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Harmonic Mobile Manipulation

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arxiv 2312.06639 v3 pith:AV33IPSV submitted 2023-12-11 cs.RO cs.AIcs.CVcs.LG

Harmonic Mobile Manipulation

Ruihan Yang , Yejin Kim , Rose Hendrix , Aniruddha Kembhavi , Xiaolong Wang , Kiana Ehsani This is my paper
classification cs.RO cs.AIcs.CVcs.LG
keywords manipulationtaskscoordinateddeploymentharmonicmmmobilenavigationrequiring
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Recent advancements in robotics have enabled robots to navigate complex scenes or manipulate diverse objects independently. However, robots are still impotent in many household tasks requiring coordinated behaviors such as opening doors. The factorization of navigation and manipulation, while effective for some tasks, fails in scenarios requiring coordinated actions. To address this challenge, we introduce, HarmonicMM, an end-to-end learning method that optimizes both navigation and manipulation, showing notable improvement over existing techniques in everyday tasks. This approach is validated in simulated and real-world environments and adapts to novel unseen settings without additional tuning. Our contributions include a new benchmark for mobile manipulation and the successful deployment with only RGB visual observation in a real unseen apartment, demonstrating the potential for practical indoor robot deployment in daily life. More results are on our project site: https://rchalyang.github.io/HarmonicMM/

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Whole-Body Mobile Manipulation using Offline Reinforcement Learning on Sub-optimal Controllers

    cs.RO 2026-04 unverdicted novelty 6.0

    WHOLE-MoMa improves whole-body mobile manipulation by applying offline RL with Q-chunking to demonstrations from randomized sub-optimal controllers, outperforming baselines and transferring to real robots without tele...

  2. InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation

    cs.RO 2026-02 unverdicted novelty 6.0

    InCoM achieves 23-28% higher success rates in mobile manipulation tasks by inferring motion intent for adaptive perception and decoupling base-arm action generation.

  3. InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation

    cs.RO 2026-02 conditional novelty 6.0

    InCoM reports 23–28 percentage-point success-rate gains in mobile manipulation benchmarks by dynamically reweighting multi-scale perception via inferred motion intent and decoupling base-arm action generation with flo...

  4. Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

    cs.RO 2024-01 conditional novelty 6.0

    A low-cost whole-body teleoperation system enables effective imitation learning for complex bimanual mobile manipulation by co-training on mobile and static demonstration datasets.