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Adaptive Mobile Manipulation for Articulated Objects In the Open World
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Deploying robots in open-ended unstructured environments such as homes has been a long-standing research problem. However, robots are often studied only in closed-off lab settings, and prior mobile manipulation work is restricted to pick-move-place, which is arguably just the tip of the iceberg in this area. In this paper, we introduce Open-World Mobile Manipulation System, a full-stack approach to tackle realistic articulated object operation, e.g. real-world doors, cabinets, drawers, and refrigerators in open-ended unstructured environments. The robot utilizes an adaptive learning framework to initially learns from a small set of data through behavior cloning, followed by learning from online practice on novel objects that fall outside the training distribution. We also develop a low-cost mobile manipulation hardware platform capable of safe and autonomous online adaptation in unstructured environments with a cost of around 20,000 USD. In our experiments we utilize 20 articulate objects across 4 buildings in the CMU campus. With less than an hour of online learning for each object, the system is able to increase success rate from 50% of BC pre-training to 95% using online adaptation. Video results at https://open-world-mobilemanip.github.io/
Forward citations
Cited by 7 Pith papers
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Motion Planning for Mobile Manipulators Navigating Doorways via Model Predictive Control
A nonlinear MPC with a soft reachability penalty generates push/pull door-opening and traversal trajectories for a mobile manipulator, demonstrated in Isaac Sim and on hardware.
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HERMES: Human-to-Robot Embodied Learning from Multi-Source Motion Data for Mobile Dexterous Manipulation
HERMES converts a single human motion demonstration into a deployable mobile bimanual dexterous manipulation policy, using RL, depth-image distillation, and closed-loop PnP pose refinement.
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AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation
AC-DiT adds mobility-to-body conditioning and perception-aware 2D/3D weighting to a diffusion transformer, improving success rates on simulated and real-world mobile manipulation tasks.
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OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
A vision-language model fine-tuned on 572K synthetic simulation examples improves open-world mobile manipulation action decisions and object grounding over GPT-4o, with 21.9% full-task success in simulation and 90% ac...
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RobotMover: Learning to Move Large Objects From Human Demonstrations
RobotMover trains a Spot robot to move large objects in the real world by imitating human-object interaction demonstrations through a compact Interaction Chain reward, without fine-tuning on hardware.
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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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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models
Embodied intelligence learning is reviewed through the complementary lenses of physical simulators and world models, with a proposed IR-L0 to IR-L4 robot capability taxonomy.
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