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This was referenced Oct 26, 2026
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Description
Add native offline NeRD workflows for Cartpole and flat/rough ANYmal C: collect transitions through recorder terms, fit resumable dynamics from HDF5, and train or play policies through Isaac Lab's normal reinforcement-learning entrypoints.
NeRD consumes the actual applied motor effort at each physics step. Actuators retain position/velocity commands, clipping and internal state. The Newton solver owns learned physics and CUDA-graph state writes; task configurations own capture schemas and recipes;
isaaclab_rl.nerdowns fitting. Rough terrain preserves live contact conditioning, terrain-aware resets and checkpoint validation.Dependency: #8822 provides shared capture and optional batched episode storage. This branch includes that unmerged commit and targets upstream
mainfrom the fork. Review only the NeRD changes. NeRD is independent of the separate SuperMimic/G1 PR #8826.The NeRD guide covers recording, fitting, policy training, logging, checkpoint upload and recovery. Newton solver replacement derives its manager class and removes the corresponding task workarounds. Legacy target-conditioned/incomplete checkpoint contracts remain rejected; no compatibility aliases are added.
Validation
The full warnings-as-errors Sphinx HTML build passed for this branch.
git diff --checkpassed.Dependencies and limits
PhysicsNeMo Robotics is optional. The validated environment uses development revision
a81160efand local attention-accumulation fixee6d2e0, with PhysicsNeMo 2.1.1. Push access to that dependency was denied; safe FP16 fitting still depends on the unpublished fix, and a released compatible package combination remains unresolved.The solver fixes robot parameters/materials while permitting supported static terrain changes through live contacts. Independent moving objects, per-surface materials, external body wrenches and force/joint-wrench sensors remain unsupported. Solver-side motor drives and MuJoCo-specific actuators are rejected when they generate effort beyond the captured boundary. Existing wrench diagnostics and optimized reconstruction kernels remain external experiments; this PR does not advertise sensor accuracy or learned-rollout convergence.
Type of change
Checklist