1. Why we are interested in computational robot co-design?

Designing robots could be understood as a process of optimizing robot design for the given task. Following question could be: How does human optimize the robot design for a given task? One possible answer could be, the human often rely on experience and intuition to design robots. However, this process is often time-consuming, expensive, and sub-optimal.

Several successful robot design frameworks include:

  1. Bio-inspired design: This approach draws inspiration from nature to design robots that mimic the behavior and structure of biological organisms. Examples include robotic fish, birds, and insects. In this approach, we often rely on how the creatures in nature have evolved to adapt to their environment.

  2. Soft robotics: This approach focuses on designing robots with soft and flexible materials. The use of soft materials allows for more adaptable interaction with the external environment. This adaptability magically works well in unstructured environments. Assume a robot foot covered with soft materials, it can adapt to different terrains and obstacles without requiring precise control or sensing.

  3. Modular robotics: This approach involves designing robots using modular components that can be easily reconfigured and combined to create different robot designs. This approach reduces the dimensionality of the design space and allows for more efficent exploration of this design space.

Although these approaches have shown great success in designing robots, following question could be “how can we control the robot?” or “how can we guarantee the obtained robot design will work appropriately considering the robot control?” or “can we find the best robot design without considering how it will be controlled?”

These questions lead us to the concept of robot co-design, which involves the simultaneous optimization of both the robot’s physical embodiment (body) and its control strategy (brain). We can also consider the robot co-design as a bioinspired robot design approach, since in nature, the body and brain of animals have co-evolved to adapt to their environment and perform specific tasks effectively.

So, how can we achieve the robot co-design? Should we wait for thousands of years of evolution? Or should we rely on huge amount of human effort to design and optimize the robot body and controller iteratively?

To answer these questions, let’s think about the current trend of AI and robot technology. We have seen significant advances in AI, especially in the field of generative models and reinforcement learning. From the robot technology perspective, we have seen significant advances in physics simulation and optimization algorithms.

Form this hint, our lab will focus on the computational robot co-design approach, which leverages the power of AI, optimization, and simulation to automate and scale the robot design process.

2. What have we done so far?

2.1. Robot co-design through diffusion based generative model

Project Lead: Tsun-Hsuan Wang

Collaborators: Juntian Zheng, Pingchuan Ma, Yilun Du, Byungchul Kim,Andrew Spielberg, Joshua B. Tenenbaum, Chuang Gan, Daniela Rus

Conference: Neurips 2023

Links: Paper, Video

Motivation

Traditional gradient-based optimization, such as differentiable simulation, may struggle with the high dimensionality and non-linearity of robot shape design. We sought a method that could explore this vast space more broadly and discover unconventional but effective morphologies.

Method

We designed an evolution-inspired design loop:

  1. Generation – The first population of ~500 candidate robots is generated with random shapes from diffusion-based generative model.
  2. Evaluation – Each design is tested in a physics simulator on the jumping and landing tasks.
  3. Selection – Only the top 12 performers are chosen based on metrics such as jump height and landing stability.
  4. Reproduction – These survivors guide the creation of the next generation of candidates.

This cycle is repeated across five generations.

Results
  • +41% improvement in jumping height.
  • +210% improvement in landing success rate.
  • Generation of novel, non-intuitive morphologies that outperform initial designs.
Contribution

This work inspired from our previous work (explained in section 2.1) but with distinct differences:

  • The use of human prior design to guide the initial generation. This enables the final designs to be 100% manufacturable using 3D printing and off-the-shelf components. This also reduces design search space significantly, enabling more efficient exploration of the design space.
  • The focus on a specific task (jumping and landing) with clear performance metrics. This allows for more targeted optimization and evaluation of designs.

2.2. Jumping Robot Design through Evolution-Inspired Approach

Project Lead: Byungchul Kim

Collaborators: Tsun-Hsuan Wang, Daniela Rus

Conference: ICRA 2025

Links: Paper, Video link

Motivation

Traditional gradient-based optimization, such as differentiable simulation, may struggle with the high dimensionality and non-linearity of robot shape design. We sought a method that could explore this vast space more broadly and discover unconventional but effective morphologies.

Method

We designed an evolution-inspired design loop:

  1. Generation – The first population of ~500 candidate robots is generated with random shapes from diffusion-based generative model.
  2. Evaluation – Each design is tested in a physics simulator on the jumping and landing tasks.
  3. Selection – Only the top 12 performers are chosen based on metrics such as jump height and landing stability.
  4. Reproduction – These survivors guide the creation of the next generation of candidates.

This cycle is repeated across five generations.

Results
  • +41% improvement in jumping height.
  • +210% improvement in landing success rate.
  • Generation of novel, non-intuitive morphologies that outperform initial designs.
Contribution

This work inspired from our previous work (explained in section 2.1) but with distinct differences:

  • The use of human prior design to guide the initial generation. This enables the final designs to be 100% manufacturable using 3D printing and off-the-shelf components. This also reduces design search space significantly, enabling more efficient exploration of the design space.
  • The focus on a specific task (jumping and landing) with clear performance metrics. This allows for more targeted optimization and evaluation of designs.

2.3. What’s next?

Although we have made interesting results in computational robot co-design, our current works have only focused on robot shape design using diffusion-based generative models. However, we can explore more complex robot design space including robot morphology (shape, structure, material), robot control (controller, policy), and robot fabrication (manufacturing process, cost). Let’s explore more complex robot design space using more advanced AI, optimization, and simulation techniques.

This page was written with the help of large language model (LLM).