LEARN 2 ASSEMBLE

Learn2Assemble with Structured Representations and Search for Robotic Architectural Construction

Niklas Funk, Georgia Chalvatzaki, Boris Belousov, Jan Peters

Conference on Robot Learning (CoRL) 2021

Abstract: Autonomous robotic assembly requires a well-orchestrated sequence of high-level actions and smooth manipulation executions. The problem of learning to assemble complex 3D structures remains challenging, as it requires drawing connections between target shapes and available building blocks, as well as creating valid assembly sequences with respect to stability and kinematic feasibility in the robot's workspace. We design a hierarchical control framework that learns to sequence the building blocks to construct arbitrary 3D designs and ensures that they are feasible, as we plan the geometric execution with the robot-in-the-loop. Our approach draws its generalization properties from combining graph-based representations with reinforcement learning (RL) and ultimately adding tree-search. Combining structured representations with model-free RL and Monte-Carlo planning allows agents to operate with various target shapes and building block types. We demonstrate the flexibility of the proposed structured representation and our algorithmic solution in a series of simulated 3D assembly tasks with robotic evaluation, which showcases our method's ability to learn to construct stable structures with a large number of building blocks.

Paper

Code

In the following, we provide more videos supplementing this work's experiments.

Evaluating Graph Architecture

Building Structures using the Multi Head Attention (MHA) and Structure2Vec (S2V) Graph Architectures

FINAL_MHA1.mp4

mha

Using MHA yields more efficient policies.

FINAL_s2v1.mp4

s2v

Using S2V results in policies placing blocks at unnecessary locations and failing to fill the entire structure more often.

SHA_final.mp4

SHA

While SHA outperforms S2V, using multiple attention heads (MHA) is still superior.

Evaluating Graph Connectivity

Building Structures using the partially connected (pc) and fully-connected (fc) setups

FINAL_pc.mp4

PC (partial Connectivity)

Results in more efficient building, i.e. using less blocks for filling the structures.

FINAL_fc.mp4

FC (full connectivity)

Uses more blocks compared to the pc setup.

Evaluating Learning Algorithms

Building Structures using the different learning algorithms

output_DQN.mp4

DQN

Using DQN to build the structure results in terminating upon an invalid action.

output_dqn_mcts.mp4

DQN + MCTS (budget 10)

Adding MCTS to the DQN agent results in succesfully solving the task.

output_epsilon.mp4

Epsilon-MCTS (budget 10)

The epsilon-mcts agent also succesfully solves this task.

output_q.mp4

Q-MCTS (budget 10)

The Q-MCTS agent, like the DQN agent fails to complete the task and finishes with an invalid action.

Evaluating Training with (w) and without (wo) the Robot-in-the-loop

Building Structures using the robot-in-the-loop and investigating whether training with or without the robot matters

output_w_robot.mp4

Trained w robot

The agent trained with the robot-in-the-loop is capable of building complex shapes without the robot destroying the structure.

output_wo.mp4

trained wo Robot

Training without the robot-in-the-loop is not sufficient. This results in policies that fail to build stable structures due to the robotic arm colliding with the actual structure.

Evaluating building complex Shapes with the Robot-in-the-loop

Building complex two- and four-sided structures with the robot, using DQN+MCTS (search budget 10).

output_2_sided.mp4

two-sided

output_FINAL1_4_sided.mp4

four-sided

Evaluating Generalization w.r.t randomized Scenes

Building complex two-sided structures with the robot, using a policy trained in the above setting (DQN+MCTS search budget 10) and thus evaluating its capability to generalize to novel (unseen) scenarios.

FINAL_vid_other_side.mp4

TWO-SIDED IN UNSEEN ENVIRONMENT

Evaluating Generalization w.r.t building Blocks

Building complex two-sided structures with and without the robot, using multiple different building blocks (DQN+MCTS search budget 10).

FINAL_w_robot.mp4

WITH THE ROBOT

FINAL_wo_robot.mp4

WITHOUT THE ROBOT

Evaluating Transfer to a real Robot

Building a single sided structure on real hardware using a different manipulator. This is achieved by initializing a simulation scene that mirrors the reality (see top left hand corner) and executing the desired actions on the real system.

Neues Video Final.mp4

transfer to real robot

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