# Simulation

Isaac Lab / Isaac Sim workspace for v2d.

This tree is **self-contained**. Scripts under `simulation/scripts/` must not import
Dream or other project trees. The layout matches `reconstruction/`.

## Layout

```
simulation/
├── run_isaaclab.sh
├── setup.sh                 # isolated venv (no conda)
├── config/paths.sh          # data + modules; uses simulation/.venv
├── scripts/                 # first-party stages
├── setup/
│   ├── 00_init_modules.sh   # clone IsaacLab if missing
│   └── 02_relocate_venv.sh  # rewrite paths after a move
├── modules/IsaacLab/        # Isaac Lab source
└── runs/                    # outputs (gitignored)
```

The existing Dream venv (`env_isaaclab`) lives here as `simulation/.venv`.
Isaac Lab source lives in `simulation/modules/IsaacLab`.

## Setup

One isolated venv under `simulation/.venv`. No conda.

```bash
cd simulation
./setup.sh
source .venv/bin/activate
```

Isaac Sim 6.0.1 and Isaac Lab 3.0.0-beta2 are already installed in `.venv`
(Python 3.12). `./setup.sh` only clones Isaac Lab if `modules/IsaacLab` is
missing, then rewrites venv shebangs / editable install paths.

## Run

```bash
cd simulation
source .venv/bin/activate
./run_isaaclab.sh --help
```

## Replay G1 Inspire + object + MANO

Same idea as NVIDIA `robotic_grounding/scripts/replay_motion.py`: teleport a
floating Inspire hand and the tracked object each physics step (no contact
forces). Data comes from `retarget/export_isaaclab.sh` (Z-up `isaaclab_replay.npz`),
not from `motion_v1` parquet.

```bash
cd retarget
./export_isaaclab.sh --video-dir ../reconstruction/runs/20200709_141754_836212060125

cd ../simulation
source .venv/bin/activate
./replay_v2d.sh --npz ../reconstruction/runs/20200709_141754_836212060125/obj_tracking_out/isaaclab_replay.npz --headless
```

Plays once: physics **settle** (object XY fixed, Z/rotation fall onto the table;
hand stays glued to the object as in the video), then the clip, then writes
`obj_tracking_out/isaaclab_replay.mp4` and exits.

`--table-z` (default 0.75 m), `--settle-sec` (default 2), `--no-settle` to skip
the drop. Gold/robot is the URDF articulation; green spheres are HaWoR MANO
joints; the coffee-can is a dynamic rigid on a cuboid table.

## SONIC whole-body (G1 29-DoF)

Frozen GEAR-SONIC v1.1 ONNX from `checkpoints/sonic/sonic_v1_1`. This is **not**
`G1_MINIMAL_CFG` (locomotion USD). SONIC uses the 29-DoF G1 (`G1_29DOF_CFG`):
three waist joints and three wrist joints per arm, no Inspire fingers.

The DexYCB clip is a tabletop right hand. Play uses **teleop** encoder mode:
standing legs + reconstructed hand root as the right-wrist target. The object
is translated into the reachable wrist band (same layout as ``g1_sonic_manip``),
not left at the capture's table centre.

```bash
cd simulation
source .venv/bin/activate
./replay_sonic.sh --headless --hands inspire \
  --npz ../reconstruction/runs/20200709_142553_836212060125/obj_tracking_out/isaaclab_replay.npz
```

Writes `obj_tracking_out/sonic_replay.mp4` next to `isaaclab_replay.npz`. `--mode g1` tracks a standing
29-DoF pose only (smoke test). GPU node; `onnxruntime-gpu` is in `simulation/.venv`.

### The robot has more than 29 joints

SONIC drives 29 body joints; everything else is fingers, free to use as a
separate grasp action channel. Anything reading or writing joints must resolve
SONIC's 29 **by name** (`play_sonic.py:_joint_ids`), never by assuming they are
indices 0..28.

| `--hands` | USD | fingers | total joints |
| --- | --- | --- | --- |
| `dex3` (default) | stock `g1.usd` | 3 per hand, 7 DoF | 43 |
| `inspire` | local mirror | 5 per hand, 12 DoF | 53 + root |
| `wuji` | CoordEx `g1_wuji_no_merge.usd` | 5 per hand, 20 DoF | 69 |

Dex3 joints are `{left,right}_hand_{index,middle}_{0,1}_joint` and
`_thumb_{0,1,2}_joint`. Inspire joints are `[LR]_<finger>_<link>_joint`.
Wuji joints are `{left,right}_finger<1-5>_joint<1-4>` (finger1 = thumb), which is
the naming written by `g1_wuji_retarget.npz`.

