Search for: All records

While distributional reinforcement learning (DistRL) has been empirically effective, the question of when and why it is better than vanilla, non-distributional RL has remained unanswered. This paper explains the benefits of DistRL through the lens of small-loss bounds, which are instance-dependent bounds that scale with optimal achievable cost. Particularly, our bounds converge much faster than those from non-distributional approaches if the optimal cost is small. As warmup, we propose a distributional contextual bandit (DistCB) algorithm, which we show enjoys small-loss regret bounds and empirically outperforms the state-of-the-art on three real-world tasks. In online RL, we propose a DistRL algorithm that constructs confidence sets using maximum likelihood estimation. We prove that our algorithm enjoys novel small-loss PAC bounds in low-rank MDPs. As part of our analysis, we introduce the l1 distributional eluder dimension which may be of independent interest. Then, in offline RL, we show that pessimistic DistRL enjoys small-loss PAC bounds that are novel to the offline setting and are more robust to bad single-policy coverage. 

Abstract The utility of visible light for 3D printing has increased in recent years owing to its accessibility and reduced materials interactions, such as scattering and absorption/degradation, relative to traditional UV light‐based processes. However, photosystems that react efficiently with visible light often require multiple molecular components and have strong and diverse absorption profiles, increasing the complexity of formulation and printing optimization. Herein, a streamlined method to select and optimize visible light 3D printing conditions is described. First, green light liquid crystal display (LCD) 3D printing using a novel resin is optimized through traditional empirical methods, which involves resin component selection, spectroscopic characterization, time‐intensive 3D printing under several different conditions, and measurements of dimensional accuracy for each printed object. Subsequent analytical quantification of dynamic photon absorption during green light polymerizations unveils relationships to cure depth that enables facile resin and 3D printing optimization using a model that is a modification to the Jacob's equation traditionally used for stereolithographic 3D printing. The approach and model are then validated using a distinct green light‐activated resin for two types of projection‐based 3D printing. 

We present the Fourier Light field Camera Array Microscope (FL-CAM) for high-throughput, single-snapshot 3D imaging. The FL-CAM substitutes a synchronized array of 48 independent imaging systems for micro-lens array of typical light field systems. 

https://arxiv.org/abs/2209.02486 

We present a computational 3D profilometric microscope employing an array of 54 cameras and 3-axis scanning to produce multi-TB datasets per sample. Using stereo and sharpness cues, our self-supervised reconstruction algorithm generates 6-gigapixel reconstructions with micron-scale resolution across >110 cm². 

Abstract With 3D printing, the desire is to be “limited only by imagination,” and although remarkable advancements have been made in recent years, the scope of printable materials remains narrow compared to other forms of manufacturing. Light‐driven polymerization methods for 3D printing are particularly attractive due to unparalleled speed and resolution, yet the reliance on high‐energy UV/violet light in contemporary processes limits the number of compatible materials due to pervasive absorption, scattering, and degradation at these short wavelengths. Such issues can be addressed with visible‐light photopolymerizations. However, these lower‐energy methods often suffer from slow reaction times and sensitivity to oxygen, precluding their utility in 3D printing processes that require rapid hardening (curing) to maximize build speed and resolution. Herein, multifunctional thiols are identified as simple additives to enable rapid high‐resolution visible‐light 3D printing under ambient (atmospheric O₂) conditions that rival modern UV/violet‐based technology. The present process is universal, providing access to commercially relevant acrylic resins with a range of disparate mechanical responses from strong and stiff to soft and extensible. Pushing forward, the insight presented within this study will inform the development of next‐generation 3D‐printing materials, such as multicomponent hydrogels and composites. 

We present a real-time spectral-scanning frequency-modulated continuous wave (FMCW) 3D imaging and velocimetry system that can produce 3D depth maps at 33 Hz, with 48° × 68° field of view (FOV) and 32.8-cm depth range. Each depth map consists of 507 × 500 pixels, with 0.095° × 0.14° angular resolution and 2.82-mm depth resolution. The system employs a grating for beam steering and a telescope for angular FOV magnification. Quantitative depth, reflectivity, and axial velocity measurements of a static 3D printed depth variation target and a moving robotic arm are demonstrated.