leagents 0.0.4

LeAgents — agentic orchestration for the LeRobot pipeline: collect -> train -> eval -> improve

LeAgents

Agentic orchestration for the LeRobot robotics pipeline — an orchestrator drives an automated collect → train → eval → improve loop over LeRobotDataset v3.0, with a deterministic loop controller, a constitution safety gate, verification gates before promotion, and (M2) a dashboard for visualizing the flow.

Architecture, research grounding (verified 2023–2026 papers), and roadmap: DESIGN.md.

Status — v0.0.4

Milestone	Scope	Status
M0	Sim-only loop on LIBERO: seed dataset → SmolVLA fine-tune → lerobot-eval gate → promote/iterate/escalate/rollback	✅ pipeline verified end-to-end on a real GPU (PushT + LIBERO smoke configs); full-scale training run pending
M1	DexFlyWheel-style self-improvement, RoboGene-style task curation, policy escalation, OKF knowledge layer (Karpathy-wiki-style, DESIGN.md §3.6) + provider-agnostic LLM proposer	🚧 knowledge layer + LLM adapter landed
M2	Flow dashboard (Rerun episode replay, WandB curves, OTel agent traces)	🚧 flow view v1 landed: runs → cycles → decisions live, eval chart, event log, knowledge browser (leagents dash)
M3	Real robot: teleop collection, HIL-SERL adapter (requires lerobot ≥ 0.6.0, see CVE note in DESIGN.md §6)	planned

What works today: the full loop state machine with budgets, the constitution gate, SQLite job store, JSONL event log, subprocess wrappers for lerobot-train / lerobot-eval, the OKF knowledge layer (knowledge/ pages with provenance, updated every cycle, linted), the DexFlyWheel data path (success-filtered rollout harvesting → accumulated mix → adaptation training), and a provider-agnostic LLM adapter (llm: anthropic:*|openai:*[@base_url], or none at all — every flow has a deterministic fallback). All covered by tests that run without a GPU or lerobot installed.

First full-scale autonomous run (2026-07-04)

One M0 run on a single RTX 5070 Ti (16 GB), fully autonomous — 3 cycles, 6.1 GPU-hours, zero human intervention, every decision/event/knowledge-page update logged:

Cycle	Data	Train	LIBERO spatial eval (100 episodes)	Decision
0	40 episodes	20k steps from smolvla_base (loss → 0.030)	0% success — arm reaches targets, never completes	promote (baseline)
1	80	20k steps, continued from the blessed checkpoint	0%	iterate
2	160	20k steps	0%	iterate — the escalate_floor guard correctly refused to escalate a 0%-plateau to a bigger policy

The 0% success is the expected outcome of the data budget, not a pipeline failure: 4→16 episodes per task versus the ~50-per-variation guidance the design research verified for SmolVLA. This run validates the loop — budgets held, weights carried over, and the decision function behaved exactly as specified. The next run scales the data schedule to 200→500 episodes (20→50 per task).

Quickstart

pip install -e ".[dev]"
pytest

# dry run — no GPU, no lerobot; synthetic eval scores exercise the decision logic
leagents run -c configs/m0_libero.yaml --dry-run

# real run (Linux; needs a GPU and the LIBERO extras)
pip install -e ".[lerobot]"
leagents run -c configs/m0_libero.yaml

# inspect runs, cycles, decisions, blessed checkpoints
leagents status

# flow dashboard — runs, cycle pipeline, decisions, eval chart, events, knowledge
pip install -e ".[dash]"
leagents dash            # → http://127.0.0.1:8321

How the loop decides

Each cycle trains a candidate checkpoint and evaluates it on LIBERO. The decision is a pure function of success-rate deltas vs. the blessed baseline (leagents/orchestrator/decision.py):

• promote — candidate beats baseline by ≥ promote_delta; it becomes the new blessed checkpoint
• iterate — small improvement; collect more data on failing task variations
• escalate — plateaued for plateau_cycles; move up the policy ladder (SmolVLA → π0.5)
• rollback — regression; the blessed checkpoint stands

Control flow is deterministic Python persisted to SQLite — LLM agents (task proposal, curation; M1) only make proposals inside the gates, never control-flow decisions.

Layout

leagents/
├── orchestrator/   # loop controller, decision logic, constitution gate, proposer
├── agents/         # data / train / eval / improve agents (LeRobot CLI wrappers)
├── contracts/      # typed records: DatasetRef, CheckpointRecord, EvalReport
├── events/         # JSONL event bus (dashboard reads this in M2)
└── store/          # SQLite job store (runs, cycles, checkpoints)
configs/            # m0_libero.yaml, constitution.yaml
tests/              # loop e2e with fake runners — no GPU needed

License

Apache-2.0