Advancing Robotic Assembly with a Novel Simulation Approach Using NVIDIA Isaac

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NVIDIA robotics and simulation researchers presented Factory: Fast Contact for Robotic Assembly at the 2022 Robotics: Science and Systems (RSS) conference. This work is a novel breakthrough in the simulation and learning of contact-rich interactions, which are ubiquitous in robotics research. Its aim is to greatly accelerate research and development in robotic assembly, as well as serve as a powerful tool for contact-rich simulation of any kind.

Robotic assembly: What, why, and challenges

Assembly is essential across the automotive, aerospace, electronics, and medical industries. Examples include tightening nuts and bolts, soldering, peg insertion, and cable routing.

However, robotic assembly remains one of the oldest and most challenging tasks in robotics. It has been exceptionally difficult to automate because of the physical complexity, high reliability, part variability, and high accuracy requirements.

In industry, robotic assembly methods may achieve high precision, accuracy, and reliability but often require expensive equipment and custom fixtures that can be time-consuming to set up and maintain (preprogrammed trajectories and careful tuning, for example). Tasks that involve robustness to variation (part types, appearance, and locations) and complex manipulation are frequently done using manual labor.

Research methods may achieve lower cost, higher adaptivity, and improved robustness but are often less reliable and slower.

Simulation: A tool for solving the challenges in robotic assembly

Simulation has been used for decades to verify, validate, and optimize robot designs and algorithms in robotics. This includes ensuring the safety of deploying these algorithms. It has also been used to generate large-scale datasets for deep learning, perform system identification, and develop planning and control methods.

In reinforcement learning (RL) research, we have recently seen how simulation results can be transferred to a real system. The importance of accurate physics simulation for robotics development cannot be overemphasized.

GIF of multiple walking robots on the wheel training in Isaac Gym.
Figure 1. ANYmal Demo Training in NVIDIA Isaac Gym, a high-performance GPU-accelerated physics simulator for robot learning

Physics-based simulators like MuJoCo and NVIDIA Isaac Gym have been used to train virtual agents to perform manipulation and locomotion tasks, such as solving a Rubik’s Cube or walking on uneven terrain using ANYmal. The policies have successfully transferred to real-world robots.

However, the power of a fast and accurate simulator has not substantially impacted robotic assembly. Developing such simulators for complex bodies with different variations and motions is a difficult task.

For example, a simple nut-and-bolt assembly requires more than pure helical motion. There are finite clearances between the threads of the nut and bolt, which allow the nut to move with six degrees of freedom. Even humans require some level of carefulness to ensure that the nut has proper initial alignment with the bolt and does not get stuck during tightening. 

However, simulating the task with traditional methods may require meshes with tens of thousands of triangles. Detecting collisions between these meshes, generating contact points and normals, and solving non-penetration constraints are major computational challenges.

Despite the fact that there is an abundance of threaded fasteners in the world, no existing robotics simulator is able to simulate even a single nut-and-bolt assembly in real time at the same rate as the underlying physical dynamics.

In Factory, the researchers developed methods to overcome the challenges in robotic assembly and other contact-rich interactions.

What is Factory?

Factory (Fast Contact for Robotic Assembly) is a set of physics simulation methods and robot learning tools for achieving real-time and faster simulation of a wide range of contact-rich interactions. One of the Factory applications is robotic assembly.

Factory offers the following central contributions:

  • A set of methods for fast, accurate physical simulation of contact-rich interactions through a novel GPU-based synthesis of signed distance function (SDF)-based collisions, contact reduction, and a Gauss-Seidel solver.
  • A robot learning suite consisting of: 
    • 60 high-quality assets, including a Franka robot and all rigid-body assemblies from the NIST Assembly Task Board 1, the established benchmark for robotic assembly
    • Three Isaac Gym-style learning environments for robotic assembly
    • Seven classical robot controllers
  • Proof-of-concept reinforcement learning policies for robots performing contact-rich tasks (a simulated Franka Robot solving the most contact-rich task on the NIST board, nut-and-bolt assembly)

The physics simulation methods in the Factory paper have been integrated into the PhysX physics engine used by Isaac Gym. The asset suite and reinforcement learning policies are available with the latest version of Isaac Gym and the Isaac Gym Environments GitHub repo. The simulation methods are also available in the Omniverse Isaac Sim simulator, with reinforcement learning examples coming later this summer.

Simulation methods and results

Using the fast GPU-based implementations of SDF collisions for objects, contact reduction algorithms for reducing contacts from the SDF collisions, and custom numerical solvers, the researchers were not only able to simulate a single M16 nut and bolt in real time but 1,024 in parallel environments and real time. This is essentially 20,000x faster than the prior state-of-the-art. 

The researchers demonstrated the simulator’s performance in a wide range of challenging scenes, including the following:

  • 512 bowls falling into a pile in the same environment
  • A pile of nuts fed into a feeder mechanism vibrating at 60 Hz
  • A Franka robot executing a hand-scripted trajectory to grasp and tighten a nut onto a bolt, with 128 instances of this environment executing in real time
GIF of 1,024 ZM16 nut-and-bolt parallel assembly.
Figure 2. The M16 nut-and-bolt assemblies scene, consisting of 1,024 parallel nut-and-bolt interactions executing in real time
GIF of robot hands retrieving nuts from a vibratory feeder and tightening them onto a bolt.
Figure 3. The Franka robot plus M16 nut-and-bolt assemblies scene, consisting of 128 parallel Franka robots retrieving nuts from a vibratory feeder mechanism and tightening them onto a bolt
GIF of 1,024 bowls falling into a pile in the same environment.
Figure 4. 1,024 bowls falling into a pile in the same environment, executing in real time

Robot learning tools

The most established benchmark for robotic assembly is the NIST assembly task board, the focus of an annual robotics competition since 2017. The NIST Task Board 1 consists of 38 unique parts. However, the CAD models provided are not ideal for physics simulations due to a lack of real-world clearances, interferences between parts, hand-derived measurements, and so on. Realistic models are hard to find.

