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Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction
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Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction
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Humans can learn to manipulate new objects by simply watching others; providing robots with the ability to learn from such demonstrations would enable a natural interface specifying new behaviors. This work develops Robot See Robot Do (RSRD), a method for imitating articulated object manipulation from a single monocular RGB human demonstration given a single static multi-view object scan. We first propose 4D Differentiable Part Models (4D-DPM), a method for recovering 3D part motion from a monocular video with differentiable rendering. This analysis-by-synthesis approach uses part-centric feature fields in an iterative optimization which enables the use of geometric regularizers to recover 3D motions from only a single video. Given this 4D reconstruction, the robot replicates object trajectories by planning bimanual arm motions that induce the demonstrated object part motion. By representing demonstrations as part-centric trajectories, RSRD focuses on replicating the demonstration's intended behavior while considering the robot's own morphological limits, rather than attempting to reproduce the hand's motion. We evaluate 4D-DPM's 3D tracking accuracy on ground truth annotated 3D part trajectories and RSRD's physical execution performance on 9 objects across 10 trials each on a bimanual YuMi robot. Each phase of RSRD achieves an average of 87% success rate, for a total end-to-end success rate of 60% across 90 trials. Notably, this is accomplished using only feature fields distilled from large pretrained vision models -- without any task-specific training, fine-tuning, dataset collection, or annotation. Project page: https://robot-see-robot-do.github.io
Forward citations
Cited by 13 Pith papers
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Video-to-robot control methods cluster into three interface families, and the field’s main bottleneck is grounding video-derived predictions into dependable closed-loop robot behavior.
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PokeNet: Learning Kinematic Models of Articulated Objects from Human Observations
PokeNet estimates joint types, axes, ranges, and operation order of articulated objects directly from a single-view point cloud video of a human demonstration.
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R2RGEN: Real-to-Real 3D Data Generation for Spatially Generalized Manipulation
R2RGen introduces a simulator-free three-stage pipeline that parses, augments, and post-processes real pointcloud observation-action pairs to improve spatial generalization in robotic manipulation policies.
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One View, Many Worlds: Single-Image to 3D Object Meets Generative Domain Randomization for One-Shot 6D Pose Estimation
Given one RGB-D photo of an unseen object, an AI-generated 3D mesh, aligned jointly in metric scale and pose, yields state-of-the-art one-shot 6D pose estimation on YCBInEOAT, TOYL, and LM-O.
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Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)
A robot policy trained on one real demonstration plus AI-generated 3D views succeeds from novel initial poses, including opposite-side starts, across six real manipulation tasks.
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Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations
RIGVid shows that filtered AI-generated videos can serve as effective supervision for complex robotic manipulation tasks without any real demonstrations.
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ODeform: Learning Continuous 4D Motion for Shape Deformation with Neural ODEs
ODeform combines two parallel neural ODEs, one for rigid motion and one for local deformation, to predict arbitrary-time 3D point-cloud deformation from an initial state and physical parameters, outperforming simpler ...
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Artiverse: A Diverse and Physically Grounded Dataset for Articulated Objects
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A survey introduces an interface-centric taxonomy for video-to-control methods in robotic manipulation and identifies the robotics integration layer as the central open challenge.
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