Robot See Robot Do: Imitating Articulated Object Manipulation with
  Monocular 4D Reconstruction
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
66f6277ce5d2d88a93bc9331
Justin Kerr
claimed_verified
2024-10-02T07:41:32.308Z
63ec69436ef3ce22b88b636d
/avatars/83436c44a4650c8c81da769e024409a7.svg
Justin Kerr
user
wisepigeon
66f6277ce5d2d88a93bc9332
Chung Min Kim
admin_assigned
2024-10-01T13:11:50.069Z
6348a53762c668c7b48bd796
/avatars/a60214a0150b9c7047c6eaf482bc195b.svg
Chung Min Kim
user
chungmin99
66f6277ce5d2d88a93bc9333
Mingxuan Wu
66f6277ce5d2d88a93bc9334
Brent Yi
admin_assigned
2024-10-01T13:11:35.799Z
63e4519e54f51ea342d231f1
/avatars/d1c63cb6a28cb8af753daa6e6bb771cd.svg
Brent Yi
user
brentyi
66f6277ce5d2d88a93bc9335
Qianqian Wang
admin_assigned
2024-10-01T13:11:30.491Z
6616f0c4c2e30710e607c2bf
/avatars/a5941e0ed940439f4c7c67747318cbfc.svg
Qianqian Wang
user
qianqian68
66f6277ce5d2d88a93bc9336
Ken Goldberg
66f6277ce5d2d88a93bc9337
Angjoo Kanazawa
admin_assigned
2024-10-01T13:11:16.275Z
6478cf7150ff7001631679c3
/avatars/65ec385a9cc44c972e6caf952e759ff1.svg
Angjoo Kanazawa
user
akanazawa
Robot See Robot Do: Imitating Articulated Object Manipulation with
  Monocular 4D Reconstruction
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
Robot See Robot Do (RSRD) enables robotic imitation of articulated object manipulation from human demonstrations using 4D Differentiable Part Models (4D-DPM) for 3D motion recovery and part-centric trajecotry planning.
4D Differentiable Part Models 4D-DPM differentiable rendering part-centric feature fields 3D motion recovery bimanual arm motions part-centric trajectories geometric regularizers