ARXIV:2603.05111 · ROBOTICS · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

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

SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty

Q: What is the startup potential of "SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipu"?

A perceptive shared autonomy system that adapts robotic manipulation based on deep learning uncertainty for industrial use.

Q: What products could be built from this research?

Develop a robotic manipulation system that enhances existing industrial robots by integrating uncertainty-aware shared autonomy, significantly improving safety and flexibility in uncertain environments.

Q: What are the practical use cases?

Automating industrial inspection tasks in hazardous areas using robotic manipulators that can transition control levels based on perception uncertainty, thereby reducing human risk and improving operational efficiency.

Q: What industries could this research disrupt?

Could replace manual inspection and existing robotic systems that lack the ability to dynamically adjust autonomy based on perception uncertainties.

arXiv

SPIRIT enhances robotic manipulation by dynamically adjusting autonomy based on deep learning uncertainty, ensuring reliable performance in uncertain conditions.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain SPIRIT enhances robotic manipulation by dynamically adjusting autonomy based on deep learning uncertainty, ensuring reliable performance in uncertain conditions.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

SPIRIT enhances robotic manipulation by dynamically adjusting autonomy based on deep learning uncertainty, ensuring reliable performance in uncertain conditions. We propose a concept termed perceptive shared autonomy, in which uncertainty estimates from DL based…

METHOD

Full abstract

Deep learning (DL) has enabled impressive advances in robotic perception, yet its limited robustness and lack of interpretability hinder reliable deployment in safety critical applications. We propose a concept termed perceptive shared autonomy, in which uncertainty estimates from DL based perception are used to regulate the level of autonomy. Specifically, when the robot's perception is confident, semi-autonomous manipulation is enabled to improve performance; when uncertainty increases, control transitions to haptic teleoperation for maintaining robustness. In this way, high-performing but uninterpretable DL methods can be integrated safely into robotic systems. A key technical enabler is an uncertainty aware DL based point cloud registration approach based on the so called Neural Tangent Kernels (NTK). We evaluate perceptive shared autonomy on challenging aerial manipulation tasks through a user study of 15 participants and realization of mock-up industrial scenarios, demonstrating reliable robotic manipulation despite failures in DL based perception. The resulting system, named SPIRIT, improves both manipulation performance and system reliability. SPIRIT was selected as a finalist of a major industrial innovation award.

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. Specifically, when the robot's perception is confident, semi-autonomous manipulation is enabled to improve performance; when uncertainty increases, control transitions to haptic teleoperation for maintaining…

WHY NOW

Robotics moved forward this cycle; last verified April 2026. Public score 6.0/10.

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

Opportunity summary

Score6.0

PainSPIRIT enhances robotic manipulation by dynamically adjusting autonomy based on deep learning uncertainty, ensuring reliable performance in uncertain conditions.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

SPIRIT enhances robotic manipulation by dynamically adjusting autonomy based on deep learning uncertainty, ensuring reliable performance in uncertain conditions.

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

ARXIV:2603.05111 · ROBOTICS · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

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

SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty

arXiv

SPIRIT enhances robotic manipulation by dynamically adjusting autonomy based on deep learning uncertainty, ensuring reliable performance in uncertain conditions.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain SPIRIT enhances robotic manipulation by dynamically adjusting autonomy based on deep learning uncertainty, ensuring reliable performance in uncertain conditions.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Robotics moved forward this cycle; last verified April 2026. Public score 6.0/10.

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

Opportunity summary

Score6.0

PainSPIRIT enhances robotic manipulation by dynamically adjusting autonomy based on deep learning uncertainty, ensuring reliable performance in uncertain conditions.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

SPIRIT enhances robotic manipulation by dynamically adjusting autonomy based on deep learning uncertainty, ensuring reliable performance in uncertain conditions.

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

Paper Pack

10.48550/arXiv.2603.05111

SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty

SPIRIT enhances robotic manipulation by dynamically adjusting autonomy based on deep learning uncertainty, ensuring reliable performance in uncertain conditions.

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; 33% coverage.

What was readable

linkedon filenot materializedderived fallback47 indexednot indexed

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

Viability

6.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Robotics moved forward this cycle; last verified April 2026. Public score 6.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
SPIRIT enhances robotic manipulation by dynamically adjusting autonomy based on deep learning uncertainty, ensuring reliable performance in uncertain conditions. We propose a concept termed perceptive shared autonomy, in which uncertainty estimates from DL based perception are used to regulate the level of autonomy.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Deep learning (DL) has enabled impressive advances in robotic perception, yet its limited robustness and lack of interpretability hinder reliable deployment in safety critical applications. We propose a concept termed perceptive shared autonomy, in which uncertainty estimates from DL based perception are used to regulate the level of autonomy.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Specifically, when the robot's perception is confident, semi-autonomous manipulation is enabled to improve performance; when uncertainty increases, control transitions to haptic teleoperation for maintaining robustness.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Robotics moved forward this cycle; last verified April 2026. Public score 6.0/10.
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

Robotics

Competitors

not indexed

Competitive landscape

SPIRIT enhances robotic manipulation by dynamically adjusting autonomy based on deep learning uncertainty, ensuring reliable performance in uncertain conditions.

Segment

Robotics

Adoption evidence

No public code link in the paper record yet

Commercial read

6.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.05111 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

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

References(47)

Human-in-the-Loop Gaussian Splatting for Robotic Teleoperation

2026Yongseok Lee, Hyunsu Kim et al.

A roadmap for AI in robotics

2025A. Billard, Alin Albu-Schaeffer et al.

Teleoperation of a Suspended Aerial Manipulator Using a Handheld Camera With an IMU

2025Miguel Arpa Perozo, Ethan Niddam et al.

Perception-Driven Shared Control Architecture for Agricultural Robots Performing Harvesting Tasks

2024Jozsef Palmieri, P. D. Lillo et al.

Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions

2024Michelle D. Zhao, Reid G. Simmons et al.

A Probabilistic Approach to Multi-Modal Adaptive Virtual Fixtures

2024Maximilian Mühlbauer, Thomas Hulin et al.

Introspective Perception for Long-Term Aerial Telemanipulation With Virtual Reality

2024Jongseok Lee, R. Balachandran et al.

Shared Autonomy of a Robotic Manipulator for Grasping Under Human Intent Uncertainty Using POMDPs

2024J.-Anne Yow, N. P. Garg et al.

Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning

2023Jianxiang Feng, Jongseok Lee et al.

A Suspended Aerial Manipulation Avatar for Physical Interaction in Unstructured Environments

2023Fanyi Kong, Grazia Zambella et al.

Uncertainty-Aware Haptic Shared Control With Humanoid Robots for Flexible Object Manipulation

2023Takumi Hara, Takashi Sato et al.

Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks

2023Dominik Schnaus, Jongseok Lee et al.

BlenderProc2: A Procedural Pipeline for Photorealistic Rendering

2023Maximilian Denninger, Dominik Winkelbauer et al.

Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities

2022Jongseok Lee, R. Balachandran et al.

Past, Present, and Future of Aerial Robotic Manipulators

2022A. Ollero, M. Tognon et al.

Autonomy in Physical Human-Robot Interaction: A Brief Survey

2021M. Selvaggio, Marco Cognetti et al.

Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes

2021Jongseo Lee, Jianxiang Feng et al.

ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning

2021Ryan Hoque, A. Balakrishna et al.

A survey of uncertainty in deep neural networks

2021J. Gawlikowski, Cedrique Rovile Njieutcheu Tassi et al.

Hierarchical Control of Redundant Aerial Manipulators with Enhanced Field of View

2021Andre Coelho, Y. Sarkisov et al.

Showing 20 of 47 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 WorkEnd-to-end Optimization of Belief and Policy Learning in Shared Autonomy Paradigms

6.0

Extension

Builds On ThisSafe Embodied AI for Long-horizon Tasks: A Cross-layer Analysis of Robotic Manipulation

0.0

Commercially relevant

Higher ViabilityManiDreams: An Open-Source Library for Robust Object Manipulation via Uncertainty-aware Task-specific Intuitive Physics

7.0

Higher ViabilityTRIAGE: Type-Routed Interventions via Aleatoric-Epistemic Gated Estimation in Robotic Manipulation and Adaptive Perception -- Don't Treat All Uncertainty the Same

8.0

Higher ViabilityEfficient and Reliable Teleoperation through Real-to-Sim-to-Real Shared Autonomy

7.0

Higher ViabilityTowards Human-Like Manipulation through RL-Augmented Teleoperation and Mixture-of-Dexterous-Experts VLA

7.0

Higher ViabilityDreamControl-v2: Simpler and Scalable Autonomous Humanoid Skills via Trainable Guided Diffusion Priors

8.0

Higher ViabilityUncertainty Mitigation and Intent Inference: A Dual-Mode Human-Machine Joint Planning System

7.0

Higher ViabilitySSP: Safety-guaranteed Surgical Policy via Joint Optimization of Behavioral and Spatial Constraints

7.0

Higher ViabilityProvably Safe Trajectory Generation for Manipulators Under Motion and Environmental Uncertainties

7.0

Conflicting

none indexed

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Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

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: 2603.05111
Route: /paper/spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty
Active tab: read
Artifact: spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty/paper-pack
REST build passport API/api/v1/paper/spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty/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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  "paper_id": "16876fdc-5c5b-459c-b35d-dfbe03ed90e8",
  "arxiv_id": "2603.05111",
  "canonical_route": "/paper/spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty",
  "endpoints": {
    "paper_pack": "/api/v1/paper/spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty/paper-pack",
    "build_passport": "/api/v1/paper/spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

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/spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty

stale

Proof freshness: stale
Proof status: unverified
Display score: 6/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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

SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty

Canonical ID spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty | Route /paper/spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.05111"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty",
  "normalized_query": "2603.05111",
  "route": "/paper/spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty",
  "paper_ref": "spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

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

Paper proof page receipt window

Watch and verify: SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty

/buildability/spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty

Watchwatch

Subject: SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty

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/spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty

Paper ref

spirit-perceptive-shared-autonomy-for-robust-robotic-manipulation-under-deep-learning-uncertainty

arXiv id

2603.05111

Freshness

Generated at

2026-03-19T18:48:05.835Z

Evidence freshness

stale

Last verification

2026-03-19T18:48:05.835Z

Sources

References

Coverage

33%

Hash state

Lineage hash

6dff38bee8e111a3fe5ca6feea5d77dbd8a5773431978f4015999a60427af43f

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: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed 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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SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty

SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty

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