title: TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction
summary: 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/
paper_authors._id: 6646b778f7261acc2c391342
paper_authors.name: Yunfan Jiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-17T11:18:15.839Z
paper_authors._id: 61e9f5398c237a147a3f4ab5
paper_authors.avatarUrl: /avatars/afd4ec17cb132b5ab56e50a678c4786d.svg
paper_authors.fullname: Yunfan Jiang
paper_authors.type: user
paper_authors.user: yunfanj
paper_authors._id: 6646b778f7261acc2c391343
paper_authors.name: Chen Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-17T11:19:12.382Z
paper_authors._id: 661d5bacce4e5d0ca21b1b8b
paper_authors.avatarUrl: /avatars/dcb214fb0e571cbde69038d83b737ae3.svg
paper_authors.fullname: Chen Wang
paper_authors.type: user
paper_authors.user: chenwangj
paper_authors._id: 6646b778f7261acc2c391344
paper_authors.name: Ruohan Zhang
paper_authors._id: 6646b778f7261acc2c391345
paper_authors.name: Jiajun Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-17T11:19:40.647Z
paper_authors._id: 647b7b8345616c3de3627863
paper_authors.avatarUrl: /avatars/8196364bffed71086ba9a0a4a62027d3.svg
paper_authors.fullname: Jiajun Wu
paper_authors.type: user
paper_authors.user: jiajunwu
paper_authors._id: 6646b778f7261acc2c391346
paper_authors.name: Li Fei-Fei
paper_title: TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction
paper_summary: 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/
paper_ai_summary: TRANSIC integrates simulation policies with human corrections to achieve successful sim-to-real transfer in complex robotic tasks.
paper_ai_keywords: simulation-to-reality sim-to-real human-in-the-loop residual policies furniture assembly