DeepAI AI Chat
Log In Sign Up

Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning

by   Satoshi Kataoka, et al.

Most successes in robotic manipulation have been restricted to single-arm robots, which limits the range of solvable tasks to pick-and-place, insertion, and objects rearrangement. In contrast, dual and multi arm robot platforms unlock a rich diversity of problems that can be tackled, such as laundry folding and executing cooking skills. However, developing controllers for multi-arm robots is complexified by a number of unique challenges, such as the need for coordinated bimanual behaviors, and collision avoidance amongst robots. Given these challenges, in this work we study how to solve bi-manual tasks using reinforcement learning (RL) trained in simulation, such that the resulting policies can be executed on real robotic platforms. Our RL approach results in significant simplifications due to using real-time (4Hz) joint-space control and directly passing unfiltered observations to neural networks policies. We also extensively discuss modifications to our simulated environment which lead to effective training of RL policies. In addition to designing control algorithms, a key challenge is how to design fair evaluation tasks for bi-manual robots that stress bimanual coordination, while removing orthogonal complicating factors such as high-level perception. In this work, we design a Connect Task, where the aim is for two robot arms to pick up and attach two blocks with magnetic connection points. We validate our approach with two xArm6 robots and 3D printed blocks with magnetic attachments, and find that our system has 100 rate at the Connect Task.


page 1

page 2

page 3

page 7


Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning

Most successes in robotic manipulation have been restricted to single-ar...

Learning Contact-Rich Manipulation Tasks with Rigid Position-Controlled Robots

To fully realize industrial automation, it is indispensable to give the ...

Lifelong Robotic Reinforcement Learning by Retaining Experiences

Multi-task learning ideally allows robots to acquire a diverse repertoir...

Learning to Play Soccer by Reinforcement and Applying Sim-to-Real to Compete in the Real World

This work presents an application of Reinforcement Learning (RL) for the...

Setting up a Reinforcement Learning Task with a Real-World Robot

Reinforcement learning is a promising approach to developing hard-to-eng...

Robot Learning of Mobile Manipulation with Reachability Behavior Priors

Mobile Manipulation (MM) systems are ideal candidates for taking up the ...

Dual-Arm Adversarial Robot Learning

Robot learning is a very promising topic for the future of automation an...