets. Comprehensive experiments on ScenePlan demonstrate the effectiveness of UniHSI in versatile interaction control and good generalizability on real scanned scenarios.

Stats

The average error of all contact pairs is defined as: ContactError = Σi,ci≠0 eri / Σi,ci≠0 1, where eri = ||dk||, if ci = contact eri = min(0.3 - ||dk||, 0), if ci = not contact

Quotes

"Interaction itself contains a strong prior in the form of human-object contact regions." "We formulate interaction as ordered sequences of human joint-object part contact pairs, which we refer to as Chain of Contacts (CoC)." "UniHSI constitutes a Large Language Model (LLM) Planner to translate language prompts into task plans in the form of CoC, and a Unified Controller that turns CoC into uniform task execution."

Key Insights Distilled From

Unified Human-Scene Interaction via Prompted Chain-of-Contacts

by Zeqi Xiao,Ta... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2309.07918.pdf

Deeper Inquiries

How can UniHSI be extended to support interactions with movable objects?

To extend UniHSI to support interactions with movable objects, several key modifications and additions can be implemented: Dynamic Object Handling: The framework can be enhanced to recognize and interact with objects that can move or be manipulated. This would involve incorporating physics-based simulations to enable realistic interactions with objects that can be pushed, pulled, or manipulated in various ways. Object Tracking: Implementing object tracking algorithms can help the system keep track of movable objects within the environment. This would enable the humanoid agent to interact with objects that change position or orientation dynamically. Adaptive Planning: The planning module of UniHSI can be adapted to dynamically adjust interaction plans based on the movement of objects. This would involve real-time monitoring of object positions and updating the interaction sequences accordingly. Collaborative Interactions: Introducing collaborative interactions where the humanoid agent can interact with other agents or entities that can move. This would require coordination and communication between multiple agents to achieve common goals. By incorporating these enhancements, UniHSI can effectively support interactions with movable objects, adding a new dimension of realism and complexity to the framework.

How can the integration of LLMs be further improved to make the entire framework more scalable and seamless?

To enhance the integration of Large Language Models (LLMs) within UniHSI and make the framework more scalable and seamless, the following strategies can be implemented: Fine-tuning and Transfer Learning: Continuously fine-tuning the LLMs on interaction data generated by the framework can improve their understanding of complex interactions. Additionally, leveraging transfer learning techniques to adapt pre-trained LLMs to the specific domain of human-scene interactions can enhance their performance. Multi-Modal Inputs: Integrating multi-modal inputs, such as visual data from the environment, can provide additional context for the LLMs to generate more accurate and context-aware interaction plans. This can improve the overall performance and scalability of the framework. Interactive Learning: Implementing interactive learning mechanisms where the LLMs can receive feedback from the environment or users during the planning process can help refine the generated plans in real-time. This interactive feedback loop can improve the adaptability and scalability of the framework. Efficient Prompt Engineering: Refining the prompt engineering techniques to provide more structured and informative prompts to the LLMs can enhance their ability to generate coherent and realistic interaction plans. This can streamline the planning process and make it more efficient. By implementing these strategies, the integration of LLMs within UniHSI can be further improved, making the framework more scalable, adaptable, and seamless in generating diverse and complex interaction plans.

What are the potential applications of UniHSI beyond embodied AI and virtual reality, and how can it be adapted to those domains?

UniHSI has the potential to be applied in various domains beyond embodied AI and virtual reality, including: Robotics: UniHSI can be adapted for robotic applications, enabling robots to interact with objects and environments in a more human-like and intuitive manner. This can be useful in tasks such as object manipulation, assembly, and navigation. Assistive Technology: The framework can be utilized in assistive technology to help individuals with disabilities or mobility issues interact with their surroundings more effectively. This can include tasks like operating smart home devices, controlling robotic prosthetics, or navigating assistive robots. Gaming and Entertainment: UniHSI can be integrated into gaming and entertainment systems to create more immersive and interactive experiences for users. This can involve controlling virtual characters in games, interacting with virtual environments, and performing complex actions based on natural language commands. Healthcare: In healthcare settings, UniHSI can be used for training medical robots, assisting in physical therapy exercises, or simulating patient interactions for medical training purposes. The framework can facilitate more realistic and interactive simulations in healthcare scenarios. By adapting UniHSI to these domains, it can revolutionize the way interactions are designed and executed, leading to more intuitive, natural, and efficient human-machine interactions across a wide range of applications.