DeepAI AI Chat
Log In Sign Up

Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation

by   I-Chun Arthur Liu, et al.

Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual observations. Prior work tackles the exploration problem by integrating motion planning and reinforcement learning. However, the motion planner augmented policy requires access to state information, which is often not available in the real-world settings. To this end, we propose to distill a state-based motion planner augmented policy to a visual control policy via (1) visual behavioral cloning to remove the motion planner dependency along with its jittery motion, and (2) vision-based reinforcement learning with the guidance of the smoothed trajectories from the behavioral cloning agent. We evaluate our method on three manipulation tasks in obstructed environments and compare it against various reinforcement learning and imitation learning baselines. The results demonstrate that our framework is highly sample-efficient and outperforms the state-of-the-art algorithms. Moreover, coupled with domain randomization, our policy is capable of zero-shot transfer to unseen environment settings with distractors. Code and videos are available at


page 5

page 8

page 15


Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments

Deep reinforcement learning (RL) agents are able to learn contact-rich m...

Learning visual servo policies via planner cloning

Learning control policies for visual servoing in novel environments is a...

Learning Deep Policies for Physics-Based Manipulation in Clutter

Uncertainty in modeling real world physics makes transferring traditiona...

Coarse-to-Fine for Sim-to-Real: Sub-Millimetre Precision Across the Workspace

When training control policies for robot manipulation via deep learning,...

Solving Rubik's Cube with a Robot Hand

We demonstrate that models trained only in simulation can be used to sol...

Data-driven Policy Transfer with Imprecise Perception Simulation

The paper presents a complete pipeline for learning continuous motion co...

Metric-Free Exploration for Topological Mapping by Task and Motion Imitation in Feature Space

We propose DeepExplorer, a simple and lightweight metric-free exploratio...