ARXIV:2603.13176 · HUMAN-ROBOT COLLABORATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Perceive What Matters: Relevance-Driven Scheduling for Multimodal Streaming Perception

Q: What products could be built from this research?

Now is the ideal time because the rise of Industry 4.0 and increased adoption of collaborative robots (cobots) in manufacturing and logistics demand smarter, real-time perception. With advancements in edge computing and AI chips, there's a growing need for software that maximizes hardware efficiency, and this framework directly addresses latency issues that current parallel perception pipelines struggle with in streaming applications.

Q: What are the practical use cases?

A warehouse automation system where robots collaborate with human workers to pick and pack items. The product schedules perception modules (e.g., object detection, pose estimation) based on real-time scene relevance, ensuring the robot quickly identifies human gestures or obstacles while ignoring redundant visual data, reducing collisions and speeding up task completion.

arXiv

A lightweight perception scheduling framework that optimizes multimodal streaming for human-robot collaboration by reducing latency and enhancing efficiency.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain A lightweight perception scheduling framework that optimizes multimodal streaming for human-robot collaboration by reducing latency and enhancing efficiency.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A lightweight perception scheduling framework that optimizes multimodal streaming for human-robot collaboration by reducing latency and enhancing efficiency. While executing multiple perception modules on a frame-by-frame basis enhances perception quality in offline settings, it…

METHOD

Full abstract

In modern human-robot collaboration (HRC) applications, multiple perception modules jointly extract visual, auditory, and contextual cues to achieve comprehensive scene understanding, enabling the robot to provide appropriate assistance to human agents intelligently. While executing multiple perception modules on a frame-by-frame basis enhances perception quality in offline settings, it inevitably accumulates latency, leading to a substantial decline in system performance in streaming perception scenarios. Recent work in scene understanding, termed Relevance, has established a solid foundation for developing efficient methodologies in HRC. However, modern perception pipelines still face challenges related to information redundancy and suboptimal allocation of computational resources. Drawing inspiration from the Relevance concept and the information sparsity in HRC events, we propose a novel lightweight perception scheduling framework that efficiently leverages output from previous frames to estimate and schedule necessary perception modules in real-time based on scene context. The experimental results demonstrate that the proposed perception scheduling framework effectively reduces computational latency by up to 27.52% compared to conventional parallel perception pipelines, while also achieving a 72.73% improvement in MMPose activation recall. Additionally, the framework demonstrates high keyframe accuracy, achieving rates of up to 98%. The results validate the framework's capability to enhance real-time perception efficiency without significantly compromising accuracy. The framework shows potential as a scalable and systematic solution for multimodal streaming perception systems in HRC.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. In modern human-robot collaboration (HRC) applications, multiple perception modules jointly extract visual, auditory, and contextual cues to achieve comprehensive scene understanding, enabling the robot…

WHY NOW

Human-Robot Collaboration moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainA lightweight perception scheduling framework that optimizes multimodal streaming for human-robot collaboration by reducing latency and enhancing efficiency.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A lightweight perception scheduling framework that optimizes multimodal streaming for human-robot collaboration by reducing latency and enhancing efficiency.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.13176 · HUMAN-ROBOT COLLABORATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Perceive What Matters: Relevance-Driven Scheduling for Multimodal Streaming Perception

arXiv

A lightweight perception scheduling framework that optimizes multimodal streaming for human-robot collaboration by reducing latency and enhancing efficiency.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain A lightweight perception scheduling framework that optimizes multimodal streaming for human-robot collaboration by reducing latency and enhancing efficiency.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Human-Robot Collaboration moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainA lightweight perception scheduling framework that optimizes multimodal streaming for human-robot collaboration by reducing latency and enhancing efficiency.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A lightweight perception scheduling framework that optimizes multimodal streaming for human-robot collaboration by reducing latency and enhancing efficiency.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.13176

Perceive What Matters: Relevance-Driven Scheduling for Multimodal Streaming Perception

A lightweight perception scheduling framework that optimizes multimodal streaming for human-robot collaboration by reducing latency and enhancing efficiency.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback24 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Human-Robot Collaboration moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A lightweight perception scheduling framework that optimizes multimodal streaming for human-robot collaboration by reducing latency and enhancing efficiency. While executing multiple perception modules on a frame-by-frame basis enhances perception quality in offline settings, it inevitably accumulates latency, leading to a substantial decline in system performance in streaming perception scenarios.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
In modern human-robot collaboration (HRC) applications, multiple perception modules jointly extract visual, auditory, and contextual cues to achieve comprehensive scene understanding, enabling the robot to provide appropriate assistance to human agents intelligently. While executing multiple perception modules on a frame-by-frame basis enhances perception quality in offline settings, it inevitably accumulates latency, leading to a substantial decline in system performance in streaming perception scenarios.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In modern human-robot collaboration (HRC) applications, multiple perception modules jointly extract visual, auditory, and contextual cues to achieve comprehensive scene understanding, enabling the robot to provide appropriate assistance to human agents intelligently.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Human-Robot Collaboration moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Human-Robot Collaboration

Competitors

not indexed

Competitive landscape

A lightweight perception scheduling framework that optimizes multimodal streaming for human-robot collaboration by reducing latency and enhancing efficiency.

Segment

Human-Robot Collaboration

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.13176 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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Bluesky

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(24)

Eye Movement Feature-Guided Signal De-Drifting in Electrooculography Systems

2025Lianming Hu, Xiaotong Zhang et al.

Bayesian Intention for Enhanced Human Robot Collaboration

2024Vanessa Hernandez-Cruz, Xiaotong Zhang et al.

