3 research outputs found
4D topology optimization: Integrated optimization of the structure and self-actuation of soft bodies for dynamic motions
Topology optimization is a powerful tool utilized in various fields for
structural design. However, its application has primarily been restricted to
static or passively moving objects, mainly focusing on hard materials with
limited deformations and contact capabilities. Designing soft and actively
moving objects, such as soft robots equipped with actuators, poses challenges
due to simulating dynamics problems involving large deformations and intricate
contact interactions. Moreover, the optimal structure depends on the object's
motion, necessitating a simultaneous design approach. To address these
challenges, we propose "4D topology optimization," an extension of
density-based topology optimization that incorporates the time dimension. This
enables the simultaneous optimization of both the structure and self-actuation
of soft bodies for specific dynamic tasks. Our method utilizes multi-indexed
and hierarchized density variables distributed over the spatiotemporal design
domain, representing the material layout, actuator layout, and time-varying
actuation. These variables are efficiently optimized using gradient-based
methods. Forward and backward simulations of soft bodies are done using the
material point method, a Lagrangian-Eulerian hybrid approach, implemented on a
recent automatic differentiation framework. We present several numerical
examples of self-actuating soft body designs aimed at achieving locomotion,
posture control, and rotation tasks. The results demonstrate the effectiveness
of our method in successfully designing soft bodies with complex structures and
biomimetic movements, benefiting from its high degree of design freedom.Comment: 36 pages, 27 figures; for supplementary video, see
https://youtu.be/sPY2jcAsNY
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Inference Algorithms and Sensorimotor Representations in Brains and Machines
Animals function in a 3D world in which survival depends on robust, well-controlled actions. Historically, researchers in Artificial Intelligence (AI) and neuroscience have explored sensory and motor systems independently. There is a growing body of literature in AI and neuroscience to suggest that they actually work in tandem. While there has been a great deal of work on vision and audition as sensory modalities in these fields, one could argue that a more fundamental modality in biology is haptics, or the sense of touch. In this thesis, we will look at building computational models that integrate tactile sensing with other sensory modalities to perform manipulation-like tasks in robots and discrimination tasks in mice. We will also explore the problem of inference through the lens of Markov Chain Monte Carlo methods (MCMC). We elaborate on the ideas discussed in this thesis in the introduction presented in Chapter 1. A challenging problem one often faces when applying probabilistic mathematical models to the study of sensory-motor systems and other problems involving learning of inference is sampling. Hamiltonian Markov Chain Monte Carlo (HMC) algorithms can efficiently draw representative samples from complex probabilistic models. Most MCMC methods rely on detailed balance to ensure that we can sample from the correct distribution. This constraint can be relaxed in discrete state spaces such as those employed by HMC type methods. In Chapter 2, we study HMC methods without detailed balance to explore faster convergence. Markov jump processes are stochastic processes on discrete state space but continuous in time. In Chapter 3, we use Markov Jump Processes to simulate waiting times along with generalized detailed balance. This waiting time ,we show, helps generate samples faster. Most MCMC methods are plagued by slow simulation times on discrete computing systems. In Chapter 4, we explore HMC in analog circuits where the problem of generating samples from a distribution is mapped to the problem of sampling charge in a capacitor.The second half of this dissertation focuses on the role of haptics in perception and action. Manipulation is a fundamental problem for artificial and biological agents. High dimensional actuators (say, fingers, trunks,etc) are really hard to control. In Chapter 5, we present an approach to learn to actuate dexterous manipulators to grasp objects in simulation. Haptics as a sensory modality is critical to many manipulation tasks. Employing haptics in high dimensional dextrous actuators is challenging. In Chapter 6, we explore how intrinsic curiosity and haptics can be used to learn exploration strategies for discrimination of objects with dextrous hands. A key component to make tactile sensing a possibility is the availability of cheap, efficient, scalable hardware. Chapter 7 presents results for tactile servoing using a physical gelsight sensor. Traditional neuroscience texts delineate sensory and motor systems as two independent systems yet recent results suggest that this may not be entirely complete. That is, there is evidence to suggest that the representations in the cortex is more distributed than is accepted. Finally in Chapter 8, we explore building a computational model of spiking neural data collected from both the barrel and motor cortices during free and active whisking. These works help towards understanding sensorimotor representations in the context of haptics and high dimensional controls. We conclude with a discussion on future directions in Chapter 9