Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space

Core Concepts

Our method constructs a shared latent space between human and robot poses to enable unsupervised human-to-robot motion retargeting, allowing robots to mimic human motions accurately and efficiently.

Abstract

The paper introduces a novel deep learning approach for human-to-robot motion retargeting that does not require paired human-robot data. The key aspects are:

Construction of a shared latent space between human and robot poses using adaptive contrastive learning and a proposed cross-domain similarity metric based on global limb rotations. This allows the model to learn the retargeting function in an unsupervised manner.

Incorporation of a reconstruction loss and a latent consistency term to ensure the shared latent space can be effectively decoded to generate robot control commands that faithfully reproduce the human motion.

Evaluation on a real TiaGo++ robot with a whole-body controller that ensures self-collision avoidance, demonstrating the effectiveness of the approach in controlling the robot to mimic diverse human motions from various modalities (text, video, key poses).

Comprehensive quantitative and qualitative results showing the proposed method outperforms prior works in terms of retargeting accuracy and computational efficiency, enabling real-time robot control at 1.5 kHz.

Ablation studies highlighting the importance of the contrastive loss in constructing the shared latent space for effective motion retargeting.

Extensions showcasing the versatility of the approach in translating human motions from text, video, and key poses to robot control.

Stats

Our method can control the robot at a rate of 1.5 kHz.
The Mean Square Error (MSE) of joint angles between ground truth and predicted results is 0.21, outperforming the baseline method (0.44).

Quotes

"Contrary to prior deep-learning-based works, our method does not require paired human-to-robot data, which facilitates its translation to new robots."
"Our model outperforms existing works regarding human-to-robot retargeting in terms of efficiency and precision."

Key Insights Distilled From

ImitationNet

by Yashuai Yan,... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2309.05310.pdf

Deeper Inquiries

How can the proposed shared latent space representation be further extended to capture higher-level semantic information about the human motions, such as the underlying intent or emotion

To extend the shared latent space representation to capture higher-level semantic information about human motions, such as underlying intent or emotion, additional layers of abstraction can be incorporated into the model. By integrating techniques from natural language processing (NLP) or affective computing, the model can learn to associate specific motion patterns with corresponding emotional states or intentions. For example, sentiment analysis algorithms could be employed to infer the emotional context of the human motions, allowing the robot to mimic not just the physical movements but also the emotional nuances conveyed by the gestures. This would involve training the model on a dataset that includes annotations or labels indicating the emotional content or intent behind each motion, enabling the shared latent space to encode this higher-level semantic information.

What are the potential challenges and limitations of the current approach in handling more complex human-robot kinematic differences, such as significant variations in the number of degrees of freedom or limb proportions

The current approach may face challenges and limitations when dealing with more complex human-robot kinematic differences, especially in cases of significant variations in the number of degrees of freedom or limb proportions. One potential challenge is the scalability of the model to accommodate a wide range of kinematic configurations, as the shared latent space may struggle to generalize effectively across vastly different anatomies. Additionally, handling variations in limb proportions could lead to distortions or inaccuracies in the retargeted motions, as the model may not adequately account for the differences in limb lengths or joint ranges of motion. Ensuring robustness and adaptability to diverse kinematic structures would require extensive training data covering a wide spectrum of anatomical variations and sophisticated regularization techniques to prevent overfitting to specific configurations.

Could the unsupervised learning framework be adapted to incorporate additional modalities, such as audio or environmental context, to enhance the naturalness and situational awareness of the retargeted robot motions

Adapting the unsupervised learning framework to incorporate additional modalities, such as audio or environmental context, can significantly enhance the naturalness and situational awareness of the retargeted robot motions. By integrating audio input, the model could learn to associate specific sound patterns or cues with corresponding motion sequences, enabling the robot to respond to auditory commands or environmental stimuli with appropriate movements. Environmental context information, such as object locations or spatial constraints, could be integrated into the model to influence the generation of robot motions in real-time, ensuring that the robot adapts its movements based on the surrounding context. This multi-modal approach would require a more complex input pipeline and potentially a more sophisticated neural network architecture capable of processing and fusing information from diverse sources effectively.