Robohub.org
 

New dual-arm robot achieves bimanual tasks by learning from simulation


by
29 August 2023



share this:

Dual arm robot holding crisp. Image: Yijiong Lin

The new Bi-Touch system, designed by scientists at the University of Bristol and based at the Bristol Robotics Laboratory, allows robots to carry out manual tasks by sensing what to do from a digital helper.

The findings, published in IEEE Robotics and Automation Letters, show how an AI agent interprets its environment through tactile and proprioceptive feedback, and then control the robots’ behaviours, enabling precise sensing, gentle interaction, and effective object manipulation to accomplish robotic tasks.

This development could revolutionise industries such as fruit picking, domestic service, and eventually recreate touch in artificial limbs.

Lead author Yijiong Lin from the Faculty of Engineering, explained: “With our Bi-Touch system, we can easily train AI agents in a virtual world within a couple of hours to achieve bimanual tasks that are tailored towards the touch. And more importantly, we can directly apply these agents from the virtual world to the real world without further training.

“The tactile bimanual agent can solve tasks even under unexpected perturbations and manipulate delicate objects in a gentle way.”

Bimanual manipulation with tactile feedback will be key to human-level robot dexterity. However, this topic is less explored than single-arm settings, partly due to the availability of suitable hardware along with the complexity of designing effective controllers for tasks with relatively large state-action spaces. The team were able to develop a tactile dual-arm robotic system using recent advances in AI and robotic tactile sensing.

The researchers built up a virtual world (simulation) that contained two robot arms equipped with tactile sensors. They then design reward functions and a goal-update mechanism that could encourage the robot agents to learn to achieve the bimanual tasks and developed a real-world tactile dual-arm robot system to which they could directly apply the agent.

The robot learns bimanual skills through Deep Reinforcement Learning (Deep-RL), one of the most advanced techniques in the field of robot learning. It is designed to teach robots to do things by letting them learn from trial and error akin to training a dog with rewards and punishments.

For robotic manipulation, the robot learns to make decisions by attempting various behaviours to achieve designated tasks, for example, lifting up objects without dropping or breaking them. When it succeeds, it gets a reward, and when it fails, it learns what not to do. With time, it figures out the best ways to grab things using these rewards and punishments. The AI agent is visually blind relying only on proprioceptive feedback – a body’s ability to sense movement, action and location and tactile feedback.

They were able to successfully enable to the dual arm robot to successfully safely lift items as fragile as a single Pringle crisp.

Co-author Professor Nathan Lepora added: “Our Bi-Touch system showcases a promising approach with affordable software and hardware for learning bimanual behaviours with touch in simulation, which can be directly applied to the real world. Our developed tactile dual-arm robot simulation allows further research on more different tasks as the code will be open-source, which is ideal for developing other downstream tasks.”

Yijiong concluded: “Our Bi-Touch system allows a tactile dual-arm robot to learn sorely from simulation, and to achieve various manipulation tasks in a gentle way in the real world.

“And now we can easily train AI agents in a virtual world within a couple of hours to achieve bimanual tasks that are tailored towards the touch.”




University of Bristol is one of the most popular and successful universities in the UK.
University of Bristol is one of the most popular and successful universities in the UK.





Related posts :



Robot Talk Episode 126 – Why are we building humanoid robots?

  20 Jun 2025
In this special live recording at Imperial College London, Claire chatted to Ben Russell, Maryam Banitalebi Dehkordi, and Petar Kormushev about humanoid robotics.

Gearing up for RoboCupJunior: Interview with Ana Patrícia Magalhães

and   18 Jun 2025
We hear from the organiser of RoboCupJunior 2025 and find out how the preparations are going for the event.

Robot Talk Episode 125 – Chatting with robots, with Gabriel Skantze

  13 Jun 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Gabriel Skantze from KTH Royal Institute of Technology about having natural face-to-face conversations with robots.

Preparing for kick-off at RoboCup2025: an interview with General Chair Marco Simões

and   12 Jun 2025
We caught up with Marco to find out what exciting events are in store at this year's RoboCup.

Interview with Amar Halilovic: Explainable AI for robotics

  10 Jun 2025
Find out about Amar's research investigating the generation of explanations for robot actions.

Robot Talk Episode 124 – Robots in the performing arts, with Amy LaViers

  06 Jun 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Amy LaViers from the Robotics, Automation, and Dance Lab about the creative relationship between humans and machines.

Robot Talk Episode 123 – Standardising robot programming, with Nick Thompson

  30 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Nick Thompson from BOW about software that makes robots easier to program.

Congratulations to the #AAMAS2025 best paper, best demo, and distinguished dissertation award winners

  29 May 2025
Find out who won the awards presented at the International Conference on Autonomous Agents and Multiagent Systems last week.



 

Robohub is supported by:




Would you like to learn how to tell impactful stories about your robot or AI system?


scicomm
training the next generation of science communicators in robotics & AI


©2025.05 - Association for the Understanding of Artificial Intelligence


 












©2025.05 - Association for the Understanding of Artificial Intelligence