Pith Number

pith:Z5N2KXTB

pith:2026:Z5N2KXTBVW5KY7ZQ2NR4Z33THS

not attested not anchored not stored refs resolved

3D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots

Chiara Castellani, Domenico Prattichizzo, Enrico Turco

A hybrid RL and DWA controller improves deformation and path completion for microrobots in 3D constrained spaces.

arxiv:2605.12689 v1 · 2026-05-12 · cs.RO

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\usepackage{pith}
\pithnumber{Z5N2KXTBVW5KY7ZQ2NR4Z33THS}

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Record completeness

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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

integrating RL with a DWA-based local planner significantly enhances both deformation and navigation capabilities compared to a pure RL and a model-based methods. In particular, the proposed autonomous controller consistently achieves high deformation and near-perfect path completion during training and maintains robust performance in unseen scenarios.

C2weakest assumption

The simulated vascular network and sparse point-cloud sensor model are sufficiently representative of real-world dynamics, contact forces, and sensing noise for the performance gains to transfer.

C3one line summary

A hybrid RL-DWA controller achieves high deformation and near-perfect path completion for deformable microrobots navigating simulated 3D vascular networks from sparse point clouds.

References

31 extracted · 31 resolved · 1 Pith anchors

[1] Path planning techniques for mobile robots: Review and prospect, 2023

[2] C. Laugier and R. Chatila,Autonomous navigation in dynamic environ- ments. Springer, 2007, vol. 35 2007

[3] Fast motion planning for high-dof robot systems using hierarchical system identification, 2019

[4] A rrt based path planning scheme for multi-dof robots in unstructured environments, 2024

[5] The dynamic window approach to collision avoidance 1997

Formal links

1 machine-checked theorem link

Receipt and verification

First computed	2026-05-18T03:09:49.860356Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

cf5ba55e61adbaac7f30d363ccef733c85db5c642b2bf12c84d24dcf5e9f2174

Aliases

arxiv: 2605.12689 · arxiv_version: 2605.12689v1 · doi: 10.48550/arxiv.2605.12689 · pith_short_12: Z5N2KXTBVW5K · pith_short_16: Z5N2KXTBVW5KY7ZQ · pith_short_8: Z5N2KXTB

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/Z5N2KXTBVW5KY7ZQ2NR4Z33THS \
  | 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: cf5ba55e61adbaac7f30d363ccef733c85db5c642b2bf12c84d24dcf5e9f2174

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "5792c6b6b3cd2fa4dcc41be13cde62dffa883eef73a5ee5a9180ad9c75da02b6",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-05-12T19:37:32Z",
    "title_canon_sha256": "449b68d79cf2ef1b4c3109b8b908ed3422ca30d5076a142bc378b439b783536d"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12689",
    "kind": "arxiv",
    "version": 1
  }
}