Release Notes#

This release expands ProtoMotions with a public Generative Pretrained Controllers (GPC) workflow, new parameter-efficient fine-tuning examples, refreshed pretrained tracker artifacts, and updated documentation for running and extending the framework.

GPC and PEFT#

  • Add the GPC training stack for discrete-token motor-control pretraining. The workflow trains an FSQ-bottleneck motion tracker, learns an autoregressive prior over grouped latent tokens, and adapts that prior to downstream tasks.

  • Add PEFT agents and model components for adapting frozen discrete priors with SFT, RLFT, and RLFT+AMP. Public examples cover target reaching and steering, each with explicit --prior-checkpoint arguments so users can train or provide the prior they want to adapt.

  • Add reusable FSQ, discrete-latent, autoregressive, frozen-decoder, and adapter utilities under the common agent and PEFT modules.

  • Document the staged GPC workflow in GPC and PEFT, including tracker training, prior training, SFT bootstrapping, and PEFT fine-tuning.

  • Keep the release example set focused: FSQ tracker training, GPC prior training, SFT target adaptation, target RLFT/RLFT+AMP, and steering RLFT/RLFT+AMP.

  • Include the SOMA FSQ tracker used for GPC prior training, and mark pretrained GPC prior and PEFT skill checkpoints as forthcoming.

  • Update checkpoint sidecars so the shipped tracker artifacts run from the packaged inference commands without private paths or stale observation bindings.

Framework Generalization#

  • Generalize supervised imitation and distillation into a reusable agent path so model-specific workflows such as MaskedMimic and GPC SFT can share the same rollout, expert, loss, and checkpoint structure.

  • Add a common fine-tuning lifecycle for agents that need frozen pretrained modules before model construction, keeping trackers, priors, and PEFT adapters on the same explicit pretrained-module contract.

  • Refactor AMP discriminator training into a reusable component so AMP-style reward shaping can compose with fine-tuning agents instead of requiring a separate one-off training loop.

  • Move task behavior into environment components and experiment wiring, so target, steering, mimic, GPC, and deployment examples now follow the same configuration patterns.

Task Control and Inference#

  • Add keyboard-controlled target commands for interactive target-reaching inference, alongside random target sampling for training.

  • Improve quickstart and workflow docs for running pretrained models, using G1 deployment assets, and exporting BeyondMimic-style tracker policies.