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Dynamics-Guided Diffusion Model for Sensor-less Robot Manipulator Design

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arxiv 2402.15038 v2 pith:2MSRWC4K submitted 2024-02-23 cs.RO cs.AIcs.LG

Xiaomeng Xu , Huy Ha , Shuran Song This is my paper
classification cs.ROcs.AIcs.LG
keywords designdgdmdiffusiondesignsmanipulatormodelobjectsdata-driven
verification ladder T0 review T1 audit T2 compute T3 formal
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We present Dynamics-Guided Diffusion Model (DGDM), a data-driven framework for generating task-specific manipulator designs without task-specific training. Given object shapes and task specifications, DGDM generates sensor-less manipulator designs that can blindly manipulate objects towards desired motions and poses using an open-loop parallel motion. This framework 1) flexibly represents manipulation tasks as interaction profiles, 2) represents the design space using a geometric diffusion model, and 3) efficiently searches this design space using the gradients provided by a dynamics network trained without any task information. We evaluate DGDM on various manipulation tasks ranging from shifting/rotating objects to converging objects to a specific pose. Our generated designs outperform optimization-based and unguided diffusion baselines relatively by 31.5% and 45.3% on average success rate. With the ability to generate a new design within 0.8s, DGDM facilitates rapid design iteration and enhances the adoption of data-driven approaches for robot mechanism design. Qualitative results are best viewed on our project website https://dgdm-robot.github.io/.

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Cited by 5 Pith papers

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    Latent Policy Barrier improves behavior-cloned visuomotor policies by using a latent dynamics model trained on expert and rollout data to guide actions back toward in-distribution expert states.

  3. VLMgineer: Vision Language Models as Robotic Toolsmiths

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    VLMgineer combines VLM-generated URDF tool designs with evolutionary search to co-design tools and action plans, outperforming human-specified and existing tools on a new simulated manipulation benchmark.

  4. Vision in Action: Learning Active Perception from Human Demonstrations

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    ViA trains bimanual manipulation policies from human demonstrations that include active head-camera movement, using a 6-DoF robot neck and a VR interface with point-cloud rendering, reporting large gains on three occl...

  5. Co-Design of Soft Gripper with Neural Physics

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