KINESIS: Motion Imitation for Human Musculoskeletal Locomotion
Summary: arXiv:2503.14637v3 Announce Type: replace-cross
Abstract: How do humans move? Advances in reinforcement learning (RL) have produced impressive results in capturing human motion using physics-based humanoid control. However, torque-controlled humanoids fail to model key aspects of human motor control such as biomechanical joint constraints & non-linear and overactuated musculotendon control. We present KINESIS, a model-free motion imitation framework that tackles these challenges. KINESIS is trained on 1.8 hours of locomotion data and achieves strong motion imitation performance on unseen trajectories. Through a negative mining approach, KINESIS learns robust locomotion priors that we leverage to deploy the policy on several downstream tasks such as text-to-control, target point reaching, and football penalty kicks. Importantly, KINESIS learns to generate muscle activity patterns that correlate well with human EMG activity. We show that these results scale seamlessly across biomechanical model complexity, demonstrating control of up to 290 muscles. Overall, the physiological plausibility makes KINESIS a promising model for tackling challenging problems in human motor control. Code, videos and benchmarks are available at https://github.com/amathislab/Kinesis.
Introduction
The field of robotics has made significant strides in recent years, particularly in the area of humanoid motion. The ability to replicate human-like movement has vast implications, from improving robotic assistants to enhancing virtual reality experiences. KINESIS stands out as a groundbreaking framework that aims to accurately imitate human locomotion, bridging the gap between human biomechanics and robotic control.
Challenges in Human Motion Imitation
Current methods in humanoid control often rely on torque-controlled models that struggle to account for the complex nature of human movement. Key challenges include:
- Biomechanical Joint Constraints: Humans have specific limits on joint movement that are often overlooked in standard models.
- Non-linear Musculotendon Control: The behavior of muscles is not linear, making it difficult to replicate with traditional algorithms.
- Overactuation: Many muscles are involved in a single movement, creating an intricate web of control that must be accurately modeled.
The KINESIS Framework
KINESIS approaches these challenges head-on with a model-free motion imitation framework. Key features include:
- Data-Driven Training: Trained on 1.8 hours of locomotion data, KINESIS demonstrates exceptional performance even with unseen trajectories.
- Negative Mining Approach: This technique allows KINESIS to learn robust locomotion priors, enhancing the reliability of its motion output.
- Versatile Application: The framework can be applied to various tasks such as text-to-control and athletic maneuvers like football penalty kicks.
Physiological Plausibility and Future Applications
One of the standout features of KINESIS is its ability to generate muscle activity patterns that closely correlate with human EMG activity. This physiological plausibility is crucial for applications in rehabilitation robotics, sports training, and more. As KINESIS scales across different levels of biomechanical model complexity, it opens doors for further research and development in human motor control.
Conclusion
KINESIS represents a significant advancement in the field of motion imitation and human locomotion modeling. By addressing the limitations of previous models and establishing a more accurate representation of human movement, KINESIS paves the way for innovative applications in robotics and beyond. For those interested in exploring KINESIS further, code, videos, and benchmarks are available on GitHub.