`wuji` is the hand the `g1_sonic_manip` task now uses. Its USD carries all 29 SONIC
body joints under identical names, so SONIC's body control is unaffected by the
choice, and it has no mimic joints at all. The reason to prefer it over `inspire`
is not the mechanism but the software: matching CoordEx's hardware is what lets its
pretrained hand VAE prior be reused as an initialization. Finger actuator gains for
`wuji` are copied from CoordEx's `wuji_hands` actuator (`_HAND_GAINS` in
`sonic/robot_cfg.py`) so finger dynamics match what that prior was trained against.

Isaac Lab's stock `g1_29dof_inspire_hand.usd` **cannot be spawned as-is**; it is
authored for a fixed-base manipulation rig and has two independent faults. Both
report the same misleading symptom, because either one stops PhysX creating the
articulation and the real complaints scroll past far above it:

```
Pattern '/World/Robot/root_joint' did not match any articulations
```

1. **Mimic joints.** The finger linkages are `physxMimicJoint:*` properties
   (gearing -1.0 on the four fingers, -1.6 and -2.4 on the thumb) that this
   PhysX build cannot resolve: `failed to find internal joint object for
   PhysxMimicJointAPI`. Both the properties **and** the applied
   `PhysxMimicJointAPI` schema have to go. Dropping only the properties leaves
   the API applied with no `referenceJoint`, which spawns fine but logs
   `must have exactly 1 "referenceJoint" relationship defined` once per joint
   per articulation -- 12 lines per robot, 48 for four environments, enough to
   bury the error you actually care about.
2. **Fixed base.** `PhysicsArticulationRootAPI` sits on `root_joint`, a
   `PhysicsFixedJoint` anchoring the pelvis to the world. Spawning with
   `fix_root_link=False` disables that joint and removes the articulation root
   along with it. Stock `g1.usd` has no root joint and puts the API on
   `/g1/pelvis` instead.

`scripts/fetch_inspire_hand.py` mirrors the asset into `assets/g1/` (gitignored,
~39 MB) and fixes both in its 19 KB physics layer, leaving the 39 MB mesh layer
untouched. It is idempotent and self-verifying.

Stripping the mimic leaves the six coupled joints per hand independently
actuated. If you want the real linkage back, re-impose the gearing in software
and mind the sign: `index_intermediate` is limited to `[-19.48, 116.88] deg`
while gearing -1.0 on a `[0, 97.4] deg` proximal would imply `[-97.4, 0]`, so the
two joint frames are oppositely oriented.

### Conventions that are easy to get wrong

Both of these produce a robot that thrashes rather than an obvious crash, so
check them first if SONIC misbehaves. A standing reference should yield
`max|action| < 0.5`; if it is ~4-5, one of these is wrong.

1. **Joint order.** SONIC reads observations and emits actions in **Isaac Lab**
   joint order, while `default_angles` / `kps` / `g1_action_scale` in
   `policy_parameters.hpp` are in **MuJoCo** order. `constants.py` keeps both
   name lists and reindexes by name, so everything downstream is Isaac Lab
   order. Deploy bridges them in `CreatePolicyCommand`.
2. **6D rotations flatten row-wise**, `[m00, m01, m10, m11, m20, m21]` -- this is
   `matrix_from_quat(q)[..., :2].reshape(-1)`, not the first two columns
   stacked. The identity is `[1,0,0,1,0,0]`, not `[1,0,0,0,1,0]`.

VR 3-point targets (`vr_3point_local_target`) are relative to the **reference
motion pelvis**, not the robot pelvis, and the third point is `torso_link`
offset by +0.35 m, not a head link. Proprioception history is oldest-first.

