OpenRAL
/

rskill-diffusion-pusht-pusht-fp32

@@ -4,12 +4,12 @@
 # ── Identity ───────────────────────────────────────────────────────────────
 schema_version: "0.1"
-name: "OpenRAL/rskill-diffusion-pusht"
-evaluated_tasks: ["pusht"]  # ADR-0060: benchmark task this checkpoint is validated for (gate)
 version: "0.1.0"
 license: "apache-2.0"
 role: "s1"
-kind: "vla"  # ADR-00XX: rSkill kind discriminator. "vla" = learnable Vision-Language-Action policy.
 # ── Policy identity ────────────────────────────────────────────────────────
 model_family: "diffusion"
@@ -29,11 +29,11 @@ sensors_required:
     min_width: 96
     min_height: 96
-# Output side (ADR-0013). The pusht_2d scene-pseudo-robot exposes its 2-D
 # (x, y) pusher position as two synthetic prismatic joints (tip_x, tip_y);
 # robots/pusht_2d/robot.yaml advertises `joint_position` over those 2 DoF.
 # The gym-pusht action IS the absolute target of those joints, so the
-# robot-side actuator is joint_position (ADR-0071 — matches every other
 # rSkill, where `kind` names the robot actuator, not the policy encoding).
 # The loader auto-fills n_dof (2) + vla_action_key from the robot YAML.
 actuators_required:
@@ -83,7 +83,7 @@ description: >
   loop, so cached pops are essentially free — this is the extreme test
   of the queue-drain contract.
-# ADR-0022 — action vocabulary surfaced to the reasoner LLM tool
 # palette so it can pick this skill by what it does (action verb +
 # object + scene), not just by its slug.
 actions:
@@ -93,11 +93,11 @@ objects:
 scenes:
   - "tabletop_2d"
-# ADR-0019 — per-checkpoint action contract (consumed by the dataset bridge
 # to bind the LeRobot v3 `action` feature shape).
 action_contract:
   dim: 2
-  # ADR-0071 — absolute positions of the 2 prismatic tip_x/tip_y joints.
   # Declared explicitly so the contract states joint-space rather than relying
   # on the undeclared-layout fallback.
   representation: "joint_positions"

 # ── Identity ───────────────────────────────────────────────────────────────
 schema_version: "0.1"
+name: "OpenRAL/rskill-diffusion-pusht-pusht-fp32"
+evaluated_tasks: ["pusht"]  # benchmark task this checkpoint is validated for (gate)
 version: "0.1.0"
 license: "apache-2.0"
 role: "s1"
+kind: "vla"  # rSkill kind discriminator. "vla" = learnable Vision-Language-Action policy.
 # ── Policy identity ────────────────────────────────────────────────────────
 model_family: "diffusion"
     min_width: 96
     min_height: 96
+# Output side. The pusht_2d scene-pseudo-robot exposes its 2-D
 # (x, y) pusher position as two synthetic prismatic joints (tip_x, tip_y);
 # robots/pusht_2d/robot.yaml advertises `joint_position` over those 2 DoF.
 # The gym-pusht action IS the absolute target of those joints, so the
+# robot-side actuator is joint_position (matches every other
 # rSkill, where `kind` names the robot actuator, not the policy encoding).
 # The loader auto-fills n_dof (2) + vla_action_key from the robot YAML.
 actuators_required:
   loop, so cached pops are essentially free — this is the extreme test
   of the queue-drain contract.
+# Action vocabulary surfaced to the reasoner LLM tool
 # palette so it can pick this skill by what it does (action verb +
 # object + scene), not just by its slug.
 actions:
 scenes:
   - "tabletop_2d"
+# Per-checkpoint action contract (consumed by the dataset bridge
 # to bind the LeRobot v3 `action` feature shape).
 action_contract:
   dim: 2
+  # absolute positions of the 2 prismatic tip_x/tip_y joints.
   # Declared explicitly so the contract states joint-space rather than relying
   # on the undeclared-layout fallback.
   representation: "joint_positions"