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Molmoact libero

Skill graph-robots/open-robot-skills/policies/molmoact-libero

Skill and tool bundles for gap (graph as policy) — Anthropic Agent Skills format, discovered by path

Install
npx -y skills add graph-robots/open-robot-skills --skill molmoact-libero

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What its author says it does

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Run the MolmoAct LIBERO checkpoint (allenai/MolmoAct-7B-D-LIBERO-0812) as a closed-loop VLA policy for the dexterous pick-and-place segment of a task. Drives a Franka Panda in the LIBERO/robosuite OSC_POSE action space from agentview + wrist cameras, served behind a vLLM-style script speaking the openpi websocket protocol; the policy server is the bundle's own preset (no policy_id). Reads the graph-scoped observation_stream each window and terminates on a gripper open→close→open cycle, a VLM yes/no check, or max_windows. Use when a pick/place (or pick-and-drop-in-container) segment on tabletop rigid LIBERO objects is delegated to a learned policy — best steered (perceive + hover above the target) first; this is the MolmoAct alternative to pi05-libero for the same task family. NOT for deformables/cloth folding, articulated objects, non-Franka embodiments, or tasks outside the LIBERO pick-place distribution.

SKILL.md

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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".

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.