Claim · 2016-03-01 · publication · Stated in source · Supported

The paper presents an inverse optimal control algorithm that it states can learn arbitrary nonlinear cost functions, such as neural networks, without meticulous feature engineering.

One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.

What it connects

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization → states its contribution → The paper presents an inverse optimal control algorithm that it states can learn arbitrary nonlinear cost functions, such as neural networks, without meticulous feature engineering.

Where it appears

No collection page holds this claim. It is listed with its records in the explorer.

Claim and evidence

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationstates its contribution ↓The paper presents an inverse optimal control algorithm that it states can learn arbitrary nonlinear cost functions, such as neural networks, without meticulous feature engineering.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2016-03-01 · publication Records dated 2016 →

Scope and limitsThe paper's own description of its contribution; not an assessment of it.

The evidence 01

primary sourceSupports the claim

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization

arXiv · 2016-03-01

Cite the original source

Cite the original document, not Selekoa. Check these details against it.

Source excerpt

“To address the former challenge, we present an algorithm capable of learning arbitrary nonlinear cost functions, such as neural networks, without meticulous feature engineering.”

Location in source arXiv v1, PDF p. 1, abstract

Machine-checked · 2026-10-31

Source details and check notes

Authors: Chelsea Finn; Sergey Levine; Pieter Abbeel
Version: arXiv:1603.00448v1, submitted 1 March 2016 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 9c359d5ec4d7fcaeebd68e9bf81b8780f9d88906f1d2347c91d18ba95487a9e1). The author list is the byline as printed on the first page.
Version date: 2016-03-01
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Chelsea Finn, Sergey Levine, Pieter Abbeel, “Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization”, 2016-03-01.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, key papers (research/key-papers). The quotation (24 words) matched the text retrieved for the candidate pack on 2026-10-28 (research/key-papers/verification.json). Matching ignored differences of whitespace, case, quotation marks, dashes and line-break hyphens, so the source’s typography can differ. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. A paper or other publication describes its own contribution, quoted from the expression the record names, usually its abstract or introduction. The record is the document’s own description, not an assessment: a claim of priority or novelty it contains is the document’s claim, flagged as such, and says nothing about earlier or later work.

Date. Date of the arXiv version quoted, submitted 1 March 2016 (UTC); later versions may word it differently.

Record ID kp-states-guided-cost-learning-deep-inverse-2016-1