Pith Number

pith:TBXV2RLO

pith:2026:TBXV2RLOAPINQWH4O24Z324224

not attested not anchored not stored refs pending

From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution

Ayal Taitler, Lidor Erez, Shahaf S. Shperberg

Reinforcement learning refines first-order hybrid plans into dynamically feasible robot trajectories using second-order constraints.

arxiv:2604.12474 v3 · 2026-04-14 · cs.RO · cs.AI

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\usepackage{pith}
\pithnumber{TBXV2RLOAPINQWH4O24Z324224}

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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

Our results show that this approach can reliably recover physical feasibility and effectively bridge the gap between a planner's initial first-order trajectory and the dynamics required for real execution.

C2weakest assumption

That an MDP incorporating analytical second-order constraints allows reinforcement learning to consistently find feasible refinements without excessive computation or failure to converge on valid trajectories.

C3one line summary

Reinforcement learning refines first-order hybrid plans into second-order dynamically feasible trajectories for robotic missions with deadlines and physical limits.

Receipt and verification

First computed	2026-06-05T01:15:23.556074Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

986f5d456e03d0d858fc76b99deb9ad7338f66490b6cbd1c53a47dc0eceabb67

Aliases

arxiv: 2604.12474 · arxiv_version: 2604.12474v3 · doi: 10.48550/arxiv.2604.12474 · pith_short_12: TBXV2RLOAPIN · pith_short_16: TBXV2RLOAPINQWH4 · pith_short_8: TBXV2RLO

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/TBXV2RLOAPINQWH4O24Z324224 \
  | 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: 986f5d456e03d0d858fc76b99deb9ad7338f66490b6cbd1c53a47dc0eceabb67

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b7eed6f0fb8cf535a0b673dee4452d8c7de634f576363629bc48404a35551d09",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-04-14T09:00:08Z",
    "title_canon_sha256": "ecd30ca0888f5fb8bd86c1b6bc8780b2a31476f40db09c327f84a3a840f6241b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.12474",
    "kind": "arxiv",
    "version": 3
  }
}