[Fix] Make h1_locomotion demo runnable on main: migrate deprecated RSL-RL config and checkpoint

[hujc7]

1. Summary

• Fixes NVBug 6120938 (P0): ./isaaclab.sh -p scripts/demos/h1_locomotion.py crashes on main (Isaac Sim 5.1.0, rsl-rl-lib==5.0.1). Two sequential incompatibilities with rsl-rl 5.x; both fixed here.
• Crash 1 — KeyError: 'class_name' at OnPolicyRunner construction: the H1 agent config uses the deprecated policy field; rsl-rl 5.x expects per-model actor/critic configs. Fixed by handle_deprecated_rsl_rl_cfg in the demo (as train.py/play.py already do).
• Crash 2 — KeyError: 'actor_state_dict' at runner.load(): the published H1 checkpoint (Isaac 5.1 assets) was saved in the pre-5.0 layout (single combined model_state_dict), while rsl-rl 5.x expects separate actor_state_dict/critic_state_dict. Fixed by a new handle_deprecated_rsl_rl_checkpoint helper that remaps the old layout, applied before loading.

2. Why both are needed

The cfg migration alone unblocks construction but the demo then fails loading the old-format published checkpoint. develop's demo avoids the second crash only because its newer (Isaac 6.0) assets ship the checkpoint in the new layout — there is no conversion code there. On main (5.1 assets) the checkpoint must be converted.

3. Changes

• source/isaaclab_rl/isaaclab_rl/rsl_rl/utils.py: add handle_deprecated_rsl_rl_checkpoint — remaps actor.N→actor_state_dict.mlp.N, critic.N→critic_state_dict.mlp.N, std→actor_state_dict.distribution.std_param; new-layout checkpoints and rsl-rl < 5.0 pass through unchanged; raises on unrecognized keys.
• scripts/demos/h1_locomotion.py: migrate the agent config, then convert the checkpoint before ppo_runner.load.
• scripts/reinforcement_learning/rsl_rl/play.py: apply the same converter to the resolved checkpoint before runner.load (covers --use_pretrained_checkpoint, which loads the same old published checkpoint; no-op for locally-trained checkpoints).
• source/isaaclab_rl/test/test_rsl_rl_cfg_deprecation.py: unit tests for the converter.
• isaaclab_rl version bump + changelog.

4. Verification

In an Isaac Sim 5.1.0 + rsl-rl-lib==5.0.1 container (the demo's GUI viewport needs a display the CI host lacks; the bug is upstream of rendering, so validated headless on the real code paths):

• Demo, unmodified → KeyError: 'class_name'; cfg-only → KeyError: 'actor_state_dict'; with both fixes → the real h1_locomotion module's __init__ completes (config migrates, checkpoint converts via strict-matching load_state_dict, runner.load succeeds) and the policy steps with finite joint targets.
• play.py --use_pretrained_checkpoint: unmodified → KeyError: 'actor_state_dict' at runner.load; with the converter → checkpoint converts, loads, and the policy runs.
• Converter unit tests: 4 passed. ./isaaclab.sh -f clean.

5. Test plan

• Container repro of both demo crashes (unmodified / cfg-only).
• Real h1_locomotion module runs headless end-to-end with both fixes (init completes + inference).
• play.py --use_pretrained_checkpoint FAIL→PASS with the converter.
• Converter unit tests + pre-commit clean.

[isaaclab-review-bot]

🤖 Isaac Lab Review Bot — PR #5903 (Updated)

📋 Summary

This PR now addresses both deprecation issues that arise when running h1_locomotion.py with rsl-rl >= 5.0:

1. Config migration (original fix): Converts deprecated policy field into actor/critic configs via handle_deprecated_rsl_rl_cfg.
2. Checkpoint migration (new): Converts pre-5.0 combined model_state_dict checkpoints into the split actor_state_dict/critic_state_dict layout via the new handle_deprecated_rsl_rl_checkpoint.

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🔬 Isaac Lab Expert

Verdict: ✅ Well-implemented checkpoint migration

The new handle_deprecated_rsl_rl_checkpoint() in isaaclab_rl/rsl_rl/utils.py:

• Correctly gates on rsl-rl version (only converts when >= 5.0)
• Detects old layout by checking for model_state_dict key and absence of actor_state_dict
• Remaps keys properly: actor.* → mlp.* in actor dict, critic.* → mlp.* in critic dict, std → distribution.std_param
• Raises ValueError for unrecognized keys (defensive, avoids silent corruption)
• Writes converted checkpoint to a sibling file (_rslrl5 suffix) rather than mutating the original
• Carries optimizer_state_dict, iter, and infos for completeness

The demo script integration is clean — calls handle_deprecated_rsl_rl_checkpoint(checkpoint, metadata.version("rsl-rl-lib")) right after fetching the published checkpoint, before the runner uses it.

