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  1. Home
  2. Signal Canvas
  3. Cognition to Control - Multi-Agent Learning for Human-Humano
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Cognition to Control - Multi-Agent Learning for Human-Humanoid Collaborative Transport

Fresh1d ago
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Viability
0.0/10

Compared to this week’s papers

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 27

Proof: no_code

Distribution: unknown

Source paper: Cognition to Control - Multi-Agent Learning for Human-Humanoid Collaborative Transport

PDF: https://arxiv.org/pdf/2603.03768v1

First buyer signal: unknown

Distribution channel: unknown

Last proof check: 2026-03-19T18:48:05.835633+00:00

Starting…

Dimensions overall score 3.0

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Prior Work
Cybo-Waiter: A Physical Agentic Framework for Humanoid Whole-Body Locomotion-Manipulation
Score 3.0stable
Higher Viability
Learning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement Learning
Score 5.0up
Higher Viability
Towards Human-Like Manipulation through RL-Augmented Teleoperation and Mixture-of-Dexterous-Experts VLA
Score 7.0up
Higher Viability
PCHC: Enabling Preference Conditioned Humanoid Control via Multi-Objective Reinforcement Learning
Score 7.0up
Higher Viability
MA-CoNav: A Master-Slave Multi-Agent Framework with Hierarchical Collaboration and Dual-Level Reflection for Long-Horizon Embodied VLN
Score 5.0up
Higher Viability
MetaWorld-X: Hierarchical World Modeling via VLM-Orchestrated Experts for Humanoid Loco-Manipulation
Score 7.0up
Higher Viability
Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learning
Score 7.0up
Higher Viability
ACLM: ADMM-Based Distributed Model Predictive Control for Collaborative Loco-Manipulation
Score 7.0up

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