arXiv subjects

Christopher Yu

Publications and source records attributed to Christopher Yu.

6 recordsLinked to original sources

arXiv2025

Heavy lifting tasks via haptic teleoperation of a wheeled humanoid

Humanoid robots can support human workers in physically demanding environments by performing tasks that require whole-body coordination, such as lifting and transporting heavy objects.These tasks, which we refer to as Dynamic Mobile Manipulation (DMM), require the simultaneous control of locomotion, manipulation, and posture under dynamic interaction forces. This paper presents a teleoperation framework for DMM on a height-adjustable wheeled humanoid robot for carrying heavy payloads. A Human-Machine Interface (HMI) enables whole-body motion retargeting from the human pilot to the robot by capturing the motion of the human and applying haptic feedback. The pilot uses body motion to regulate robot posture and locomotion, while arm movements guide manipulation.Real time haptic feedback delivers end effector wrenches and balance related cues, closing the loop between human perception and robot environment interaction. We evaluate the different telelocomotion mappings that offer varying levels of balance assistance, allowing the pilot to either manually or automatically regulate the robot's lean in response to payload-induced disturbances. The system is validated in experiments involving dynamic lifting of barbells and boxes up to 2.5 kg (21% of robot mass), demonstrating coordinated whole-body control, height variation, and disturbance handling under pilot guidance. Video demo can be found at: https://youtu.be/jF270_bG1h8?feature=shared

cs.RO↗

arXiv2022

Denoising Particle Beam Micrographs with Plug-and-Play Methods

In a particle beam microscope, a raster-scanned focused beam of particles interacts with a sample to generate a secondary electron (SE) signal pixel by pixel. Conventionally formed micrographs are noisy because of limitations on acquisition time and dose. Recent work has shown that estimation methods applicable to a time-resolved measurement paradigm can greatly reduce noise, but these methods apply pixel by pixel without exploiting image structure. Raw SE count data can be modeled with a compound Poisson (Neyman Type A) likelihood, which implies data variance that is signal-dependent and greater than the variation in the underlying particle-sample interaction. These statistical properties make methods that assume additive white Gaussian noise ineffective. This paper introduces methods for particle beam micrograph denoising that use the plug-and-play framework to exploit image structure while being applicable to the unusual data likelihoods of this modality. Approximations of the data likelihood that vary in accuracy and computational complexity are combined with denoising by total variation regularization, BM3D, and DnCNN. Methods are provided for both conventional and time-resolved measurements, assuming SE counts are available. In simulations representative of helium ion microscopy and scanning electron microscopy, significant improvements in root mean-squared error (RMSE), structural similarity index measure (SSIM), and qualitative appearance are obtained. Average reductions in RMSE are by factors ranging from 2.24 to 4.11.

physics.med-ph↗

arXiv2022

Non-Line-of-Sight Tracking and Mapping with an Active Corner Camera

The ability to form non-line-of-sight (NLOS) images of changing scenes could be transformative in a variety of fields, including search and rescue, autonomous vehicle navigation, and reconnaissance. Most existing active NLOS methods illuminate the hidden scene using a pulsed laser directed at a relay surface and collect time-resolved measurements of returning light. The prevailing approaches include raster scanning of a rectangular grid on a vertical wall opposite the volume of interest to generate a collection of confocal measurements. These are inherently limited by the need for laser scanning. Methods that avoid laser scanning track the moving parts of the hidden scene as one or two point targets. In this work, based on more complete optical response modeling yet still without multiple illumination positions, we demonstrate accurate reconstructions of objects in motion and a 'map' of the stationary scenery behind them. The ability to count, localize, and characterize the sizes of hidden objects in motion, combined with mapping of the stationary hidden scene, could greatly improve indoor situational awareness in a variety of applications.

eess.IV↗

arXiv2021

Online Beam Current Estimation in Particle Beam Microscopy

In conventional particle beam microscopy, knowledge of the beam current is essential for accurate micrograph formation and sample milling. This generally necessitates offline calibration of the instrument. In this work, we establish that beam current can be estimated online, from the same secondary electron count data that is used to form micrographs. Our methods depend on the recently introduced time-resolved measurement concept, which combines multiple short measurements at a single pixel and has previously been shown to partially mitigate the effect of beam current variation on micrograph accuracy. We analyze the problem of jointly estimating beam current and secondary electron yield using the Cramer-Rao bound. Joint estimators operating at a single pixel and estimators that exploit models for inter-pixel correlation and Markov beam current variation are proposed and tested on synthetic microscopy data. Our estimates of secondary electron yield that incorporate explicit beam current estimation beat state-of-the-art methods, resulting in micrograph accuracy nearly indistinguishable from what is obtained with perfect beam current knowledge. Our novel beam current estimation could help improve milling outcomes, prevent sample damage, and enable online instrument diagnostics.

physics.med-ph↗

arXiv2020

Two-Dimensional Non-Line-of-Sight Scene Estimation from a Single Edge Occluder

Passive non-line-of-sight imaging methods are often faster and stealthier than their active counterparts, requiring less complex and costly equipment. However, many of these methods exploit motion of an occluder or the hidden scene, or require knowledge or calibration of complicated occluders. The edge of a wall is a known and ubiquitous occluding structure that may be used as an aperture to image the region hidden behind it. Light from around the corner is cast onto the floor forming a fan-like penumbra rather than a sharp shadow. Subtle variations in the penumbra contain a remarkable amount of information about the hidden scene. Previous work has leveraged the vertical nature of the edge to demonstrate 1D (in angle measured around the corner) reconstructions of moving and stationary hidden scenery from as little as a single photograph of the penumbra. In this work, we introduce a second reconstruction dimension: range measured from the edge. We derive a new forward model, accounting for radial falloff, and propose two inversion algorithms to form 2D reconstructions from a single photograph of the penumbra. Performances of both algorithms are demonstrated on experimental data corresponding to several different hidden scene configurations. A Cramer-Rao bound analysis further demonstrates the feasibility (and utility) of the 2D corner camera.

eess.IV↗

arXiv2020

Repulsive Curves

Curves play a fundamental role across computer graphics, physical simulation, and mathematical visualization, yet most tools for curve design do nothing to prevent crossings or self-intersections. This paper develops efficient algorithms for (self-)repulsion of plane and space curves that are well-suited to problems in computational design. Our starting point is the so-called tangent-point energy, which provides an infinite barrier to self-intersection. In contrast to local collision detection strategies used in, e.g., physical simulation, this energy considers interactions between all pairs of points, and is hence useful for global shape optimization: local minima tend to be aesthetically pleasing, physically valid, and nicely distributed in space. A reformulation of gradient descent, based on a Sobolev-Slobodeckij inner product enables us to make rapid progress toward local minima---independent of curve resolution. We also develop a hierarchical multigrid scheme that significantly reduces the per-step cost of optimization. The energy is easily integrated with a variety of constraints and penalties (e.g., inextensibility, or obstacle avoidance), which we use for applications including curve packing, knot untangling, graph embedding, non-crossing spline interpolation, flow visualization, and robotic path planning.

cs.GR↗