Relevance for Human Robot Collaboration

2024Xiaotong Zhang, Dingcheng Huang et al.

How Does Perception Affect Safety: New Metrics and Strategy

2023Xiaotong Zhang, Jinger Chong et al.

VIoTGPT: Learning to Schedule Vision Tools in LLMs towards Intelligent Video Internet of Things

2023Yaoyao Zhong, Mengshi Qi et al.

Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Prediction

2023Aadi Kothari, Tony Tohme et al.

Latency vs precision: Stability preserving perception scheduling

2023R. Aldana-López, R. Aragüés et al.

Keyframe Selection for Visual Localization and Mapping Tasks: A Systematic Literature Review

2023N. Dias, Gustavo Teodoro Laureano et al.

Context-Aware Streaming Perception in Dynamic Environments

2022Gur-Eyal Sela, Ionel Gog et al.

BoT-SORT: Robust Associations Multi-Pedestrian Tracking

2022Nir Aharon, Roy Orfaig et al.

Self-Cueing Real-Time Attention Scheduling in Criticality-Aware Visual Machine Perception

2022Shengzhong Liu, Xinzhe Fu et al.

Event Detection in Soccer Video Based on Self-Attention

2020Sifan Ma, En Shao et al.

Self-Supervised Learning to Detect Key Frames in Videos

2020X. Yan, Syed Zulqarnain Gilani et al.

Comparative Analysis of Key-frame Extraction Techniques For Video Summarization

2020Vishal Parikh, Jay Mehta et al.

MediaPipe: A Framework for Building Perception Pipelines

2019Camillo Lugaresi, Jiuqiang Tang et al.

Detecting the Starting Frame of Actions in Video

2019Iljung S. Kwak, D. Kriegman et al.

Unsupervised Extraction of Video Highlights via Robust Recurrent Auto-Encoders

2015Huan Yang, Baoyuan Wang et al.

Video summarization via minimum sparse reconstruction

2015Shaohui Mei, Genliang Guan et al.

Key-frame Extraction by Analysis of Histograms of Video Frames Using Statistical Methods

2015Sheena C V, N. Narayanan

Sampling-based robotic information gathering algorithms

2014Geoffrey A. Hollinger, G. Sukhatme

Showing 20 of 24 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkHarmonizing Real-Time Constraints and Long-Horizon Reasoning: An Asynchronous Agentic Framework for Dynamic Scheduling

7.0

Prior WorkRocks, Pebbles and Sand: Modality-aware Scheduling for Multimodal Large Language Model Inference

7.0

Prior WorkProgressive Online Video Understanding with Evidence-Aligned Timing and Transparent Decisions

7.0

Prior WorkReDAG-RT: Global Rate-Priority Scheduling for Real-Time Multi-DAG Execution in ROS 2

7.0

Prior WorkThink as Needed: Geometry-Driven Adaptive Perception for Autonomous Driving

7.0

Prior WorkMulti-Cycle Spatio-Temporal Adaptation in Human-Robot Teaming

7.0

Prior WorkPRAM-R: A Perception-Reasoning-Action-Memory Framework with LLM-Guided Modality Routing for Adaptive Autonomous Driving

7.0

Prior WorkPAS3R: Pose-Adaptive Streaming 3D Reconstruction for Long Video Sequences

7.0

Prior WorkFaster-HEAL: An Efficient and Privacy-Preserving Collaborative Perception Framework for Heterogeneous Autonomous Vehicles

7.0

Prior WorkOmniStream: Mastering Perception, Reconstruction and Action in Continuous Streams

7.0

Extension

none indexed

Commercially relevant

none indexed

Conflicting

none indexed

Related Resources

Owned Distribution

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.13176
Route: /paper/perceive-what-matters-relevance-driven-scheduling-for-multimodal-streaming-perception
Active tab: read
Artifact: perceive-what-matters-relevance-driven-scheduling-for-multimodal-streaming-perception

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/perceive-what-matters-relevance-driven-scheduling-for-multimodal-streaming-perception/paper-pack
REST build passport API/api/v1/paper/perceive-what-matters-relevance-driven-scheduling-for-multimodal-streaming-perception/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/perceive-what-matters-relevance-driven-scheduling-for-multimodal-streaming-perception

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Perceive What Matters: Relevance-Driven Scheduling for Multimodal Streaming Perception

Canonical ID perceive-what-matters-relevance-driven-scheduling-for-multimodal-streaming-perception | Route /paper/perceive-what-matters-relevance-driven-scheduling-for-multimodal-streaming-perception

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/perceive-what-matters-relevance-driven-scheduling-for-multimodal-streaming-perception

MCP example

{
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}

source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Perceive What Matters: Relevance-Driven Scheduling for Multimodal Streaming Perception

/buildability/perceive-what-matters-relevance-driven-scheduling-for-multimodal-streaming-perception

Watchwatch

Subject: Perceive What Matters: Relevance-Driven Scheduling for Multimodal Streaming Perception

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/perceive-what-matters-relevance-driven-scheduling-for-multimodal-streaming-perception

Paper ref

perceive-what-matters-relevance-driven-scheduling-for-multimodal-streaming-perception

arXiv id

2603.13176

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

ecf87e6484c01959767471623f589b016dcd15cd6bb80869acb77aff7c955304

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Perceive What Matters: Relevance-Driven Scheduling for Multimodal Streaming Perception

Perceive What Matters: Relevance-Driven Scheduling for Multimodal Streaming Perception

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Claim map

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References(24)

Related Papers

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Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline