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Humanoid robots learn autonomous object lifting and delivery via reinforcement learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed AdaptManip, a novel framework for humanoid robots to autonomously navigate, lift, and deliver objects. Unlike previous methods relying on human demonstrations, AdaptManip utilizes reinforcement learning without human input to train a robust policy. The system integrates a real-time object state estimator, a whole-body locomotion policy with residual manipulation control, and a LiDAR-based localization system, all trained in simulation and deployed zero-shot on real hardware. AI

IMPACT This research advances autonomous capabilities in humanoid robots, potentially leading to more versatile robotic assistants in logistics and manufacturing.

RANK_REASON The cluster describes a research paper detailing a new framework for robotic manipulation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Humanoid robots learn autonomous object lifting and delivery via reinforcement learning

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The cluster describes a research paper detailing a new framework for robotic manipulation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Morgan Byrd, Donghoon Baek, Kartik Garg, Hyunyoung Jung, Daesol Cho, Maks Sorokin, Robert Wright, Sehoon Ha ·

    AdaptManip: Learning Adaptive Whole-Body Object Lifting and Delivery with Online Recurrent State Estimation

    arXiv:2602.14363v2 Announce Type: replace-cross Abstract: This paper presents Adaptive Whole-body Loco-Manipulation, AdaptManip, a fully autonomous framework for humanoid robots to perform integrated navigation, object lifting, and delivery. Unlike prior imitation learning-based …

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