feat(env): config to skip the discarded scene rebuild on reset Libero (#4272)

* perf(libero): skip the discarded scene rebuild on reset

LIBERO's OffScreenRenderEnv defaults to hard_reset=True, so every reset() frees
the MjSim, re-serialises the scene with model.get_xml(), recompiles it with
MjSim.from_xml_string(), constructs a fresh offscreen GL context and re-wires
every observable.

When init states are in use, LiberoEnv.reset() immediately calls
set_init_state(), which overwrites the whole sim state -- so all of that work is
discarded. This passes hard_reset=not init_states instead. Without init states
the randomisation reset() performs is the only thing placing the objects, so the
hard reset is kept.

Measured on an RTX 3060 Ti (EGL, robosuite 1.4.0, mujoco 3.2.7, 256x256 x2
cameras), through LiberoEnv.reset(), fresh env per arm:

  suite            hard      soft     saved
  libero_spatial   1697 ms   233 ms   1464 ms
  libero_object    1394 ms   172 ms   1222 ms
  libero_goal      1177 ms   164 ms   1013 ms
  libero_10        1528 ms   207 ms   1321 ms

Equivalence
-----------
Immediately after set_init_state, qpos, qvel, ctrl and act are bit-identical
between the two paths on every suite tested.

After the 10 settle steps that reset() runs, 9 of 41 qpos entries differ:
robot0_joint1..7 (<= 2.4e-5 rad) and gripper0_finger_joint1/2 (<= 2.1e-4 rad).
No object joint differs on any suite. The drift is driven by the gripper
component of the settle action ([0,0,0,0,0,0,-1]); replacing it with zeros keeps
the two paths bit-identical for 12 further steps, and with num_steps_wait=0 there
is no divergence at all.

Wrist-camera pixels can differ by up to ~87/255, because a sub-millimetre finger
displacement crosses rasterisation boundaries at 256x256. The pixel metric badly
overstates the physical difference here; 2.1e-4 rad is 0.012 degrees.

So this is not bit-identical end to end, and reviewers should decide whether
0.012 degrees of gripper drift is acceptable for the benchmark. It does not
change object placement, which is what the fixed init states exist to control.


* refactor(env): config param libero + docs

---------

Co-authored-by: Dimitar Dimitrov <dvdimitrov13@gmail.com>

docs/source/libero.mdx

@@ -92,6 +92,20 @@ LIBERO supports two control modes — `relative` (default) and `absolute`. Diffe
 --env.control_mode=relative   # or "absolute"
 ```
 
+### Reset performance
+
+By default, LeRobot preserves LIBERO's hard-reset behavior. With fixed initial
+states enabled, you can opt into soft resets to skip rebuilding the simulator
+model and renderer on every episode:
+
+```bash
+--env.init_states=true --env.hard_reset=false
+```
+
+Soft resets are faster but are not bit-identical to hard resets after the
+environment's settling steps, so camera observations and policy results may
+differ slightly. Use hard resets when reproducing benchmark results.
+
 ### Policy inputs and outputs
 
 **Observations:**

docs/source/libero_plus.mdx

@@ -134,6 +134,20 @@ LIBERO-plus supports two control modes — `relative` (default) and `absolute`.
 --env.control_mode=relative   # or "absolute"
 ```
 
+### Reset performance
+
+By default, LeRobot preserves LIBERO's hard-reset behavior. With fixed initial
+states enabled, you can opt into soft resets to skip rebuilding the simulator
+model and renderer on every episode:
+
+```bash
+--env.init_states=true --env.hard_reset=false
+```
+
+Soft resets are faster but are not bit-identical to hard resets after the
+environment's settling steps, so camera observations and policy results may
+differ slightly. Use hard resets when reproducing benchmark results.
+
 ### Policy inputs and outputs
 
 **Observations:**