Authors: Haozhi Qi, Brent Yi, Mike Lambeta, Yi Ma, Roberto Calandra, Jitendra Malik
Abstract: Learning policies in simulation and transferring them to the real world has
become a promising approach in dexterous manipulation. However, bridging the
sim-to-real gap for each new task requires substantial human effort, such as
careful reward engineering, hyperparameter tuning, and system identification.
In this work, we present a system that leverages low-level skills to address
these challenges for more complex tasks. Specifically, we introduce a
hierarchical policy for in-hand object reorientation based on previously
acquired rotation skills. This hierarchical policy learns to select which
low-level skill to execute based on feedback from both the environment and the
low-level skill policies themselves. Compared to learning from scratch, the
hierarchical policy is more robust to out-of-distribution changes and transfers
easily from simulation to real-world environments. Additionally, we propose a
generalizable object pose estimator that uses proprioceptive information,
low-level skill predictions, and control errors as inputs to estimate the
object pose over time. We demonstrate that our system can reorient objects,
including symmetrical and textureless ones, to a desired pose.
Source: http://arxiv.org/abs/2501.05439v1