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🧪 Test Coverage Analyzer

Excellent test coverage added. TestHandleDeprecatedRslRlCheckpoint covers:

1. ✅ Converts combined state dict with correct key remapping and value preservation
2. ✅ Passes through new-layout checkpoints untouched
3. ✅ Passes through when rsl-rl < 5.0 (no conversion needed)
4. ✅ Raises ValueError on unrecognized keys

All test cases use tmp_path fixture properly and test both the path return value and the loaded content.

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📦 Release Hygiene

• Version bump to 0.5.2 ✅
• CHANGELOG entry with clear description ✅
• __init__.py exports the new public function ✅

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🔍 Minor Observations (non-blocking)

1. The weights_only=False in torch.load is intentional (rsl-rl checkpoints may contain non-tensor objects). This matches the pattern used by rsl-rl's own OnPolicyRunner.load().
2. The conversion only supports MLP actor-critic architectures (documented in the ValueError message). RNN/Transformer checkpoints would need separate handling, but this matches the scope of the H1 demo which uses MLP.

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🏁 Final Verdict: LGTM ✅

Comprehensive fix that addresses the full stack of deprecation issues (config + checkpoint). Clean implementation, good error handling, proper tests, correct release hygiene. Ready to merge.

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📝 Update (bf96706): New commit extends handle_deprecated_rsl_rl_checkpoint to scripts/reinforcement_learning/rsl_rl/play.py. This is correct — play.py --use_pretrained_checkpoint loads published (pre-5.0) checkpoints through the same OnPolicyRunner.load path and would hit the same KeyError: 'actor_state_dict'. The fix imports the converter and applies it to resume_path just before runner.load(), with a no-op guard for new-layout checkpoints. Consistent with the pattern already used in the demo script. Previous assessment stands — LGTM ✅.

📝 Update (5f1d13d): Branch merged with main — brings in the daqp 0.8.5 pin bump (#5902, version 0.54.4, CHANGELOG, setup.py). These changes are from an unrelated already-merged PR and do not affect this PR's logic. No new concerns — LGTM ✅.

[greptile-apps]

Greptile Summary

This PR fixes a crash in the H1 locomotion demo caused by the H1RoughPPORunnerCfg using the deprecated policy field from rsl-rl 4.x, which OnPolicyRunner in rsl-rl 5.x no longer accepts — raising a KeyError: 'class_name' when it tries to pop that key from the absent actor dict.

• Adds handle_deprecated_rsl_rl_cfg(agent_cfg, metadata.version("rsl-rl-lib")) immediately after parse_rsl_rl_cfg, exactly mirroring the steps already performed in scripts/reinforcement_learning/rsl_rl/train.py and play.py.
• The import style (from importlib import metadata) differs slightly from train.py/play.py (import importlib.metadata as metadata) but is functionally identical.
• No logic changes beyond the migration call; the fix is minimal and well-scoped to the demo.

Confidence Score: 5/5

Safe to merge — the change is a single, well-understood migration call that already works correctly in train.py and play.py.

The fix is minimal (3 lines), directly mirrors tested patterns from the sibling training and play scripts, and handle_deprecated_rsl_rl_cfg has dedicated unit tests for rsl-rl 5.x migration. No new logic is introduced and no existing behavior is altered.

No files require special attention.

Important Files Changed

Filename	Overview
scripts/demos/h1_locomotion.py	Adds handle_deprecated_rsl_rl_cfg call after config parsing to migrate the legacy policy field into actor/critic sub-configs, matching the pattern already used by train.py and play.py; fixes the KeyError crash with rsl-rl-lib 5.x.

Sequence Diagram

sequenceDiagram
    participant Script as h1_locomotion.py
    participant CLI as cli_args
    participant Handle as handle_deprecated_rsl_rl_cfg
    participant Runner as OnPolicyRunner (rsl-rl 5.x)

    Script->>CLI: parse_rsl_rl_cfg(TASK, args_cli)
    CLI-->>Script: agent_cfg (legacy policy field)
    Note over Script: NEW: migrate config
    Script->>Handle: handle_deprecated_rsl_rl_cfg(agent_cfg, "5.0.1")
    Handle-->>Script: agent_cfg (actor + critic with class_name)
    Script->>Runner: OnPolicyRunner(env, agent_cfg.to_dict(), ...)
    Runner-->>Script: ppo_runner (no KeyError)

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