ARXIV:2602.10514 · ROBOTICS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning

arXiv

Develop a communication-free cooperative jumping capability for quadrupedal robots using advanced multi-agent reinforcement learning.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Develop a communication-free cooperative jumping capability for quadrupedal robots using advanced multi-agent reinforcement learning.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop a communication-free cooperative jumping capability for quadrupedal robots using advanced multi-agent reinforcement learning. To transcend these boundaries, we introduce Co-jump, a cooperative task where two quadrupedal robots synchronize to execute jumps far beyond…

METHOD

Full abstract

While single-agent legged locomotion has witnessed remarkable progress, individual robots remain fundamentally constrained by physical actuation limits. To transcend these boundaries, we introduce Co-jump, a cooperative task where two quadrupedal robots synchronize to execute jumps far beyond their solo capabilities. We tackle the high-impulse contact dynamics of this task under a decentralized setting, achieving synchronization without explicit communication or pre-specified motion primitives. Our framework leverages Multi-Agent Proximal Policy Optimization (MAPPO) enhanced by a progressive curriculum strategy, which effectively overcomes the sparse-reward exploration challenges inherent in mechanically coupled systems. We demonstrate robust performance in simulation and successful transfer to physical hardware, executing multi-directional jumps onto platforms up to 1.5 m in height. Specifically, one of the robots achieves a foot-end elevation of 1.1 m, which represents a 144% improvement over the 0.45 m jump height of a standalone quadrupedal robot, demonstrating superior vertical performance. Notably, this precise coordination is achieved solely through proprioceptive feedback, establishing a foundation for communication-free collaborative locomotion in constrained environments.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. We demonstrate robust performance in simulation and successful transfer to physical hardware, executing multi-directional jumps onto platforms up to 1.5 m in height.

WHY NOW

Robotics moved forward this cycle; last verified April 2026. Public score 5.0/10.

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

Opportunity summary

Score5.0

PainDevelop a communication-free cooperative jumping capability for quadrupedal robots using advanced multi-agent reinforcement learning.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a communication-free cooperative jumping capability for quadrupedal robots using advanced multi-agent reinforcement learning.

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

ARXIV:2602.10514 · ROBOTICS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning

arXiv

Develop a communication-free cooperative jumping capability for quadrupedal robots using advanced multi-agent reinforcement learning.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Develop a communication-free cooperative jumping capability for quadrupedal robots using advanced multi-agent reinforcement learning.

Evidence 0 refs | 0 sources | 17% 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 5.0/10.

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

Opportunity summary

Score5.0

PainDevelop a communication-free cooperative jumping capability for quadrupedal robots using advanced multi-agent reinforcement learning.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a communication-free cooperative jumping capability for quadrupedal robots using advanced multi-agent reinforcement learning.

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

Paper Pack

10.48550/arXiv.2602.10514

Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning

Develop a communication-free cooperative jumping capability for quadrupedal robots using advanced multi-agent reinforcement learning.

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 fallback39 indexednot indexed

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

Viability

5.0

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 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Develop a communication-free cooperative jumping capability for quadrupedal robots using advanced multi-agent reinforcement learning. To transcend these boundaries, we introduce Co-jump, a cooperative task where two quadrupedal robots synchronize to execute jumps far beyond their solo capabilities.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
While single-agent legged locomotion has witnessed remarkable progress, individual robots remain fundamentally constrained by physical actuation limits. To transcend these boundaries, we introduce Co-jump, a cooperative task where two quadrupedal robots synchronize to execute jumps far beyond their solo capabilities.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. We demonstrate robust performance in simulation and successful transfer to physical hardware, executing multi-directional jumps onto platforms up to 1.5 m in height.
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 5.0/10.
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

Develop a communication-free cooperative jumping capability for quadrupedal robots using advanced multi-agent reinforcement learning.

Segment

Robotics

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.10514 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(39)

MARG: MAstering Risky Gap Terrains for Legged Robots With Elevation Mapping

2025Yinzhao Dong, Ji Ma et al.

Multi-Quadruped Cooperative Object Transport: Learning Decentralized Pinch-Lift-Move

2025Bikram Pandit, Aayam Shrestha et al.

Collaborative Loco-Manipulation for Pick-and-Place Tasks with Dynamic Reward Curriculum

2025Tianxu An, Flavio De Vincenti et al.

Learning aggressive animal locomotion skills for quadrupedal robots solely from monocular videos

2025Liu Zhao, Zeren Luo et al.

OmniNet: Omnidirectional Jumping Neural Network With Height-Awareness for Quadrupedal Robots

2025Yimin Han, Jiahui Zhang et al.

Learning stable bipedal locomotion skills for quadrupedal robots on challenging terrains with automatic fall recovery

2025Erdong Xiao, Yinzhao Dong et al.

Stable Imitation of Multigait and Bipedal Motions for Quadrupedal Robots Over Uneven Terrains

2025Erdong Xiao, Yinzhao Dong et al.

High-speed control and navigation for quadrupedal robots on complex and discrete terrain

2025Hyeongjun Kim, Hyunsik Oh et al.

Learning Humanoid Standing-up Control across Diverse Postures

2025Tao Huang, Junli Ren et al.

Cooperative dual-actor proximal policy optimization algorithm for multi-robot complex control task

2025Jacky Baltes, Ilham Akbar et al.

Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing

2024Yuming Feng, Chuye Hong et al.

Online Omnidirectional Jumping Trajectory Planning for Quadrupedal Robots on Uneven Terrains

2024Linzhu Yue, Zhitao Song et al.

Stage-Wise Reward Shaping for Acrobatic Robots: A Constrained Multi-Objective Reinforcement Learning Approach

2024Dohyeong Kim, Hyeokjin Kwon et al.

SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience

2024Elliot Chane-Sane, Joseph Amigo et al.

An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning

2024Christopher Amato

PIE: Parkour With Implicit-Explicit Learning Framework for Legged Robots

2024Shixin Luo, Songbo Li et al.

Learning Highly Dynamic Behaviors for Quadrupedal Robots

2024Chong Zhang, Jiapeng Sheng et al.

Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion

2024Tairan He, Chong Zhang et al.

Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-Free Design

2024Vassil Atanassov, Jiatao Ding et al.

Multi-agent Reinforcement Learning: A Comprehensive Survey

2023Dom Huh, Prasant Mohapatra

Showing 20 of 39 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 WorkLearning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement Learning

5.0

Extension

Builds On ThisSafety-Critical Centralized Nonlinear MPC for Cooperative Payload Transportation by Two Quadrupedal Robots

4.0

Commercially relevant

Higher ViabilityDual-Horizon Hybrid Internal Model for Low-Gravity Quadrupedal Jumping with Hardware-in-the-Loop Validation

7.0

Higher ViabilityUnderactuated multimodal jumping robot for extraterrestrial exploration

7.0

Higher ViabilityMulti-Gait Learning for Humanoid Robots Using Reinforcement Learning with Selective Adversarial Motion Prior

7.0

Higher ViabilityADMM-Based Distributed MPC with Control Barrier Functions for Safe Multi-Robot Quadrupedal Locomotion

7.0

Higher ViabilityPerceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching

7.0

Higher ViabilityRL-Augmented MPC for Non-Gaited Legged and Hybrid Locomotion

8.0

Conflicting

Competing ApproachLearning Dynamic Pick-and-Place for a Legged Manipulator

4.0

Competing ApproachA Physical Imitation Learning Pipeline for Energy-Efficient Quadruped Locomotion Assisted by Parallel Elastic Joint

3.0

Related Resources

assistive robotics(glossary)
How does Multi-Graph Search improve robotics?(question)
What is the impact of AI on robotics?(question)
Why is quick iteration important in robotics?(question)
Robotics – Use Cases(use_case)
Robotics and Automation – Use Cases(use_case)

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: 2602.10514
Route: /paper/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning
Active tab: read
Artifact: co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning/paper-pack
REST build passport API/api/v1/paper/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning/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

{
  "contract_version": "paper-r2",
  "paper_id": "42733b19-d8f1-4e30-8988-af67cf41bdaf",
  "arxiv_id": "2602.10514",
  "canonical_route": "/paper/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning",
  "endpoints": {
    "paper_pack": "/api/v1/paper/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning/paper-pack",
    "build_passport": "/api/v1/paper/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning/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/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
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

Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning

Canonical ID co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning | Route /paper/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.10514"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning",
  "normalized_query": "2602.10514",
  "route": "/paper/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning",
  "paper_ref": "co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning",
  "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: Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning

/buildability/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning

Watchwatch

Subject: Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning

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/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning

