Representation or Objective? Synergy Action Spaces for Human-Like Dexterous Control

Learning dexterous motor control over redundant musculoskeletal systems promises biologically grounded behavior. Still, reinforcement learning tends to discover non-human, high-effort activations. On a dexterous manipulation task, standard policy search learns to complete the task by de-humanizing its muscle use, roughly doubling effort and drifting off the human activation manifold as performance rises. A natural remedy is to constrain where the policy may act by confining it to a low-dimensional coordination manifold, a muscle-synergy action space. We ask whether this suffices to recover human-like dexterous control, and whether the source of the manifold matters, comparing synergies derived from human motion capture, system dynamics, and learned predictive and generative models. We find that a coordination bottleneck restores human-like muscle use in aggregate, and that different sources buy different strengths in task reliability, zero-shot transfer, or coordination, but no action-space prior recovers human-like control at the level of individual movements. Constraining where actions may live does not determine which the policy picks. What does is the objective: a behavioral imitation reward recovers per-movement human-likeness even with no coordination prior at all. We conclude that human-like dexterous motor control is a property of the objective, not the action space, and release the benchmark, synergy bases, and human-data pipeline.