44 research outputs found
Evolving soft locomotion in aquatic and terrestrial environments: effects of material properties and environmental transitions
Designing soft robots poses considerable challenges: automated design
approaches may be particularly appealing in this field, as they promise to
optimize complex multi-material machines with very little or no human
intervention. Evolutionary soft robotics is concerned with the application of
optimization algorithms inspired by natural evolution in order to let soft
robots (both morphologies and controllers) spontaneously evolve within
physically-realistic simulated environments, figuring out how to satisfy a set
of objectives defined by human designers. In this paper a powerful evolutionary
system is put in place in order to perform a broad investigation on the
free-form evolution of walking and swimming soft robots in different
environments. Three sets of experiments are reported, tackling different
aspects of the evolution of soft locomotion. The first two sets explore the
effects of different material properties on the evolution of terrestrial and
aquatic soft locomotion: particularly, we show how different materials lead to
the evolution of different morphologies, behaviors, and energy-performance
tradeoffs. It is found that within our simplified physics world stiffer robots
evolve more sophisticated and effective gaits and morphologies on land, while
softer ones tend to perform better in water. The third set of experiments
starts investigating the effect and potential benefits of major environmental
transitions (land - water) during evolution. Results provide interesting
morphological exaptation phenomena, and point out a potential asymmetry between
land-water and water-land transitions: while the first type of transition
appears to be detrimental, the second one seems to have some beneficial
effects.Comment: 37 pages, 22 figures, currently under review (journal
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics
Physical simulators have been widely used in robot planning and control.
Among them, differentiable simulators are particularly favored, as they can be
incorporated into gradient-based optimization algorithms that are efficient in
solving inverse problems such as optimal control and motion planning.
Simulating deformable objects is, however, more challenging compared to rigid
body dynamics. The underlying physical laws of deformable objects are more
complex, and the resulting systems have orders of magnitude more degrees of
freedom and therefore they are significantly more computationally expensive to
simulate. Computing gradients with respect to physical design or controller
parameters is typically even more computationally challenging. In this paper,
we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical
simulator for deformable objects, ChainQueen, based on the Moving Least Squares
Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects
including contact and can be seamlessly incorporated into inference, control
and co-design systems. We demonstrate that our simulator achieves high
precision in both forward simulation and backward gradient computation. We have
successfully employed it in a diverse set of control tasks for soft robots,
including problems with nearly 3,000 decision variables.Comment: In submission to ICRA 2019. Supplemental Video:
https://www.youtube.com/watch?v=4IWD4iGIsB4 Project Page:
https://github.com/yuanming-hu/ChainQuee
A hybrid dynamic model for bio-inspired soft robots - Application to a flapping-wing micro air vehicle.
International audienceThe paper deals with the dynamic modeling of bio-inspired robots with soft appendages such as flying insect-like or swimming fish-like robots. In order to model such soft systems, we propose to use the Mobile Multibody System framework introduced in [1][2][3]. In such a framework, the robot is considered as a tree-like structure of rigid bodies where the evolution of the position of the joints is governed by stress-strain laws or control torques. Based on the Newton-Euler formulation of these systems, we propose a new algorithm able to compute at each step of a time loop both the net and passive joint accelerations along with the control torques supplied by the motors. To illustrate, based on previous work [4], the proposed algorithm is applied to the simulation of the hovering flight of a soft flapping-wing insect-like robot (see the attached video)
An Amphibious Fully-Soft Miniature Crawling Robot Powered by Electrohydraulic Fluid Kinetic Energy
Miniature locomotion robots with the ability to navigate confined
environments show great promise for a wide range of tasks, including search and
rescue operations. Soft miniature locomotion robots, as a burgeoning field,
have attracted significant research interest due to their exceptional terrain
adaptability and safety features. In this paper, we introduce a fully-soft
miniature crawling robot directly powered by fluid kinetic energy generated by
an electrohydraulic actuator. Through optimization of the operating voltage and
design parameters, the crawling velocity of the robot is dramatically enhanced,
reaching 16 mm/s. The optimized robot weighs 6.3 g and measures 5 cm in length,
5 cm in width, and 6 mm in height. By combining two robots in parallel, the
robot can achieve a turning rate of approximately 3 degrees/s. Additionally, by
reconfiguring the distribution of electrodes in the electrohydraulic actuator,