Paper ref

co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning

arXiv id

2602.10514

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

7819f1ae91528038d353e97df4a118fd21211aea6cfa3453bfa81780669fa916

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.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "WebPage",
      "@id": "https://sciencetostartup.com/paper/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning#webpage",
      "url": "https://sciencetostartup.com/paper/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning",
      "name": "Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning",
      "description": "Develop a communication-free cooperative jumping capability for quadrupedal robots using advanced multi-agent reinforcement learning.",
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      }
    },
    {
      "@type": "ScholarlyArticle",
      "@id": "https://sciencetostartup.com/paper/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning#scholarlyArticle",
      "headline": "Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning",
      "description": "Develop a communication-free cooperative jumping capability for quadrupedal robots using advanced multi-agent reinforcement learning.",
      "url": "https://sciencetostartup.com/paper/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning",
      "sameAs": "https://arxiv.org/abs/2602.10514",
      "identifier": {
        "@type": "PropertyValue",
        "propertyID": "arXiv",
        "value": "2602.10514"
      },
      "isAccessibleForFree": true,
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      },
      "datePublished": "2026-02-11T04:28:04.000Z",
      "citation": [
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "28f87fd9de122dc2133f79d4a1fdb1079dfb602b"
          },
          "url": "https://www.semanticscholar.org/paper/28f87fd9de122dc2133f79d4a1fdb1079dfb602b"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a6af3716aec5a04ef9ad04d2370614e841f27d0a"
          },
          "url": "https://www.semanticscholar.org/paper/a6af3716aec5a04ef9ad04d2370614e841f27d0a"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "cdab9cfc590ab4d7a5be6193aa2bc7d71ff4e505"
          },
          "url": "https://www.semanticscholar.org/paper/cdab9cfc590ab4d7a5be6193aa2bc7d71ff4e505"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "6bf744e8c8b56f2972d15729de22fc4f0a939d39"
          },
          "url": "https://www.semanticscholar.org/paper/6bf744e8c8b56f2972d15729de22fc4f0a939d39"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a77770ab218f328ac6e4250ff554ed931ebbeb82"
          },
          "url": "https://www.semanticscholar.org/paper/a77770ab218f328ac6e4250ff554ed931ebbeb82"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "fd95db9e93d6cc2adddd38b292c7a2d135ede233"
          },
          "url": "https://www.semanticscholar.org/paper/fd95db9e93d6cc2adddd38b292c7a2d135ede233"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "9d0f2d40dc20bb81f411338fa9f161db5f230477"
          },
          "url": "https://www.semanticscholar.org/paper/9d0f2d40dc20bb81f411338fa9f161db5f230477"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "1e35564041ed03535e26f9af85e7ad8a52663e0b"
          },
          "url": "https://www.semanticscholar.org/paper/1e35564041ed03535e26f9af85e7ad8a52663e0b"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "df13415f41fdd7bf510ae21177c57e1af268dd98"
          },
          "url": "https://www.semanticscholar.org/paper/df13415f41fdd7bf510ae21177c57e1af268dd98"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a0ebab50b01dbe2194275d649413f928b31d0185"
          },
          "url": "https://www.semanticscholar.org/paper/a0ebab50b01dbe2194275d649413f928b31d0185"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "e949a83732b1d90bfea607857b39f3fd57b65470"
          },
          "url": "https://www.semanticscholar.org/paper/e949a83732b1d90bfea607857b39f3fd57b65470"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "8675337b1f6f95d44c7720c7600912e2d2f68ce4"
          },
          "url": "https://www.semanticscholar.org/paper/8675337b1f6f95d44c7720c7600912e2d2f68ce4"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "170211218234106787be1c21db9a5fad70f3e7f8"
          },
          "url": "https://www.semanticscholar.org/paper/170211218234106787be1c21db9a5fad70f3e7f8"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "979935ec9e7a8812028786cc7324ed03a37da48e"
          },
          "url": "https://www.semanticscholar.org/paper/979935ec9e7a8812028786cc7324ed03a37da48e"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "9ceb0bf99e756b20502cd9f80b418ea9e59d7bcc"
          },
          "url": "https://www.semanticscholar.org/paper/9ceb0bf99e756b20502cd9f80b418ea9e59d7bcc"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "521e7089b18455acb0567300387515308cf559aa"
          },
          "url": "https://www.semanticscholar.org/paper/521e7089b18455acb0567300387515308cf559aa"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "3b8700a7b5173bfde252d807be87e8fb8f248bc3"
          },
          "url": "https://www.semanticscholar.org/paper/3b8700a7b5173bfde252d807be87e8fb8f248bc3"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "c59812a09d67800d7cb893bc25a328cb24b8aa16"
          },
          "url": "https://www.semanticscholar.org/paper/c59812a09d67800d7cb893bc25a328cb24b8aa16"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a359a247fb7bad1e53719e6d5c093825cdff6600"
          },
          "url": "https://www.semanticscholar.org/paper/a359a247fb7bad1e53719e6d5c093825cdff6600"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "4768692b14a86edc398199a089e2e35ab271e2ca"
          },
          "url": "https://www.semanticscholar.org/paper/4768692b14a86edc398199a089e2e35ab271e2ca"
        }
      ],
      "additionalProperty": [
        {
          "@type": "PropertyValue",
          "propertyID": "viabilityScore",
          "value": 5
        },
        {
          "@type": "PropertyValue",
          "propertyID": "researchDomain",
          "value": "Robotics"
        }
      ]
    },
    {
      "@type": "BreadcrumbList",
      "itemListElement": [
        {
          "@type": "ListItem",
          "position": 1,
          "name": "Home",
          "item": "https://sciencetostartup.com"
        },
        {
          "@type": "ListItem",
          "position": 2,
          "name": "Robotics",
          "item": "https://sciencetostartup.com/topics"
        },
        {
          "@type": "ListItem",
          "position": 3,
          "name": "Co-jump: Cooperative Jumping with Quadrupedal Robots via Mul",
          "item": "https://sciencetostartup.com/paper/co-jump-cooperative-jumping-with-quadrupedal-robots-via-multi-agent-reinforcement-learning"
        }
      ]
    }
  ]
}

Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning

Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(39)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(39)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline