Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual
  and Action Representations
Autonomous robotic systems capable of learning novel manipulation tasks are
poised to transform industries from manufacturing to service automation.
However, modern methods (e.g., VIP and R3M) still face significant hurdles,
notably the domain gap among robotic embodiments and the sparsity of successful
task executions within specific action spaces, resulting in misaligned and
ambiguous task representations. We introduce Ag2Manip (Agent-Agnostic
representations for Manipulation), a framework aimed at surmounting these
challenges through two key innovations: a novel agent-agnostic visual
representation derived from human manipulation videos, with the specifics of
embodiments obscured to enhance generalizability; and an agent-agnostic action
representation abstracting a robot's kinematics to a universal agent proxy,
emphasizing crucial interactions between end-effector and object. Ag2Manip's
empirical validation across simulated benchmarks like FrankaKitchen, ManiSkill,
and PartManip shows a 325% increase in performance, achieved without
domain-specific demonstrations. Ablation studies underline the essential
contributions of the visual and action representations to this success.
Extending our evaluations to the real world, Ag2Manip significantly improves
imitation learning success rates from 50% to 77.5%, demonstrating its
effectiveness and generalizability across both simulated and physical
environments.
66305ba8090586ab8c761440
Puhao Li
admin_assigned
2024-04-30T07:55:03.163Z
63d4e83913ae45b7807acbb1
/avatars/4b8778ed239b64de5f8781f0ba57168b.svg
Puhao Li
user
puhao
66305ba8090586ab8c761441
Tengyu Liu
admin_assigned
2024-04-30T07:55:23.988Z
634379a81bdd3dfa55dcbe82
https://cdn-avatars.huggingface.co/v1/production/uploads/1665366425318-noauth.png
Tengyu Liu
user
oxrider
66305ba8090586ab8c761442
Yuyang Li
admin_assigned
2024-04-30T07:55:48.311Z
637cef70b8e573d75beb5748
https://cdn-avatars.huggingface.co/v1/production/uploads/637cef70b8e573d75beb5748/hMl_pKUO3BanR47RQqN6o.png
Yuyang Li
user
aidenli
66305ba8090586ab8c761443
Muzhi Han
admin_assigned
2024-04-30T07:55:55.002Z
64ba3db4b4f1dd6959da9367
/avatars/41d13aef18ef79abab0c6a8e6037b458.svg
Muzhi Han
user
hamster6
66305ba8090586ab8c761444
Haoran Geng
admin_assigned
2024-04-30T07:56:00.738Z
651a752df7724d9aee9fb970
/avatars/956dc2a6fabf10bea15994152c04badc.svg
Haoran Geng
user
HaoranGeng
66305ba8090586ab8c761445
Shu Wang
66305ba8090586ab8c761446
Yixin Zhu
66305ba8090586ab8c761447
Song-Chun Zhu
66305ba8090586ab8c761448
Siyuan Huang
admin_assigned
2024-04-30T08:05:42.906Z
63c7a33121bd95f80ed74652
/avatars/7dd59afea785a2bff0ec2b757abd474e.svg
Siyuan Huang
user
thuhsy
Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual
  and Action Representations
Autonomous robotic systems capable of learning novel manipulation tasks are
poised to transform industries from manufacturing to service automation.
However, modern methods (e.g., VIP and R3M) still face significant hurdles,
notably the domain gap among robotic embodiments and the sparsity of successful
task executions within specific action spaces, resulting in misaligned and
ambiguous task representations. We introduce Ag2Manip (Agent-Agnostic
representations for Manipulation), a framework aimed at surmounting these
challenges through two key innovations: a novel agent-agnostic visual
representation derived from human manipulation videos, with the specifics of
embodiments obscured to enhance generalizability; and an agent-agnostic action
representation abstracting a robot's kinematics to a universal agent proxy,
emphasizing crucial interactions between end-effector and object. Ag2Manip's
empirical validation across simulated benchmarks like FrankaKitchen, ManiSkill,
and PartManip shows a 325% increase in performance, achieved without
domain-specific demonstrations. Ablation studies underline the essential
contributions of the visual and action representations to this success.
Extending our evaluations to the real world, Ag2Manip significantly improves
imitation learning success rates from 50% to 77.5%, demonstrating its
effectiveness and generalizability across both simulated and physical
environments.
Ag2Manip addresses domain gaps and sparsity in robotic manipulation tasks by introducing agent-agnostic visual and action representations, achieving significant performance improvements in both simulation and real-world settings.
agent-agnostic visual representation agent-agnostic action representation FrankaKitchen ManiSkill PartManip imitation learning generalizability