Card: the sharpa_fardexbot hand is the rig hand DexSuite ships; no slot note

README.md

@@ -31,9 +31,9 @@ Scripted-expert DexSuite data for **put_bread_toaster** with the floating wrist
 
 The controller reaches the toast, pinches it, lifts it, carries it over a slot, lowers it in, releases it, moves over the lever and presses it down. Each episode ends at its first successful transition, so `success` and `terminated` are set on the last frame only. DART: during recording the environment executed the controller's command plus a smooth Ornstein-Uhlenbeck perturbation of the wrist pose (1.5 cm and 4 degrees, 0.6 s time constant, scaled by 1.0 / 0.5 / 0.1 in the free / approach / in-hand phases and switched off while pinching, lowering into the slot, releasing and pressing the lever), and the recorded action is the controller's clean command, its correction from the perturbed state (Laskey et al., 2017). The command the environment actually executed at step t is stored in `obs.state.other.action` of frame t+1, so replaying `action` open loop does not reproduce the recorded episodes. Each episode randomizes the toast position (±3 cm in x, ±4 cm in y) and yaw (any heading), the toaster position (±2 cm) and yaw (±8°), the start wrist pose (±5 cm and ±12° per axis) and the relaxed finger pose; 200 of 218 attempts were kept, the other 18 failed the controller's own checks and were discarded.
 
-The recording uses 8 physics substeps per control step (`physics_substeps: 8` in `meta/env_config.json`; DexSuite's default is 2), so build evaluation environments from `meta/env_config.json`. The toaster had its original 3.2 cm slots when the set was recorded. DexSuite 0.1.1 defaults to wider 5.4 cm collision slots (the visual mesh is unchanged); set `DEXSUITE_TOASTER_SLOT=narrow` before building the environment to evaluate in the recorded slot geometry.
+The recording uses 8 physics substeps per control step (`physics_substeps: 8` in `meta/env_config.json`; DexSuite's default is 2), so build evaluation environments from `meta/env_config.json`.
 
-The robot is an arm-less floating wrist (a virtual 6-DoF gantry) carrying the right Sharpa hand (22 DoF); the 29-D action is 7 `osc_pose_abs_quat` values (position offset and orientation relative to the episode-start wrist pose) followed by 22 hand joint position targets in radians. The recording names the hand `sharpa_fardexbot`, the Sharpa hand with its wrist-camera mount, which shows in the front-camera frames; DexSuite registers that name as an alias of the plain `sharpa` hand. Besides the standard DexSuite keys, every frame carries per-finger contact forces (`obs.state.other.finger_force_world`, `finger_force_norm` in N, `finger_force_display`, `finger_force_saturated`, `finger_contact`; thumb, index, middle, ring, pinky) and the controller's phase (`obs.state.other.controller_phase`: 0 free, 1 approach, 2 in hand) and stage (`obs.state.other.controller_stage`, named in `meta/controller.json`). `meta/env_config.json` rebuilds the environment and `meta/controller_episodes/episode_*.json` keeps each episode's scene seed, style seed and controller parameters. Depth was rendered during collection but is not included.
+The robot is an arm-less floating wrist (a virtual 6-DoF gantry) carrying the right Sharpa hand (22 DoF); the 29-D action is 7 `osc_pose_abs_quat` values (position offset and orientation relative to the episode-start wrist pose) followed by 22 hand joint position targets in radians. The hand is `sharpa_fardexbot`, the right Sharpa hand carrying the wrist-camera mount of the real handheld rig (mount plate, quick changer and D405 body, which collide with the scene and show in the front-camera frames); DexSuite ships it under that name. Besides the standard DexSuite keys, every frame carries per-finger contact forces (`obs.state.other.finger_force_world`, `finger_force_norm` in N, `finger_force_display`, `finger_force_saturated`, `finger_contact`; thumb, index, middle, ring, pinky) and the controller's phase (`obs.state.other.controller_phase`: 0 free, 1 approach, 2 in hand) and stage (`obs.state.other.controller_stage`, named in `meta/controller.json`). `meta/env_config.json` rebuilds the environment and `meta/controller_episodes/episode_*.json` keeps each episode's scene seed, style seed and controller parameters. Depth was rendered during collection but is not included.
 
 - **Homepage:** https://huggingface.co/dexsuite
 - **Paper:** https://arxiv.org/abs/2610.37799
@@ -62,7 +62,7 @@ python -m scripts.imitation_learning.robomimic.train \
 
 `--sensors cameras --cameras front` trains on the front camera and robot proprio instead, and `--config act`, `act_img` or `diffusion` select the other policy recipes (see `scripts/imitation_learning/robomimic/README.md`). `--dataset dexsuite/put_bread_toaster_floating_sharpa_scripted` works as well.
 
-Rollouts build the environment from `meta/env_config.json`, 8 physics substeps included. Prefix the command with `DEXSUITE_TOASTER_SLOT=narrow` to evaluate in the 3.2 cm slots the set was recorded with.
+Rollouts build the environment from `meta/env_config.json`, 8 physics substeps included.
 
 ## Dataset Structure