{ "_comment": "Upstream paper results for SmolVLA (0.45B) on LIBERO benchmark. NOT locally reproduced \u2014 numbers sourced from arxiv:2506.01844 Table 2. Reproduction is achievable via lerobot-eval (see reproduction_cli below); deferred to a dedicated benchmark session due to ~8h GPU time per full run.", "schema_version": "0.1", "source": { "paper": "SmolVLA: A vision-language-action model for affordable and efficient robotics", "arxiv": "https://arxiv.org/abs/2506.01844", "table": "Table 2 \u2014 Simulation benchmarks (LIBERO and Meta-World)", "model_variant": "SmolVLA (0.45B)", "evaluated_by": "upstream authors", "reproduced_locally": false, "reproduction_planned": "Deferred \u2014 use lerobot-eval CLI (see reproduction_cli). Requires ~8h GPU on A100-class hardware.", "reproduction_cli": { "description": "Re-run this benchmark inside the OpenRAL repo. openral benchmark run is the canonical producer of RSkillEvalResult JSONs with reproduced_locally=true.", "command": "openral benchmark run --suite libero_spatial --rskill smolvla-libero", "status": "in-tree (libero_spatial); libero_object / libero_goal / libero_10 catalogue YAMLs land in a follow-up", "notes": [ "The 'results' block on this file is still the paper snapshot (reproduced_locally=false); re-running the command above and overwriting the file with its output flips that flag.", "libero_10 is the lerobot/upstream name for LIBERO-Long (what the SmolVLA paper calls 'Long').", "lerobot.envs.LiberoEnv auto-injects LiberoProcessorStep to remap LIBERO obs keys to observation.images.* / observation.state.*.", "No separate LIBERO gym install needed \u2014 lerobot bundles the adapter. Only lerobot + its sim extras required.", "For LIBERO-plus (robustness, ~10k variants): --env.type=libero_plus" ] } }, "benchmark": { "name": "LIBERO", "dataset": "physical-intelligence/libero", "dataset_episodes": 1693, "protocol": "10 trials per task, binary success (task fully completed or not), multi-task training", "tasks_per_suite": 10, "suites": [ "spatial", "object", "goal", "long" ], "total_tasks": 40, "robot": "Franka Panda (7-DOF)", "simulator": "LIBERO (MuJoCo-based)" }, "eval_config": { "action_chunk_size": 50, "flow_matching_steps": 10, "image_size": [ 512, 512 ], "cameras": [ "OBS_IMAGE_1", "OBS_IMAGE_2" ], "inference_mode": "synchronous", "vla_pretraining": false, "training_steps": 100000, "batch_size": 64 }, "results": { "libero_spatial": { "success_rate": 0.9, "n_tasks": 10, "n_trials_per_task": 10 }, "libero_object": { "success_rate": 0.96, "n_tasks": 10, "n_trials_per_task": 10 }, "libero_goal": { "success_rate": 0.92, "n_tasks": 10, "n_trials_per_task": 10 }, "libero_long": { "success_rate": 0.71, "n_tasks": 10, "n_trials_per_task": 10 }, "libero_avg": { "success_rate": 0.873, "note": "Average across all four LIBERO suites" } }, "baselines": { "diffusion_policy": { "libero_spatial": 0.783, "libero_object": 0.925, "libero_goal": 0.683, "libero_long": 0.505, "avg": 0.724, "vla_pretraining": false }, "octo_90M": { "libero_spatial": 0.789, "libero_object": 0.857, "libero_goal": 0.846, "libero_long": 0.511, "avg": 0.751, "vla_pretraining": true }, "openvla_7b": { "libero_spatial": 0.847, "libero_object": 0.884, "libero_goal": 0.792, "libero_long": 0.537, "avg": 0.765, "vla_pretraining": true }, "pi0_paligemma_3b": { "libero_spatial": 0.87, "libero_object": 0.63, "libero_goal": 0.89, "libero_long": 0.48, "avg": 0.718, "vla_pretraining": false }, "pi0_3b_robotics_pretrained": { "libero_spatial": 0.9, "libero_object": 0.86, "libero_goal": 0.95, "libero_long": 0.73, "avg": 0.86, "vla_pretraining": true } } }