Periodic Reporting for period 2 - 3DPBio (Computational Models of Motion for Fabrication-aware Design of Bioinspired Systems)
Okres sprawozdawczy: 2021-08-01 do 2023-01-31
Most robots today are designed to be either as rigid as possible (e.g. industrial Kuka robot arms, Boston Dynamics' Spot and Atlas, etc), or they are fully soft (e.g. Soft Robotics' mGrip). In contrast, the morphological designs we see in nature are fundamentally different. Vertebrates, for example, feature a rigid skeletal system that is complemented by various types of soft tissues (articular cartilage and ligaments, muscles and tendons, soft pads that modulate the forces arising from physical interactions with external objects, etc). Indeed, structural compliance arising from the strategic use of soft materials is an integral part of the design of every biomechanical system, defining the performance, efficiency and robustness of its movements. In our quest to create robots that are as skilled and adaptable as their living counterparts, developing designs that seamlessly integrate soft and rigid materials is therefore of utmost importance. Nevertheless, despite decades of research, such hybrid soft-rigid designs are still out of reach.
In defining how future generations of robots will be made, additive manufacturing (AM) technologies will play a pivotal role. This is because they allow us to create designs of unparalleled geometric complexity using a constantly expanding range of materials. Indeed, if past developments are an indication, within the next decade we will be able to fabricate physical structures that approach, at least at the macro scale, the functional sophistication of their biological counterparts. However, while this unprecedented capability enables fascinating opportunities, it also leads to an explosion in the dimensionality of the space that must be explored during the design process. As AM technologies keep evolving, the gap between "what we can produce" and "what we can design" is therefore rapidly growing.
To effectively leverage the extraordinary design possibilities enabled by AM, our project aims to develop the computational and mathematical foundations required to study a fundamental scientific question: how are physical deformations, mechanical movements and overall functional capabilities governed by geometric shape features, material compositions and the design of compliant actuation systems? By enabling computers to reason about this question, our work will establish new ways to algorithmically create digital designs that can be turned into mechanical lifeforms at the push of a button.
In defining how future generations of robots will be made, additive manufacturing (AM) technologies will play a pivotal role. This is because they allow us to create designs of unparalleled geometric complexity using a constantly expanding range of materials. Indeed, if past developments are an indication, within the next decade we will be able to fabricate physical structures that approach, at least at the macro scale, the functional sophistication of their biological counterparts. However, while this unprecedented capability enables fascinating opportunities, it also leads to an explosion in the dimensionality of the space that must be explored during the design process. As AM technologies keep evolving, the gap between "what we can produce" and "what we can design" is therefore rapidly growing.
To effectively leverage the extraordinary design possibilities enabled by AM, our project aims to develop the computational and mathematical foundations required to study a fundamental scientific question: how are physical deformations, mechanical movements and overall functional capabilities governed by geometric shape features, material compositions and the design of compliant actuation systems? By enabling computers to reason about this question, our work will establish new ways to algorithmically create digital designs that can be turned into mechanical lifeforms at the push of a button.
Since the start of the project, we have structured our investigations into three complementary lines of research.
Stream 1: Robotic Materials
The focus for this stream of research is to develop computational tools for physics-based modelling and optimization of 3D printable structures that are designed to undergo large deformations. Our research objectives here are twofold. First, we aim to capture the physical behavior of such 3D printed structures using simulation models that strike a balance between computational effort and predictive power. Second, we seek to effectively map structures optimized in simulation to fabrication blueprints. Our efforts in this area have already led to a novel representation of deformable objects based on neural implicit models, as well as an entirely new way of creating textile-like soft robotic materials using 3D printing.
Stream 2: Computational methods for mechatronic systems
In this research stream, we focus our efforts on formalizing novel algorithms that can be used to co-design motions, actuator configurations and force transmission elements such as rigid or flexible linkages, tendon-like strands, membrane reinforcement structures, etc for new types of mechatronic systems. Our efforts in this area have recently led to a first-of-its kind computational design system for complex animatronic systems, as well as numerical optimization methods for multi-degree-of-freedom mechanisms that feature both rigid and compliant elements.
Stream 3: Motion controllers for hybrid soft-rigid robots
With our third research stream, our goal is to formalise the process of developing control policies for hybrid rigid-soft robots. We focus, in particular, on bio-inspired motion controllers for agile locomotion and manipulation behaviors where rich interactions with the environment are key. As a first step forward, we have developed a new type of differentiable simulator, and we have shown how it can be used to generate locomotion gaits for compliant legged robots using trajectory optimization techniques. To mitigate the relatively high computational costs associated with trajectory optimization, we have also begun to formalize and analyse policy learning algorithms that leverage differentiable simulators.
Stream 1: Robotic Materials
The focus for this stream of research is to develop computational tools for physics-based modelling and optimization of 3D printable structures that are designed to undergo large deformations. Our research objectives here are twofold. First, we aim to capture the physical behavior of such 3D printed structures using simulation models that strike a balance between computational effort and predictive power. Second, we seek to effectively map structures optimized in simulation to fabrication blueprints. Our efforts in this area have already led to a novel representation of deformable objects based on neural implicit models, as well as an entirely new way of creating textile-like soft robotic materials using 3D printing.
Stream 2: Computational methods for mechatronic systems
In this research stream, we focus our efforts on formalizing novel algorithms that can be used to co-design motions, actuator configurations and force transmission elements such as rigid or flexible linkages, tendon-like strands, membrane reinforcement structures, etc for new types of mechatronic systems. Our efforts in this area have recently led to a first-of-its kind computational design system for complex animatronic systems, as well as numerical optimization methods for multi-degree-of-freedom mechanisms that feature both rigid and compliant elements.
Stream 3: Motion controllers for hybrid soft-rigid robots
With our third research stream, our goal is to formalise the process of developing control policies for hybrid rigid-soft robots. We focus, in particular, on bio-inspired motion controllers for agile locomotion and manipulation behaviors where rich interactions with the environment are key. As a first step forward, we have developed a new type of differentiable simulator, and we have shown how it can be used to generate locomotion gaits for compliant legged robots using trajectory optimization techniques. To mitigate the relatively high computational costs associated with trajectory optimization, we have also begun to formalize and analyse policy learning algorithms that leverage differentiable simulators.
Our early investigations into the three research streams outlined above have already led to ten peer reviewed articles which have been published in the top venues for robotics and visual computing research. Through these publications, we have been able to push forward the state of the art in the areas of robotic materials, computational design of hybrid soft-rigid robotic systems, and motion control for bio-inspired robots, as briefly summarized above. Moving forward, our expectation is that we will continue to publish high quality, high impact articles in these fields. For instance, we are currently exploring different geometric templates for robotic materials, and the impact they have on the macro-level deformation behavior of the structures we can create with them. As the main challenge we have moving forward, we are working towards a mathematical model of morphological computation that predicts how changes to the physical design of a hybrid rigid-soft robot improves or hinders its desired function.