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Multi-skill Mobile Manipulation for Object Rearrangement

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arxiv 2209.02778 v1 pith:X75VQLIA submitted 2022-09-06 cs.RO cs.LG

Jiayuan Gu , Devendra Singh Chaplot , Hao Su , Jitendra Malik This is my paper
classification cs.ROcs.LG
keywords manipulationmobileskillmultiplenavigationobjectskillsstationary
verification ladder T0 review T1 audit T2 compute T3 formal
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We study a modular approach to tackle long-horizon mobile manipulation tasks for object rearrangement, which decomposes a full task into a sequence of subtasks. To tackle the entire task, prior work chains multiple stationary manipulation skills with a point-goal navigation skill, which are learned individually on subtasks. Although more effective than monolithic end-to-end RL policies, this framework suffers from compounding errors in skill chaining, e.g., navigating to a bad location where a stationary manipulation skill can not reach its target to manipulate. To this end, we propose that the manipulation skills should include mobility to have flexibility in interacting with the target object from multiple locations and at the same time the navigation skill could have multiple end points which lead to successful manipulation. We operationalize these ideas by implementing mobile manipulation skills rather than stationary ones and training a navigation skill trained with region goal instead of point goal. We evaluate our multi-skill mobile manipulation method M3 on 3 challenging long-horizon mobile manipulation tasks in the Home Assistant Benchmark (HAB), and show superior performance as compared to the baselines.

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Cited by 5 Pith papers

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

  1. Mobile Manipulation with Active Inference for Long-Horizon Rearrangement Tasks

    cs.RO 2025-07 conditional novelty 7.0 of 10

    A hierarchical active inference agent with a whole-body controller achieves 66.5% average success on Habitat's three long-horizon rearrangement tasks, beating the compared RL baselines (54.7%).

  2. FloAff-Kitchen: Bridging Navigation and Manipulation via Canonical and Progressive Floor Affordance Learning

    cs.RO 2026-07 conditional novelty 5.0 of 10

    Canonical local floor geometry plus progressive skill adaptation predicts robot base placements that raise simulated kitchen mobile-manipulation success over prior FloAff methods.

  3. Diffusion Policy for Coordinated Control of a Nonholonomic Mobile Base and Dual Arms in Door Opening and Passing

    cs.RO 2026-05 unverdicted novelty 5.0 of 10

    A diffusion policy learns coordinated control of a mobile base and dual arms to open and traverse damped pull doors in a single end-to-end visuomotor model.

  4. N2M: Bridging Navigation and Manipulation by Learning Pose Preference from Rollout

    cs.RO 2025-09 conditional novelty 5.0 of 10

    N2M predicts preferable base poses for manipulation policies from ego-centric point clouds, learned from rollouts, lifting success from 3% to 54% in the PnPCounterToCab task.

  5. MoTo: A Zero-shot Plug-in Interaction-aware Navigation for General Mobile Manipulation

    cs.RO 2025-09 conditional novelty 4.0 of 10

    MoTo turns existing fixed-base manipulation models into mobile manipulators by using VLM-picked contact keypoints and trajectory optimization to find docking points, with no training of MoTo itself.

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