Image for assembly components such as nut-and-bolt, USB, and Wi-Fi connector .
Figure 5. The real NIST Task Board 1. Compare to the simulated board in Figure 6.

Factory uses 60 high-quality, simulation-ready part models, each with an Onshape CAD model, one or more OBJ meshes, a URDF description, and estimated material properties that conform to international standards (ISO 724, ISO 965, and ISO 286) or which are based on models sourced from manufacturers. These models include all parts on the NIST assembly Task Board 1 with dimensional variations that span real-world tolerance bands. Clearance between parts ranges from 0 to a maximum of 2.66 mm, with many parts within the 0.1-0.5 mm range.

Image for simulated assembly components such as nut-and-bolt, USB, and Wi-Fi connector
Figure 6. Rendering of a simulated NIST Task Board 1, demonstrating the provided assets

Factory provides three robotic assembly scenes for Isaac Gym that can be used for developing planning and control algorithms, collecting simulated sensor data for supervised learning, and training RL agents. Each scene contains a Franka robot and disassembled assemblies from the NIST Task Board 1.

The assets can be randomized in types and locations across all environments. All scenes have been tested with up to 128 simultaneous environments on an NVIDIA RTX 3090 GPU. The scenes are shown below:

Images of a robot arm for different assembly tasks such as robot arm plus nut-and-bolt, robot arm plus USB connector, and robot arm plus gears
Figure 7. Factory robotic assembly environments

The seven robot controllers available in the learning environments include a joint-space inverse differential kinematics (IK) motion controller, a joint-space inverse dynamics (ID) controller, a task-space impedance controller, an operational space motion controller, an open-loop force controller, a closed-loop proportional force controller, and a hybrid force-motion controller.

The researchers intend that the models, environments, and controllers continuously grow with contributions from them and the community. 

Proof-of-concept RL policies

Factory employs GPU-accelerated on-policy RL to solve the most contact-rich task on NIST Task Board 1: assembling a nut onto a bolt. Like many assembly tasks, such a procedure is long-horizon and challenging to learn end-to-end. The problem was separated into three phases:

  1. Pick: The robot grasps the nut with a parallel-jaw gripper from a random location on a work surface.
  2. Place: The robot transports the nut to the top of a bolt fixed to the surface.
  3. Screw: The robot brings the nut into contact with the bolt, engages the mating threads, and tightens the nut until it contacts the base of the bolt head.
GIF of trained robot arm picking up a nut.
Figure 8. A trained robot arm picking up a nut, one of the achieved goal states of the trained subpolicies for FrankaNutBoltEnv
GIF of trained robot arm placing a nut onto a bolt.
Figure 9. A trained robot arm placing a nut onto a bolt, one of the achieved goal states of the trained subpolicies for FrankaNutBoltEnv
GIF of trained robot arm screwing a nut onto a bolt.
Figure 10. A trained robot arm screwing a nut onto a bolt, one of the achieved goal states of the trained subpolicies for FrankaNutBoltEnv

The training was done on a single GPU. Large randomizations were applied to the initial position and orientation of the objects with a batch of 3-4 policies trained simultaneously using proximal policy optimization (PPO). Each batch takes 1-1.5 hours to train and each subpolicy is trained in over 128 environments with a maximum of 1,024 policy updates for rapid experimentation. The success rate was 98.4% at test time.

Finally, to evaluate the potential for sim-to-real transfer (transferring the policy learned in simulation to real-world robotics systems), the researchers compared the contact forces generated during these interactions in simulation to contact forces measured in the real world by humans performing the same task with a wrench. For more information, see the R-PAL Daily Interactive Manipulation (DIM) dataset.

The figure below shows that the histogram of the simulation Fasten Nut lies in the middle of the histogram of the Real Fasten Nut, which shows a strong consistency with the real-world values.

Graph showing histogram of real and simulated contact forces
Figure 11. Comparison of simulated contact forces during screw subpolicy execution with analogous real-world contact forces from the Daily Interactive Manipulation (DIM) dataset

Conclusion and future directions

Although Factory was developed with robotic assembly as a motivating application, there are no limitations on using the methods for entirely different tasks within robotics, such as grasping complex non-convex shapes in home environments, locomotion on uneven outdoor terrain, and non-prehensile manipulation of aggregates of objects.

The future direction of this work is to realize full end-to-end simulation for complex physical interactions, including techniques for efficiently transferring the trained policies to real-world robotic systems. This can potentially minimize cost and risk, improve safety, and achieve efficient behaviors.

One day, every advanced industrial manufacturing robot might be trained in simulation using such techniques for seamless transfer to the real world.

Towards this end, NVIDIA developers are working to refine the physics simulation methods used by the Factory research so that they can be used within Omniverse Isaac Sim. Limited functionality is already present, and will become more robust over time.

Get started with Factory

The Factory RL environments will also be available in future versions of the Omniverse Isaac Gym Environments for Isaac Sim.