FreeMusco: Motion-Free Learning of Latent Control for Morphology-Adaptive Locomotion in Musculoskeletal Characters

Abstract

We propose FreeMusco, a motion-free framework that jointly learns latent representations and control policies for musculoskeletal characters. By leveraging the musculoskeletal model as a strong prior, our method enables energy-aware and morphology-adaptive locomotion to emerge without motion data. The framework generalizes across human, non-human, and synthetic morphologies, where distinct energy-efficient strategies naturally appear—for example, quadrupedal gaits in Chimanoid versus bipedal gaits in Humanoid. The latent space and corresponding control policy are constructed from scratch, without demonstration, and enable downstream tasks such as goal navigation and path following—representing, to our knowledge, the first motion-free method to provide such capabilities. FreeMusco learns diverse and physically plausible locomotion behaviors through model-based reinforcement learning, guided by the locomotion objective that combines control, balancing, and biomechanical terms. To better capture the periodic structure of natural gait, we introduce a temporally averaged loss formulation, which compares simulated and target states over a time window rather than on a per-frame basis. We further encourage behavioral diversity by randomizing target poses and energy levels during training, enabling locomotion to be flexibly modulated in both form and intensity at runtime. Together, these results demonstrate that versatile and adaptive locomotion control can emerge without motion capture, offering a new direction for simulating movement in characters where data collection is impractical or impossible.

Chimanoid: Goal Velocity-Only

Ostrich: Goal Velocity-Only

Ostrich: Goal Velocity + Pose + Energy

Humanoid Latent Space
Trained with Velocity-Only Goals

Humanoid Latent Space
Trained with Velocity + Pose + Energy Goals

Chimanoid Latent Space
Trained with Velocity-Only Goals

Ostrich Latent Space
Trained with Velocity + Pose + Energy Goals

Humanoid

Chimanoid

Muscle-Actuated (Ours)

Torque-Actuated

Torque-Actuated
(with manually tuned torque limits)

Torque-actuated humanoid failed to learn even basic balanced walking behavior. Even with manual tuning of joint torque limits, it only learned a very short-stepped, high-frequency locomotion.