Cybersickness is a major obstacle to immersive VR experiences, and various strategies have been proposed to overcome it. In particular, providing artificial sensory stimuli is considered a prominent approach, which improves presence and real-virtual congruence, thereby helping the situation. In this study, we evaluated the effectiveness of artificial sensory stimuli in inducing virtual walking sensations in mitigating cybersickness. We conducted two experiments involving a total of 80 participants: (1) a static user experiment ($\mathrm{N}=40$), in which participants used controller-based steering without physical movement, and (2) a dynamic user experiment ($\mathrm{N}=40$), in which participants walked on an omnidirectional treadmill, enabling virtual movement similar to real walking. In both experiments, participants were divided into two groups based on the presence or absence of vibrotactile feedback. We collected heart rate and subjective discomfort data during exposure to a VR environment and then evaluated cybersickness and presence. Across both experiments, the results indicated that virtual walking sensations induced through vibrotactile feedback significantly reduced cybersickness without negatively affecting the perceived task load or presence. Additionally, in the dynamic user locomotion experiment, a greater reduction in cybersickness was observed when vibrotactile feedback was present. These findings suggest that even if the real-virtual sensations are synchronized, vibrotactile feedback may induce cognitive distraction that further mitigates cybersickness.

This study presents a recursive algorithm for solving the regularised least squares problem for online identification of rigid body dynamic model parameters with emphasis on the physical consistency of estimated inertial parameters. One of the geometric approaches is to use a regulariser that represents how close the pseudo-inertia matrix is to a given reference on the feasible manifold in the regression problem. The proposed extension enables memory-efficient online learning in addition to the benefits of geometry-aware convex regularisation using the log-determinant divergence of the pseudo-inertia matrix. Also, the recursive version endows the estimator with the capability to deal with time-variation of parameters by introducing an optional forgetting mechanism. The characteristics of the recursive regularised least squares algorithm is demonstrated using the MIT Cheetah 3 leg swinging experiment dataset and compared to the existing batch optimisation method.

This paper compares force-feedback methods for sensorimotor skill transfer in a VR engraving art task. Using a 3-DoF force-feedback device, we implemented a VR engraving art system that provides two different haptic feedback methods of haptic guidance and disturbance. As haptic guidance, the force and profile we gathered from an art expert were presented as is, while the inverse direction of the profile was given as haptic disturbance. We evaluated the user’s task performance with two methods with a baseline of no haptic feedback and the results showed haptic disturbance led to slightly better performance than guidance.

Programming manipulation behaviors can become increasingly difficult with a growing number and complexity of manipulation tasks, particularly in a dynamic and unstructured environment. Recent progress in unsupervised skill discovery algorithms has shown great promise in learning an extensive collection of behaviors without extrinsic supervision. On the other hand, safety is one of the most critical factors for real- world robot applications. As skill discovery methods typically encourage exploratory and dynamic behaviors, it can often be the case that a large portion of learned skills remain too dangerous and unsafe. In this paper, we introduce the novel problem of Safety-Aware Skill Discovery, which aims to learn, in a task-agnostic fashion, a repertoire of reusable skills that are inherently safe to be composed for solving downstream tasks. We present a computationally tractable algorithm that learns a latent-conditioned skill policy that maximizes intrinsic rewards regularized with a safety-critic that can model any user-defined safety constraints. Using the pretrained safe skill repertoire, hierarchical reinforcement learning can solve multiple downstream tasks without the need for explicit consideration of safety during training and testing. We evaluate our algorithm on a collection of force-controlled robotic manipulation tasks in simulation and show promising downstream task performance while satisfying safety constraints.

Accurate knowledge of ecological condition (EC) is crucial to evaluate the deviation of a given ecosystem from a reference condition, and is of interest to forest managers as it helps them prioritize management activities. The present study aimed to estimate EC classes of the Talladega National Forest (NF) using NAIP images, and airborne laser scanning (ALS) data, as well as field measurements from 255 plots. The results indicated that the EC classes could be distinguished using zq25, imean, and p5thmetrics from ALS, as well as Enhanced Vegetation Index. Among them, we used zq25to generate a map for the entire study area. Accordingly, the dominant EC was class 3, suggesting that almost half of the forestland is composed of young and dense stands with woody understory. This cover type is not desirable in terms of wildfire risk and far from the historical conditions. Thus, NF managers might thin and/or prescribed burn these areas to improve EC.

