Add Isaac Lab integration spike under roboeval/integrations/isaac/

[arryan0112]

Wraps any single-env Isaac Lab environment (gym.make("Isaac-...-v0", num_envs=1)) into the EnvironmentAdapter Protocol so policies can be evaluated against Isaac Lab tasks through EvalRunner. Zero changes to existing SDK files.

The adapter handles three Isaac-specific concerns that distinguish it from the existing Gymnasium adapter:

1. Always-batched envs. Isaac envs subclass gym.vector.VectorEnv and return tensors with a leading batch dimension even at num_envs=1. The adapter slices batch_index=0 (configurable) to expose single-episode semantics to the runner.

2. GPU tensors. Observations come back as torch.Tensor on cuda:0. The tensor_to_numpy helper moves any tensor to CPU and converts to numpy before it reaches the runner's JSON writer.

3. Tensor actions on the env device. Policies typically return numpy arrays or Python scalars; the adapter wraps them in a batched torch tensor on the env's device with the expected (num_envs, action_dim) shape.

Vectorized eval (num_envs > 1 producing N independent rollouts) is explicitly out of scope for this spike. The adapter accepts num_envs > 1 but only reads batch_index=0; a clear warning fires explaining the parallelism cost. True vector eval requires SDK runner changes.

Files added:

• roboeval/integrations/isaac/init.py (public re-exports)
• roboeval/integrations/isaac/adapter.py (adapter + 6 default hooks
  • tensor_to_numpy helper + action shape normalization)
• roboeval/integrations/isaac/demo_rollout.py (Isaac-Cartpole-Direct-v0 manual rollout demo)
• roboeval/integrations/isaac/README.md (usage, mapping table, cloud GPU setup workflow)
• roboeval/integrations/isaac/notes.md (design rationale, 12 known edge cases, real-Isaac validation plan)
• roboeval/integrations/isaac/requirements.txt (Isaac Sim + Isaac Lab install pointers; not a single pip line)
• roboeval/integrations/isaac/tests/init.py
• roboeval/integrations/isaac/tests/test_adapter.py (46 unit + integration tests using mock Isaac envs; runs on Mac, no Isaac install needed)

What's verified by the test suite:

• All 46 tests pass on Mac in 23ms via mocked Isaac envs (real torch tensors, no Isaac dependency)
• Adapter integrates cleanly with EvalRunner + Ruleset
• info["isaac"] namespace preserved through to StepRecord
• Rich env info flows through to_serializable + JSON write
• Hook overrides work (outcome, action, info_keys, etc.)
• Multi-episode reset behavior
• Action shape normalization across 4 input shapes (scalar, 1-D array matching action_dim, 1-D with batch shape, pre-batched 2-D tensor)

What's NOT verified (requires cloud GPU pass):

• Real Isaac-Cartpole-Direct-v0 smoke test
• GPU->CPU tensor sync against actual cuda:0 tensors
• env.device / env.sim_device attribute resolution on real Isaac
• env.reset(options=...) acceptance across Isaac task variants

See notes.md section 12 for the cloud GPU validation plan.

Validate:
python -m unittest discover -s roboeval/integrations/isaac/tests