Harmon: Whole-Body Motion Generation of Humanoid Robots from Language Descriptions (2410.12773v1)

Abstract: Humanoid robots, with their human-like embodiment, have the potential to integrate seamlessly into human environments. Critical to their coexistence and cooperation with humans is the ability to understand natural language communications and exhibit human-like behaviors. This work focuses on generating diverse whole-body motions for humanoid robots from language descriptions. We leverage human motion priors from extensive human motion datasets to initialize humanoid motions and employ the commonsense reasoning capabilities of Vision LLMs (VLMs) to edit and refine these motions. Our approach demonstrates the capability to produce natural, expressive, and text-aligned humanoid motions, validated through both simulated and real-world experiments. More videos can be found at https://ut-austin-rpl.github.io/Harmon/.

• The paper introduces Harmon, a diffusion-based generative model that transforms textual descriptions into humanoid motions using human motion priors and inverse kinematics.
• It refines motion quality through Vision Language Models to capture expressive hand and head movements for enhanced action fidelity.
• Experimental evaluations show an 81.2% alignment between generated motions and text descriptions, underscoring its potential for intuitive human-robot interactions.

Overview of Harmon: Language-Driven Motion Generation for Humanoid Robots

The paper "Harmon: Whole-Body Motion Generation of Humanoid Robots from Language Descriptions" presents an advanced framework for generating humanoid robot motions based on natural language inputs. The authors aim to bridge the gap between textual descriptions and robotic motion execution by leveraging human motion data and Vision LLMs (VLMs).

Core Methodology

Harmon, the proposed system, utilizes human motion priors to initiate humanoid robot actions. It capitalizes on extensive human motion datasets, applying a diffusion-based generative model named PhysDiff, to convert textual descriptions into plausible human motions. These motions are then retargeted to humanoid robots via inverse kinematics, translating the SMPL parameters into robot joint configurations.

One of the key components of Harmon is its use of VLMs to refine and enhance motion quality. The VLMs generate expressive components of movement that the initial modeling may miss, such as detailed hand and head movements. This is achieved through an iterative process where VLMs assess and adjust the humanoid motion to ensure alignment with the text description.

Experimental Findings

The paper employs a comprehensive evaluation framework to compare Harmon with several baselines:

1. VLM-Based Motion Generation: This baseline, devoid of motion priors, showcases the importance of leveraging human motion data for initializing complex actions.
2. Human Motion Retargeting: By directly using retargeted human motions, this baseline helps evaluate the impact of VLMs' iterative adjustments.
3. Harmon without Head or Finger Movements: This variant highlights the significance of incorporating expressive body parts for more comprehensive humanoid motions.

Through human studies, Harmon demonstrated superior performance with an impressive 81.2% alignment score between generated motions and textual descriptions. This underscores the efficacy of integrating human motion priors with VLM-based refinement.

Implications and Future Directions

The paper suggests several theoretical and practical implications for AI and robotics. From a theoretical perspective, it demonstrates the potential for combining learning from vast human datasets with advanced LLMs to create more sophisticated and adaptable robotic systems. Practically, Harmon could enable more intuitive human-robot interactions, essential for scenarios where robots need to operate in human-centric environments.

The authors also point towards limitations in current methodologies, particularly in balancing upper and lower body coordination during real-world robot deployments. They suggest future exploration into more dynamic control mechanisms, such as reinforcement learning, to enhance the adaptability and robustness of humanoid motion execution.

Conclusion

Harmon's approach provides a robust framework for converting language into precise and expressive humanoid robot actions. By integrating human motion priors with VLMs, this work paves the way for enhanced human-robot interaction capabilities, indicating a promising avenue for future research and development in AI-driven robotics.