Adaptive sampling (RSI)

Reference-state initialization (RSI) decides where in a motion clip each training episode begins. wbc-mjlab uses adaptive sampling: the clip timeline is split into bins; bins that correlate with failure are sampled more often on later resets, so the policy spends more time on hard segments.

Why it matters

Whole-body tracking on motion libraries spans many skills and contact phases. Uniform random starts waste steps on easy frames; adaptive RSI concentrates training on regions where the policy falls or terminates early.

At a high level:

Episode starts at bin B of clip T
     ↓
Rollout until termination or horizon
     ↓
Measure failure signal (strategy-specific)
     ↓
Update bin failure EMA for (T, B)
     ↓
Next reset: sample (T', B') ∝ failure weight + exploration floor

Failure signals (strategies)

Strategy	Failure means…
similarity_ema	Low tracking similarity over the episode (reward-aligned in WBC/Zest presets)
binary_failure	Episode ended early before timeout (BeyondMimic-style preset)

Presets choose the strategy and whether similarity follows hand-tuned kernels or the same ``motion_*`` rewards as training (similarity_from_rewards=True).

Connection to training

• Assistive wrench — bin failure levels can scale curriculum assist on the anchor body (harder bins → more help early in training).

• Persistence — WBC/Zest presets can save rsi_bin_stats.npz; resume (--agent.resume True) restores bin EMA so curriculum continues across runs.

• Visualization — Viser play shows per-clip bin strips during eval — see Visualization (Viser).

Multi-clip training

Each parallel env can be on a different clip. RSI maintains per-(trajectory, bin) statistics, so adaptive sampling works across an entire library, not one motion file.

Implementation reference

RsiCfg fields, motion-command hook, and kernel details: RSI (reference-state initialization). Motion playback and resampling: Motion command.