3D Diffusion Policy
Imitation learning provides an efficient way to teach robots dexterous
skills; however, learning complex skills robustly and generalizablely usually
consumes large amounts of human demonstrations. To tackle this challenging
problem, we present 3D Diffusion Policy (DP3), a novel visual imitation
learning approach that incorporates the power of 3D visual representations into
diffusion policies, a class of conditional action generative models. The core
design of DP3 is the utilization of a compact 3D visual representation,
extracted from sparse point clouds with an efficient point encoder. In our
experiments involving 72 simulation tasks, DP3 successfully handles most tasks
with just 10 demonstrations and surpasses baselines with a 55.3% relative
improvement. In 4 real robot tasks, DP3 demonstrates precise control with a
high success rate of 85%, given only 40 demonstrations of each task, and shows
excellent generalization abilities in diverse aspects, including space,
viewpoint, appearance, and instance. Interestingly, in real robot experiments,
DP3 rarely violates safety requirements, in contrast to baseline methods which
frequently do, necessitating human intervention. Our extensive evaluation
highlights the critical importance of 3D representations in real-world robot
learning. Videos, code, and data are available on
https://3d-diffusion-policy.github.io .
65e9558e6434c9e6f4a30604
Yanjie Ze
claimed_verified
2024-03-07T09:13:40.115Z
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Yanjie Ze
user
yjze
65e9558e6434c9e6f4a30605
Gu Zhang
claimed_verified
2024-03-07T09:13:37.901Z
65e95effe19214e9d13d7a2a
/avatars/35ae40579ac53d7b598957be9c8b4710.svg
Gu Zhang
user
GuZhang
65e9558e6434c9e6f4a30606
Kangning Zhang
claimed_verified
2024-03-07T16:02:46.137Z
63b6def76fca9d2a1902fa14
/avatars/c7f2487450ea954e2bca4fc5a6db8eb3.svg
张康宁
user
zhuiguang-ning
65e9558e6434c9e6f4a30607
Chenyuan Hu
admin_assigned
2024-03-07T09:49:28.551Z
63ec3830ca08a72ba9d04b81
/avatars/12245876937e12651dc794ca99f9109d.svg
chenyuan hu
user
hcy1223
65e9558e6434c9e6f4a30608
Muhan Wang
admin_assigned
2024-03-07T09:49:34.997Z
656492f7055bb4df5e8a3a49
/avatars/b3b7e8e10853541a9bfa1ce7ce2217fd.svg
Muhan Wang
user
Sparklist
65e9558e6434c9e6f4a30609
Huazhe Xu
admin_assigned
2024-03-07T09:49:41.282Z
649c130f92525b3ac9fbcffe
/avatars/b60e4d6803f7e4ef42200ddcb9e5bac1.svg
Huazhe Xu
user
Huazhe-Xu
3D Diffusion Policy
Imitation learning provides an efficient way to teach robots dexterous
skills; however, learning complex skills robustly and generalizablely usually
consumes large amounts of human demonstrations. To tackle this challenging
problem, we present 3D Diffusion Policy (DP3), a novel visual imitation
learning approach that incorporates the power of 3D visual representations into
diffusion policies, a class of conditional action generative models. The core
design of DP3 is the utilization of a compact 3D visual representation,
extracted from sparse point clouds with an efficient point encoder. In our
experiments involving 72 simulation tasks, DP3 successfully handles most tasks
with just 10 demonstrations and surpasses baselines with a 55.3% relative
improvement. In 4 real robot tasks, DP3 demonstrates precise control with a
high success rate of 85%, given only 40 demonstrations of each task, and shows
excellent generalization abilities in diverse aspects, including space,
viewpoint, appearance, and instance. Interestingly, in real robot experiments,
DP3 rarely violates safety requirements, in contrast to baseline methods which
frequently do, necessitating human intervention. Our extensive evaluation
highlights the critical importance of 3D representations in real-world robot
learning. Videos, code, and data are available on
https://3d-diffusion-policy.github.io .
3D Diffusion Policy (DP3) utilizes 3D visual representations for efficient imitation learning, achieving robust and generalized skills with fewer demonstrations compared to existing methods.
3D Diffusion Policy DP3 3D visual representations sparse point clouds point encoder diffusion policies conditional action generative models