Pith Number

pith:I5NLUQMK

pith:2026:I5NLUQMKEOIRU5QFGMQNKT5AZM

not attested not anchored not stored refs pending

CycleRL: Sim-to-Real Deep Reinforcement Learning for Robust Autonomous Bicycle Control

Gelu Liu, Junliang Wu, Songyuan Li, Teng Wang, Xiangwei Zhu, Zhijie Wu

CycleRL trains a PPO policy in simulation that transfers directly to physical bicycle hardware for balance and tracking.

arxiv:2603.15013 v3 · 2026-03-16 · cs.RO

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\usepackage{pith}
\pithnumber{I5NLUQMKEOIRU5QFGMQNKT5AZM}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

CycleRL achieves a 99.90% balance success rate, 1.15° heading tracking error, and 0.18 m/s velocity tracking error in simulation, with successful hardware deployment that validates DRL as offering superior adaptability over traditional methods.

C2weakest assumption

That systematic domain randomization over a limited set of simulation parameters is sufficient to cover all real-world uncertainties and enable zero-shot transfer to physical hardware without additional adaptation or fine-tuning.

C3one line summary

CycleRL trains a PPO policy in Isaac Sim with domain randomization to achieve 99.9% balance success and direct hardware transfer for autonomous bicycle control.

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-06-26T01:15:18.361625Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

475aba418a23911a76053320d54fa0cb1d1ed26faf618aad5b87f8905d81cbb7

Aliases

arxiv: 2603.15013 · arxiv_version: 2603.15013v3 · doi: 10.48550/arxiv.2603.15013 · pith_short_12: I5NLUQMKEOIR · pith_short_16: I5NLUQMKEOIRU5QF · pith_short_8: I5NLUQMK

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/I5NLUQMKEOIRU5QFGMQNKT5AZM \
  | 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: 475aba418a23911a76053320d54fa0cb1d1ed26faf618aad5b87f8905d81cbb7

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e42ff80effcd12cf3ec2c971535605e85c299c658a8d58ed5a2a212454ff789d",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-03-16T09:17:51Z",
    "title_canon_sha256": "d639c6f0d46a0e9d6d17ab963bb7b9172a3c6fb14622b41d94b97b6e92c4930f"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2603.15013",
    "kind": "arxiv",
    "version": 3
  }
}