Molmoact Libero

molmoact-libero

Closed-loop VLA-policy skill backed by one model checkpoint: AllenAI's MolmoAct LIBERO checkpoint (allenai/MolmoAct-7B-D-LIBERO-0812). The skill is the model — it owns its serving preset (molmoact-libero), so a policy node names this skill, not a free-floating policy_id. The closed-loop replan/execute/terminate body and the load-bearing LIBERO observation encoding live in gap.runtime.policy.run_policy_loop; the websocket client is resolved (and cached per preset) through the executor's PolicyExecutor.

This is the MolmoAct alternative to pi05-libero for the same task family — the two are the policy A/B axis the benchmark ablates. Pick whichever the task / experiment calls for; their capability envelope is the same.

Capability

• Embodiment: Franka Panda (LIBERO/robosuite), OSC_POSE delta action

space [Δx, Δy, Δz, Δrx, Δry, Δrz, gripper]. No embodiment translation happens in the loop — the checkpoint's native action space is forwarded to sim.apply_policy_action.

• Tasks: the LIBERO pick-and-place distribution — pick a tabletop rigid

object, optionally place/drop it in a container. Works best steered: perceive the target and hover the end-effector above it (preserving the current rotation) before handing over, so the policy starts in-distribution.

• Not for: deformables / cloth folding, articulated objects, non-LIBERO

embodiments, or tasks the checkpoint never saw. If the task is outside this envelope, pick a different skill or report a missing capability — do not delegate it here and hope.

Serving

The bundle ships its own server.py and declares MolmoAct-flavored openpi as a git dep in its own pyproject.toml, so the bundle is self-contained: no $GAP_OPENPI_DIR clone, no shared venv. First-run setup is gap skills install molmoact-libero, which uv syncs the bundle's .venv/ with vLLM + MolmoAct deps. The launcher then spawns the server via uv run --project policies/molmoact-libero -- python server.py ... (so the bundle's own venv activates automatically) and downloads the checkpoint from hf://allenai/MolmoAct-7B-D-LIBERO-0812 on first run.

The bundle's server.py is a placeholder that documents how to wire up a vLLM-style server speaking the openpi websocket protocol; replace it with your real serving script (e.g., from an internal MolmoAct fork) before running the bundle for the first time. Run it yourself with gap policy serve molmoact-libero. A policies: config entry named molmoact-libero overrides the recipe (e.g. an external url:).

Termination & exits

The loop exits on whichever fires first — a commanded gripper open→close→open cycle (gripper_cycle, the per-item terminator for clean-all loops; set gripper_cycle_termination: true), a non-empty termination_prompt answered yes by the VLM (completed_by_vlm), or the max_windows backstop. These are the subgraph's success exits; the failure exit is failed (the loop raised). Whether the task actually succeeded is a checkpoint, not an exit — attach a postcondition that checks the world (e.g. the object is in the container), never an exit value like "folded".

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: graph-robots
• Source: graph-robots/open-robot-skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.