### Batched SONIC (for RL rollouts)

The shipped ONNX pair is traced at batch 1, so an RL rollout would need one
inference call per environment per tick. Re-export graphs that take any batch:

```bash
python scripts/export_sonic_dynamic_batch.py   # writes model_*_batch.onnx
./jobs/sonic_bench.sh                          # GPU node: equivalence + throughput
```

`SonicOnnxAgent` prefers `model_*_batch.onnx` when present and accepts either
`(D,)` or `(N, D)`. Two things had to be patched, both invisible at batch 1:

1. 20 `Reshape` targets spell the batch out as a literal `1`; they become `-1`.
2. The encoder-mode one-hot is a `ScatterND` into a constant of shape `[1, 3]`,
   indexed by a `torch.arange(batch)` that constant-folded to `[0]`. It can only
   ever fill row 0, so every environment past the first would get an all-zero
   one-hot and therefore a zero token. It is rebuilt as
   `onehot[b, k] = (encoder_index[b] == k)`, which also allows per-env modes.

CUDA is opt-in via `SONIC_ORT_CUDA=1`. onnxruntime-gpu 1.29 links the CUDA 13
runtime, whose wheels sit in `nvidia/cu13/lib` with nothing putting them on the
loader path. `policy.py` preloads the four it needs (`libcudart`, `libcublas`,
`libcublasLt`, `libcurand`) with `RTLD_LOCAL` -- not `LD_LIBRARY_PATH`, which
would shadow the CUDA 12 libs torch is built against, and not `RTLD_GLOBAL`,
which would expose CUDA 13 cuBLAS symbols for torch to bind to.

Measured on one H100 (`jobs/sonic_bench.log`), encoder+decoder per control tick:

| batch | CPU | CUDA |
| ----: | ----: | ----: |
| 256 | 6.5k env-steps/s | 204k env-steps/s |
| 4096 | 6.2k env-steps/s | 644k env-steps/s (6.4 ms/tick) |

So a frozen SONIC inner loop is affordable inside an RL rollout on GPU, and is
roughly 100x too slow on CPU.

### Where the hand can actually go

`scripts/probe_wrist_tracking.py` sweeps commanded right-wrist targets in the
pelvis frame and records where the hand ends up (`jobs/sonic_probe.sh`, results
in `runs/wrist_tracking.npz`). This bounds anything built on top of SONIC,
because a policy cannot place the hand better than the controller beneath it.

Tracking is **repeatable but biased**. Spread over the settling window is 1.3 mm
median, so commanding the same target twice lands in the same place; but the
steady-state offset is 8 cm median over a 27-point grid, and structured:

| commanded | median error | signed bias on that axis |
| --- | ---: | ---: |
| x = 0.40 | 0.042 m | +0.013 m |
| x = 0.55 | 0.101 m | −0.055 m (arm out of reach) |
| y = −0.30 | 0.035 m | +0.028 m |
| y = 0.00 | 0.116 m | +0.062 m (cannot cross the midline) |
| z = −0.05 | 0.032 m | +0.026 m |
| z = +0.25 | 0.105 m | +0.077 m |

A smooth repeatable bias is learnable, so absolute wrist pose is still a usable
action space; random 8 cm scatter would not have been. The practical envelope is
**x 0.30-0.42, y −0.28..−0.12, z −0.05..0.15**, where error is 1-4 cm. Outside it
the arm saturates. Kinematics agree: a target at x = 0.45, z = 0.03 is 0.59 m
from the shoulder, about the whole arm.

This rules out replaying the DexYCB layout directly. In that clip the can sits
**1.10 m** in front of the pelvis and the demonstrated hand path spans x =
0.59-0.93 m, so a standing G1 cannot reach any of it — the human was leaning
over the table. Worse, the capture's table is 1.2 m deep with the can at its
centre, so even standing flush against the front edge leaves the can 0.60 m out.
Use the clip for grasp reference; author the scene for the robot's workspace.

## Physical G1 RL env

Manager-based Isaac Lab env (gravity + contacts), not kinematic replay:

`simulation/source/v2d_sim/tasks/g1_table_object/` → Gym `V2D-G1-TableObject-v0`

Train/play wrappers are in `../rl` (algorithm still a PPO stub):

```bash
cd ../rl
./play.sh --headless --steps 200
./train.sh --headless --num_envs 64
```

## Manipulation over frozen SONIC

`simulation/source/v2d_sim/tasks/g1_sonic_manip/` → Gym `V2D-G1-SonicManip-v0`

SONIC is frozen and owns balance and all 29 body joints. The learned policy
never sees a joint: its action is a **right-wrist target in the pelvis frame (3)
plus a finger closure (1)**, and `mdp/actions.py` runs SONIC inside the env to
turn that into joint targets. Physics 200 Hz, SONIC 50 Hz, policy 25 Hz.

