TRANSIC introduces a novel approach for policy transfer from simulation to real-world robotic tasks by utilizing human operator feedback for online correction. Initially, a base policy is developed in a simulated environment, and once deployed in the real world, human operators intervene when necessary, providing corrections. These corrections are then used to train a residual policy that enhances overall performance in contact-rich manipulation tasks. The framework integrates both the base and residual policies to work seamlessly in real-time, significantly addressing gaps between simulation and reality and ensuring more reliable robotic operations.
TRANSIC effectively bridges the gap between simulation and real-world application by leveraging online corrections from human operators to refine robotic policies.
The strategy involves training a base policy in simulation and using human interventions during real-world deployment to enhance policy performance through learned residuals.
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