Learning and Transferring Closed-Loop Robot Software
Overview
This research investigates whether improvements in closed-loop robot policy implementations for source tasks can be transferred to new tasks. By treating complete closed-loop implementations as reusable execution experience, the authors use a coding agent to generate and iteratively improve policy code from demonstrations and simulation feedback. Validated implementations are stored in a software archive. The study evaluates policy learning across four RoboCasa source tasks and cross-task transfer on nine additional target tasks. Results show that iterative optimization increases success rates from 28.3% to 64.2% on source tasks, and using archived code improves performance on new tasks from 41.5% (initially generated) to 57.0%. The approach is further validated on a physical Unitree G1 robot.
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Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · Reported success rate: 28.3 percent · Basis not reported · Differing source assertions
“es using archived implementations, target demonstrations, and execution feedback. We evaluate policy learning and its relationship to cross-task transfer. For learning, across four source tasks in RoboCasa, iterative optimization increases the success rate from 28.3% with initially generated policy code to 64.2% with validation-selected policy code. We further validate this learning approach on a physical Unitree G1.”
Exact source · revision 2Source owner not reported
Reported quantity · Reported success rate: 64.2 percent · Basis not reported · Differing source assertions
“es using archived implementations, target demonstrations, and execution feedback. We evaluate policy learning and its relationship to cross-task transfer. For learning, across four source tasks in RoboCasa, iterative optimization increases the success rate from 28.3% with initially generated policy code to 64.2% with validation-selected policy code. We further validate this learning approach on a physical Unitree G1.”
Exact source · revision 2Source owner not reported
Reported quantity · Reported success rate: 45.2 percent · Basis not reported · Differing source assertions
“For transfer, across nine additional target tasks in RoboCasa, success rates are 45.2% without references, 41.5% using initially generated policy code from source tasks, and 57.0% using their validation-selected policy code. These results show that improving source-task code also improves learning on new tasks.”
Exact source · revision 2Source owner not reported
Evidence dependency changes (1)
- 2026-11-02T17:50:25.991Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalLearning and Transferring Closed-Loop Robot Software
This research investigates whether improvements in closed-loop robot policy implementations for source tasks can be transferred to new tasks. By treating complete closed-loop implementations as reusable execution experience, the authors use a coding agent to generate and iteratively improve policy code from demonstrations and simulation feedback. Validated implementations are stored in a software archive. The study evaluates policy learning across four RoboCasa source tasks and cross-task transfer on nine additional target tasks. Results show that iterative optimization increases success rates from 28.3% to 64.2% on source tasks, and using archived code improves performance on new tasks from 41.5% (initially generated) to 57.0%. The approach is further validated on a physical Unitree G1 robot.
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