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Multi-skill Mobile Manipulation for Object Rearrangement
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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.
Forward citations
Cited by 6 Pith papers
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Mobile Manipulation with Active Inference for Long-Horizon Rearrangement Tasks
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%).
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ManiTaskGen: A Comprehensive Task Generator for Benchmarking and Improving Vision-Language Agents on Embodied Decision-Making
ManiTaskGen automatically generates diverse, feasible mobile manipulation tasks from any input scene, and uses them to benchmark and improve vision-language robot agents.
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A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards
IKER uses VLM-generated keypoint rewards to train manipulation policies in simulation that transfer to a real robot, enabling multi-step tasks and replanning.
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FloAff-Kitchen: Bridging Navigation and Manipulation via Canonical and Progressive Floor Affordance Learning
Canonical local floor geometry plus progressive skill adaptation predicts robot base placements that raise simulated kitchen mobile-manipulation success over prior FloAff methods.
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N2M: Bridging Navigation and Manipulation by Learning Pose Preference from Rollout
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.
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MoTo: A Zero-shot Plug-in Interaction-aware Navigation for General Mobile Manipulation
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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