prism-jepa-red-cube-arx

PRISM-JEPA · red_cube (ARX-X5) — LeWM world model + PRISM action prior

Complete deployable stack for goal-conditioned visuomotor planning on the ARX-X5 "red cube" task: the LeWM JEPA world model + the PRISM goal-conditioned action prior + self-contained PRISM-MPPI inference code.

⚠️ Status — read first

This is a research artifact / deployment hand-off package, NOT a validated policy.

• Trained on 201 teleop demos (Xia-2004/red_cube, 42,165 frames, 5-DoF).
• The world model converges cleanly and its forward model is accurate (rollout pred/id ≈ 0.25 < 0.5), but its MPPI cost surface is weak/flat on this small-real-robot, small-action data (CV ≈ 0.14 ≪ 0.30 "discriminative" threshold). Consequence: the planner's distinctive cost-rescoring is dormant, so in practice PRISM ≈ a goal-conditioned BC prior — in offline A/B it produces ~31% more expert-like actions than vanilla LeWM-MPPI (which wanders), but adds no measurable goal-progress in the world model's own latent metric (paired t-test p = 0.57).
• Never run on the real robot. Treat as a starting point; add workspace/velocity safety limits and validate before any hardware run.
• Full analysis & how these numbers were obtained: project doc docs/30_red_cube_cv_investigation_and_prism.md.

Contents

Observation / action space

• Observation: single top-down RGB frame, 224×224×3 uint8 (RealSense camera_third).
• Goal: an RGB goal image, same format (the prior + cost are conditioned on it).
• Action: 5-DoF delta end-effector [dx, dy, dz, dyaw, d_gripper], raw units, one per control tick. plan() returns one plan-step = A_block = 5 ticks → shape (5, 5).

Dependencies

torch, numpy, einops, and transformers (the encoder inside the ckpt is a HuggingFace ViT, needed at unpickle time). The three bundled .py files must be importable from the working directory. (If unpickling complains about a missing class, also pip install stable-pretraining.)

Deploy — receding-horizon control loop

from arx_inference_demo import PrismMPPIInference

planner = PrismMPPIInference(
    lewm_ckpt  = "lewm_red_cube_epoch_100_object.ckpt",
    prior_ckpt = "prior_head_red_cube.pt",
    use_prism  = True,     # True = PRISM (prior ⊗ MPPI via PoG fusion); False = vanilla LeWM-MPPI
    device     = "cuda",
)

goal_img = load_goal_image()                   # (224,224,3) uint8 — the task goal image
while not done:
    obs     = camera.read()                    # (224,224,3) uint8, top-down camera_third view
    actions = planner.plan(obs, goal_img)      # (5, 5) raw [dx,dy,dz,dyaw,d_gripper]
    for a in actions:                          # receding horizon: execute the block, then replan
        robot.execute(a)                       # (or execute fewer than 5 and replan more often)

plan() runs one full PRISM-MPPI optimization and returns the first A_block = 5 env-step actions of the optimized plan, in raw action units (already de-normalized).

Key hyperparameters (PrismMPPIInference constructor)

H, A_block, A_raw, history_size must match the checkpoints — the constructor asserts the prior head's config agrees. Change them only if you retrain.

PRISM vs vanilla (A/B)

Build a second planner with use_prism=False for a baseline (plain LeWM-MPPI, no prior, same encoder/predictor/MPPI loop). On this task PRISM produces more expert-like actions; vanilla tends to wander because the cost surface is flat.

Provenance

Data: Xia-2004/red_cube (ARX-X5 left-arm teleop). Sibling of Xia-2004/arx-left-cube. World-model architecture is identical to the sim LeWM (ViT-tiny, embed_dim 192, predictor depth 6 / heads 16) — part of the PRISM-JEPA project (sister of Newt-PRISM, CoRL 2026).