Two consequences of the probe above are baked into the config. The action range
is clamped to the well-tracked band rather than the arm's kinematic limit, since
commanding outside it just saturates. And the scene is re-authored rather than
copied from the capture: a 0.8 m deep table with the object spawning at pelvis
x 0.31-0.41, y −0.27..−0.13, which is where tracking is a few centimetres.

The object is the **reconstructed can with its SAM3D texture**, spawned from
`obj_tracking_out/isaaclab_assets/object.urdf` (written by
`retarget/export_isaaclab.sh`), not a placeholder cuboid. The spawn config sets
no `visual_material`, which is what lets the URDF's own MTL and 1024px texture
survive USD conversion; setting one silently replaces the texture with a flat
colour. Collision is a convex hull, which suits a can and keeps startup quick.

That mesh is **y-up** and the URDF applies no correction, so spawning it
unrotated in a z-up world lays the can on its side. It technically balances
there, but on 198 contact vertices with 1.2 cm between the support polygon and
the centre of mass, so the first touch rolls it away. `OBJECT_REST_ROT` is the
clip's tracked orientation with its 8.8° tilt removed, putting body +y at world
+z: 2066 contact vertices and 6.1 cm of margin, standing on its base with the
yaw the capture recorded. `OBJECT_REST_H` (6.94 cm) is the lowest rotated vertex
in that attitude — it depends on the rotation and is *not* half the bounding
box, so recompute both together if the clip or the mesh scale changes.

Reset spins the can with `mdp.events.reset_object_on_table` rather than
`reset_root_state_uniform`, which composes its sampled rotation on the right
(`q_default * q_delta`) and so yaws about the *body* z axis. With an upright can
that axis points sideways, so the stock term would tip it over by up to the
sampled angle. Composing on the left keeps the axis vertical in the world.

Height gained is measured from the object's **resting pose**, not the table top.
The mesh origin is its centroid, so it already sits ~7 cm up when untouched;
measuring from the table top pays a constant lift reward for doing nothing.

Because tracking is biased, the policy is given the **achieved** wrist pose and
the object-to-hand vector, not just its own command, so it closes the loop on
where the hand went instead of trusting where it aimed.

```bash
./jobs/sonic_manip_smoke.sh          # scripted reach/close/lift, no learning
cd ../rl && ./train.sh --headless --task V2D-G1-SonicManip-v0 --num_envs 32
```

Run the smoke test first. It answers whether the environment is sound (robot
stays up, hand reaches the target, scripted grasp lifts) separately from whether
the reward is learnable — after training the two are hard to tell apart.

`BatchedSonicController` (`sonic/batched.py`) is the vectorised inner loop. With
a fixed standing reference nearly the whole 1751-D encoder vector is constant,
so it is built once and only the heading (6) and VR 3-point target (21) are
rewritten per step. Its packing is bit-exact against the single-env
`pack_encoder` path, and `vr3_local_batch` against `vr3_local_from_bodies`.

Known limits of this first version: the wrist **orientation** target is held at
the standing pose, so the policy controls position and grip only. Only wrist
position tracking was measured; if grasping turns out to need a specific
approach angle, orientation is the next thing to add to the action space.

## CoordEx baseline

`V2D-CoorDex-WalkGrab-Play-v0` runs CoordEx's released WalkGrab policy on a
reconstructed object, as a literature baseline. This is **not** SONIC: the robot is
CoordEx's G1-Wuji and the whole MDP is theirs. `--clip` picks which reconstruction
goes on the table, and videos are written per clip so runs do not overwrite:

```bash
./jobs/coordex_v2d_play.sh --headless --video --clip 20200709_141754_836212060125
```

`V2D-CoorDex-WalkGrab-Stock-v0` is the control: the same policy on CoordEx's own
cylinder. Use it to separate porting bugs from genuine generalization failure — it
grasps and lifts, which is how we established the port is sound. On the coffee can
the same policy makes contact but lifts it only ~3 mm, so the failure is
out-of-distribution object geometry, not wiring and not the hand hardware.