the robot can achieve 2 degrees-of-freedom translational motion, improving its
maneuverability in narrow spaces. Finally, we demonstrate the use of a soft
water-proof skin for underwater locomotion and actuation. In comparison with
other soft miniature crawling robots, our robot with full softness can achieve
relatively high crawling velocity as well as increased robustness and recovery
Collective control of modular soft robots via embodied Spiking Neural Cellular Automata
Voxel-based Soft Robots (VSRs) are a form of modular soft robots, composed of
several deformable cubes, i.e., voxels. Each VSR is thus an ensemble of simple
agents, namely the voxels, which must cooperate to give rise to the overall VSR
behavior. Within this paradigm, collective intelligence plays a key role in
enabling the emerge of coordination, as each voxel is independently controlled,
exploiting only the local sensory information together with some knowledge
passed from its direct neighbors (distributed or collective control). In this
work, we propose a novel form of collective control, influenced by Neural
Cellular Automata (NCA) and based on the bio-inspired Spiking Neural Networks:
the embodied Spiking NCA (SNCA). We experiment with different variants of SNCA,
and find them to be competitive with the state-of-the-art distributed
controllers for the task of locomotion. In addition, our findings show
significant improvement with respect to the baseline in terms of adaptability
to unforeseen environmental changes, which could be a determining factor for
physical practicability of VSRs.Comment: Workshop on "From Cells to Societies: Collective Learning across
Scales" at the International Conference on Learning Representations
(Cells2Societies@ICLR
On the Entanglement between Evolvability and Fitness: an Experimental Study on Voxel-based Soft Robots
The concept of evolvability, that is the capacity to produce heritable and adaptive phenotypic variation, is crucial in the current understanding of evolution. However, while its meaning is intuitive, there is no consensus on how to quantitatively measure it. As a consequence, it is hard to evaluate the interplay between evolvability and fitness and its dependency on key factors like the evolutionary algorithm (EA) or the representation of the individuals. Here, we propose to use MAP-Elites, a well-established Quality Diversity EA, as a support structure for measuring evolvability and for highlighting its interplay with fitness. We map the solutions generated during the evolutionary process to a MAP-Elites-like grid and then visualize their fitness and evolvability as maps. This procedures does not affect the EA execution and can hence be applied to any EA: it only requires to have two descriptors for the solutions that can be used to meaningfully characterize them. We apply this general methodology to the case of Voxel-based Soft Robots, a kind of modular robots with a body composed of uniform elements whose volume is individually varied by the robot brain. Namely, we optimize the robots for the task of locomotion using evolutionary computation. We consider four representations, two for the brain only and two for both body and brain of the VSR, and two EAs (MAP-Elites and a simple evolutionary strategy) and examine the evolvability and fitness maps. The experiments suggest that our methodology permits to discover interesting patterns in the maps: fitness maps appear to depend more on the representation of the solution, whereas evolvability maps appear to depend more on the EA. As an aside, we find that MAP-Elites is particularly effective in the simultaneous evolution of the body and the brain of Voxel-based Soft Robots
Learned and Controlled Autonomous Robotic Exploration in an Extreme, Unknown Environment
Exploring and traversing extreme terrain with surface robots is difficult,
but highly desirable for many applications, including exploration of planetary
surfaces, search and rescue, among others. For these applications, to ensure
the robot can predictably locomote, the interaction between the terrain and
vehicle, terramechanics, must be incorporated into the model of the robot's
locomotion. Modeling terramechanic effects is difficult and may be impossible
in situations where the terrain is not known a priori. For these reasons,
learning a terramechanics model online is desirable to increase the
predictability of the robot's motion. A problem with previous implementations
of learning algorithms is that the terramechanics model and corresponding
generated control policies are not easily interpretable or extensible. If the
models were of interpretable form, designers could use the learned models to
inform vehicle and/or control design changes to refine the robot architecture
for future applications. This paper explores a new method for learning a
terramechanics model and a control policy using a model-based genetic
algorithm. The proposed method yields an interpretable model, which can be
analyzed using preexisting analysis methods. The paper provides simulation
results that show for a practical application, the genetic algorithm
performance is approximately equal to the performance of a state-of-the-art
neural network approach, which does not provide an easily interpretable model.Comment: Published in: 2019 IEEE Aerospace Conference Date of Conference: 2-9
March 2019 Date Added to IEEE Xplore: 20 June 201