TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction
Learning in simulation and transferring the learned policy to the real world
has the potential to enable generalist robots. The key challenge of this
approach is to address simulation-to-reality (sim-to-real) gaps. Previous
methods often require domain-specific knowledge a priori. We argue that a
straightforward way to obtain such knowledge is by asking humans to observe and
assist robot policy execution in the real world. The robots can then learn from
humans to close various sim-to-real gaps. We propose TRANSIC, a data-driven
approach to enable successful sim-to-real transfer based on a human-in-the-loop
framework. TRANSIC allows humans to augment simulation policies to overcome
various unmodeled sim-to-real gaps holistically through intervention and online
correction. Residual policies can be learned from human corrections and
integrated with simulation policies for autonomous execution. We show that our
approach can achieve successful sim-to-real transfer in complex and
contact-rich manipulation tasks such as furniture assembly. Through synergistic
integration of policies learned in simulation and from humans, TRANSIC is
effective as a holistic approach to addressing various, often coexisting
sim-to-real gaps. It displays attractive properties such as scaling with human
effort. Videos and code are available at https://transic-robot.github.io/
6646b778f7261acc2c391342
Yunfan Jiang
admin_assigned
2024-05-17T11:18:15.839Z
61e9f5398c237a147a3f4ab5
/avatars/afd4ec17cb132b5ab56e50a678c4786d.svg
Yunfan Jiang
user
yunfanj
6646b778f7261acc2c391343
Chen Wang
admin_assigned
2024-05-17T11:19:12.382Z
661d5bacce4e5d0ca21b1b8b
/avatars/dcb214fb0e571cbde69038d83b737ae3.svg
Chen Wang
user
chenwangj
6646b778f7261acc2c391344
Ruohan Zhang
6646b778f7261acc2c391345
Jiajun Wu
admin_assigned
2024-05-17T11:19:40.647Z
647b7b8345616c3de3627863
/avatars/8196364bffed71086ba9a0a4a62027d3.svg
Jiajun Wu
user
jiajunwu
6646b778f7261acc2c391346
Li Fei-Fei
TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction
Learning in simulation and transferring the learned policy to the real world
has the potential to enable generalist robots. The key challenge of this
approach is to address simulation-to-reality (sim-to-real) gaps. Previous
methods often require domain-specific knowledge a priori. We argue that a
straightforward way to obtain such knowledge is by asking humans to observe and
assist robot policy execution in the real world. The robots can then learn from
humans to close various sim-to-real gaps. We propose TRANSIC, a data-driven
approach to enable successful sim-to-real transfer based on a human-in-the-loop
framework. TRANSIC allows humans to augment simulation policies to overcome
various unmodeled sim-to-real gaps holistically through intervention and online
correction. Residual policies can be learned from human corrections and
integrated with simulation policies for autonomous execution. We show that our
approach can achieve successful sim-to-real transfer in complex and
contact-rich manipulation tasks such as furniture assembly. Through synergistic
integration of policies learned in simulation and from humans, TRANSIC is
effective as a holistic approach to addressing various, often coexisting
sim-to-real gaps. It displays attractive properties such as scaling with human
effort. Videos and code are available at https://transic-robot.github.io/
TRANSIC integrates simulation policies with human corrections to achieve successful sim-to-real transfer in complex robotic tasks.
simulation-to-reality sim-to-real human-in-the-loop residual policies furniture assembly