pith:OZB7D6Z7
CoLA-Flow Policy: Temporally Coherent Imitation Learning via Continuous Latent Action Flow Matching for Robotic Manipulation
Performing flow matching inside a continuous latent action space produces near-single-step inference and markedly smoother robotic trajectories than direct action-space methods.
arxiv:2601.23087 v4 · 2026-01-30 · cs.RO
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Claims
CoLA-Flow Policy achieves near-single-step inference, improves trajectory smoothness by up to 93.7% and task success by up to 25 percentage points over raw action-space flow baselines, while remaining significantly faster than diffusion-based policies.
That mapping action sequences into a continuous latent space and learning an explicit latent flow will reliably separate global motion structure from low-level control noise across varied real-world conditions and sensor modalities.
CoLA-Flow Policy encodes action sequences into latent trajectories and performs flow matching there, yielding near-single-step inference with up to 93.7% smoother trajectories and 25-point higher success rates than raw-action flow baselines.
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Receipt and verification
| First computed | 2026-05-20T00:04:23.904173Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
7643f1fb3fca7cf6d2f89589fc5f61742d979611e538d806aaa30d488643d484
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/OZB7D6Z7ZJ6PNUXYSWE7YX3BOQ \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 7643f1fb3fca7cf6d2f89589fc5f61742d979611e538d806aaa30d488643d484
Canonical record JSON
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