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ARXIV:2603.06181 · ROBOTICS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.06181ROBOTICSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Evaluate and improve humanoid robot motion with a human-likeness scoring system and benchmark dataset, enabling more natural robot interactions.
Opportunity summary
Pain Evaluate and improve humanoid robot motion with a human-likeness scoring system and benchmark dataset, enabling more natural robot interactions.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Evaluate and improve humanoid robot motion with a human-likeness scoring system and benchmark dataset, enabling more natural robot interactions. Inspired by the Turing Test, we propose the Motion Turing Test, a framework that evaluates…
Humanoid robots have achieved significant progress in motion generation and control, exhibiting movements that appear increasingly natural and human-like. Inspired by the Turing Test, we propose the Motion Turing Test, a framework that evaluates…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. To address this, we propose a simple baseline model and demonstrate that it outperforms several contemporary LLM-based methods.
Robotics moved forward this cycle; last verified April 2026. Public score 8.0/10.
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Evaluate and improve humanoid robot motion with a human-likeness scoring system and benchmark dataset, enabling more natural robot interactions.
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Paper Pack
10.48550/arXiv.2603.06181Evaluate and improve humanoid robot motion with a human-likeness scoring system and benchmark dataset, enabling more natural robot interactions.
Abstract
Humanoid robots have achieved significant progress in motion generation and control, exhibiting movements that appear increasingly natural and human-like. Inspired by the Turing Test, we propose the Motion Turing Test, a framework that evaluates whether human observers can discriminate between humanoid robot and human poses using only kinematic information. To facilitate this evaluation, we present the Human-Humanoid Motion (HHMotion) dataset, which consists of 1,000 motion sequences spanning 15 action categories, performed by 11 humanoid models and 10 human subjects. All motion sequences are converted into SMPL-X representations to eliminate the influence of visual appearance. We recruited 30 annotators to rate the human-likeness of each pose on a 0-5 scale, resulting in over 500 hours of annotation. Analysis of the collected data reveals that humanoid motions still exhibit noticeable deviations from human movements, particularly in dynamic actions such as jumping, boxing, and running. Building on HHMotion, we formulate a human-likeness evaluation task that aims to automatically predict human-likeness scores from motion data. Despite recent progress in multimodal large language models, we find that they remain inadequate for assessing motion human-likeness. To address this, we propose a simple baseline model and demonstrate that it outperforms several contemporary LLM-based methods. The dataset, code, and benchmark will be publicly released to support future research in the community.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 8.0
PROBLEM
Evaluate and improve humanoid robot motion with a human-likeness scoring system and benchmark dataset, enabling more natural robot interactions. Inspired by the Turing Test, we propose the Motion Turing Test, a framework that evaluates whether human observers can discriminate be...
METHOD
Humanoid robots have achieved significant progress in motion generation and control, exhibiting movements that appear increasingly natural and human-like. Inspired by the Turing Test, we propose the Motion Turing Test, a framework that evaluates whether human observers can discr...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. To address this, we propose a simple baseline model and demonstrate that it outperforms several contemporary LLM-based methods.
WHY NOW
Robotics moved forward this cycle; last verified April 2026. Public score 8.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Evaluate and improve humanoid robot motion with a human-likeness scoring system and benchmark dataset, enabling more natural robot interactions. Inspired by the Turing Test, we propose the Motion Turing Test, a framework that evaluates whether human observers can discriminate between humanoid robot and human poses using only kinematic information.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Humanoid robots have achieved significant progress in motion generation and control, exhibiting movements that appear increasingly natural and human-like. Inspired by the Turing Test, we propose the Motion Turing Test, a framework that evaluates whether human observers can discriminate between humanoid robot and human poses using only kinematic information.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. To address this, we propose a simple baseline model and demonstrate that it outperforms several contemporary LLM-based methods.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Robotics moved forward this cycle; last verified April 2026. Public score 8.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Evaluate and improve humanoid robot motion with a human-likeness scoring system and benchmark dataset, enabling more natural robot interactions.
Segment
Robotics
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
Direct
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CITED BY
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Commercially relevant
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Build Passport
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status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
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stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
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Evidence
0 references, 0 sources, 17% evidence coverage.
Gaps
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Buyer clarity
missing
Current read
No budget owner is verified for this paper.
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Defensibility
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Defensibility signals are missing.
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
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Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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People
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Regulatory need unclassified.
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Gaps
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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TIMELINE
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BUZZ
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