Manipulation involves a broad spectrum of skills, e.g., polishing, peeling, flipping, screwing, etc., requiring complex and delicate control over both force and position. This paper aims at designing an optimal haptic interface for providing a robot with direct demonstrations of human's innate intelligence in performing a wide range of force-based bimanual manipulation tasks. Based on the proprioceptive actuation mechanism, kinodynamic design parameters of the (dual) 7-DOF haptic arm are optimized so as to maximize the force transparency perceived by the operator over the full real-scale workspace of human arm while also ensuring other important constraints including robot-to-operator collision and singularity avoidance, payload, controlled stiffness, etc. 2.65 kg of average reflective mass and 1500 N/m of controlled stiffness is achieved over the entire workspace. We show the efficacy of our haptic interface by demonstrating various force-based manipulation tasks with a light-weight anthropomorphic bimanual manipulator, LIMS2-AMBIDEX [1].

Robotic painting tasks in the real world are often made complicated by the highly complex and stochastic nature of the dynamics that underlie, e.g., physical contact between the painting tool and a canvas, color blendings between painting mediums, and many more. Simulation-based inverse graphics algorithms, for example, can not be directly transferred to the real-world due in large to the considerable gap in the viable range of painting strokes the robot can accurately generate onto the physical canvas. In this paper, we aim at minimizing this gap by appealing to a data-driven skill learning approach. The core idea lies in allowing the robot to learn continuous stroke-level skills that jointly encodes action trajectories and painted outcomes from an extensive collection of human demonstrations. We demonstrate the efficacy of our method through extensive real-world experiments using a 4-dof torque-controllable manipulator with a digital canvas(iPad).

Recent work has shed light on the often unreliable performance of constrained least-squares estimation methods for robot mass-inertial parameter identification, particularly for high degree-of-freedom systems subject to noisy and incomplete measurements. Instead, differential geometric identification methods have proven to be significantly more accurate and robust. These methods account for the fact that the mass-inertial parameters reside in a curved Riemannian space, and allow perturbations in the mass-inertial properties to be measured in a coordinate-invariant manner. Yet, a continued drawback of existing geometric methods is that the corresponding optimization problems are inherently nonconvex, have numerous local minima, and are computationally highly intensive to solve. In this paper, we propose a convex formulation under the same coordinate-invariant Riemannian geometric framework that directly addresses these and other deficiencies of the geometric approach. Our convex formulation leads to a globally optimal solution, reduced computations, faster and more reliable convergence, and easy inclusion of additional convex constraints. The main idea behind our approach is an entropic divergence measure that allows for the convex regularization of the inertial parameter identification problem. Extensive experiments with the 3-DoF MIT Cheetah leg, the 7-DoF AMBIDEX tendon-driven arm, and a 16-link articulated human model show markedly improved robustness and generalizability vis-à-vis existing vector space methods while ensuring fast, guaranteed convergence to the global solution.

Existing adaptive robot control laws typically require an engineering choice of a constant adaptation gain matrix, which often involves repeated and time-consuming trial and error. Moreover, physical consistency of the estimated inertial parameters or the uniform positive definiteness of the estimated robot mass matrix cannot in general be guaranteed without nonsmooth corrections, e.g., projection to the boundary of the feasible parameter set. In this paper we present a natural adaptive control law that mitigates many of these difficulties, by exploiting the coordinate-invariant differential geometric structure of the space of physically consistent inertial parameters. Our approach provides a more generalizable and physically consistent adaptation law for the robot parameters without significant additional computations compared to existing methods. Simulation results showing markedly improved tracking error convergence over existing adaptive control laws are provided as validation.