kinetic-bc-pickplace

Kinetic BC Pick/Place Policy

A lightweight, standalone behavior-cloning policy for tabletop pick-and-place in simulation. Trained inside the Kinetic modular VLA stack, but this checkpoint runs on its own — pure numpy + safetensors, no framework needed.

• Size: ~25 KB (2-layer MLP, 16 → 256 → 7, tanh)
• Standalone success: 0.90 on randomized sim scenes (40 episodes)
• Task: pick-bottle / place-cup on a randomized tabletop
• Trained on: 5,924 transitions from a heuristic expert (behavior cloning)

What "standalone" means here

The policy is closed-loop and object-relative: each control tick it takes the current end-effector position, the target object's position, the gripper state, and a subtask one-hot, and predicts the absolute EE target (as an offset from the object). No hand-coded controller is in the action path — the weights drive.

Usage

pip install numpy safetensors

from inference import KineticBCPolicy

pol = KineticBCPolicy("model.safetensors")
out = pol.act(
    ee_xyz=[0.30, 0.0, 0.90],       # current end-effector xyz
    target_obj_xyz=[0.45, 0.10, 0.78],  # detected object xyz
    gripper_open=1.0,               # 1.0 open, 0.0 closed
    subtask="reach",                # see subtask list below
)
# {'ee_target_xyz': [...], 'rpy': [...], 'gripper': 0.98}

Call act() every control tick, send ee_target_xyz to your IK/controller, and drive gripper open/closed. Sequence subtasks with your own planner.

I/O

Subtasks: locate, navigate, reach, grasp, lift, return_to_user, release, retreat, place

Limitations

• Sim only. Trained and evaluated in Kinetic's mock sim; not validated on hardware.
• Distilled from a heuristic expert — it imitates a hand-coded controller, so it inherits that ceiling; it is not learned from real demonstrations.
• Single object type per scene (bottle / cup); no vision encoder — consumes object positions from an upstream detector.
• 0.90 success ≠ 1.0: expect occasional missed grasps on hard randomizations.

License

Apache-2.0. See the Kinetic repo.