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  1. Free, publicly-accessible full text available July 1, 2023
  2. Free, publicly-accessible full text available April 1, 2023
  3. Despite the large amount of research on kinesthetic haptic devices and haptic effect modeling, there is limited work assessing the perceived realism of kinesthetic model renderings. Identifying the impact of haptic effect parameters in perceived realism can help to inform the required accuracy of kinesthetic renderings. In this work, we model common kinesthetic haptic effects and evaluate the perceived realism of varying model parameters via a user study. Our results suggest that parameter accuracy requirements to achieve realistic ratings vary depending on the specific haptic parameter.
    Free, publicly-accessible full text available March 21, 2023
  4. Many applications in robotics require computing a robot manipulator's "proximity" to a collision state in a given configuration. This collision proximity is commonly framed as a summation over closest Euclidean distances between many pairs of rigid shapes in a scene. Computing many such pairwise distances is inefficient, while more efficient approximations of this procedure, such as through supervised learning, lack accuracy and robustness. In this work, we present an approach for computing a collision proximity function for robot manipulators that formalizes the trade-off between efficiency and accuracy and provides an algorithm that gives control over it. Our algorithm, called Proxima, works in one of two ways: (1) given a time budget as input, the algorithm returns an as-accurate-as-possible proximity approximation value in this time; or (2) given an accuracy budget, the algorithm returns an as-fast-as-possible proximity approximation value that is within the given accuracy bounds. We show the robustness of our approach through analytical investigation and simulation experiments on a wide set of robot models ranging from 6 to 132 degrees of freedom. We demonstrate that controlling the trade-off between efficiency and accuracy in proximity computations via our approach can enable safe and accurate real-time robot motion-optimization even on high-dimensionalmore »robot models.« less
    Free, publicly-accessible full text available January 1, 2023
  5. In telemanipulation, showing the user multiple views of the remote environment can offer many benefits, although such different views can also create a problem for control. Systems must either choose a single fixed control frame, aligned with at most one of the views or switch between view-aligned control frames, enabling view-aligned control at the expense of switching costs. In this paper, we explore the trade-off between these options. We study the feasibility, benefits, and drawbacks of switching the user's control frame to align with the actively used view during telemanipulation. We additionally explore the effectiveness of explicit and implicit methods for switching control frames. Our results show that switching between multiple view-specific control frames offers significant performance gains compared to a fixed control frame. We also find personal preferences for explicit or implicit switching based on how participants planned their movements. Our findings offer concrete design guidelines for future multi-camera interfaces.
    Free, publicly-accessible full text available January 1, 2023
  6. To understand how the realism of a kinesthetic haptic rendering is affected by the accurate selection of the rendering model parameters, we conducted a preliminary user study where subjects compared three real-world objects to their equivalent haptic rendering. The subjects rated the rendering realism as the model parameters were varied about their nominal values. The results suggest that the required accuracy of various haptic rendering parameters is not equally important when considering the perceived realism.
  7. Manipulations of a constrained object often use a non-rigid grasp that allows the object to rotate relative to the end effector. This orientation slip strategy is often present in natural human demonstrations, yet it is generally overlooked in methods to identify constraints from such demonstrations. In this paper, we present a method to model and recognize prehensile orientation slip in human demonstrations of constrained interactions. Using only observations of an end effector, we can detect the type of constraint, parameters of the constraint, and orientation slip properties. Our method uses a novel hierarchical model selection method that is informed by multiple origins of physics-based evidence. A study with eight participants shows that orientation slip occurs in natural demonstrations and confirms that it can be detected by our method.
  8. In this work, we present a per-instant pose optimization method that can generate configurations that achieve specified pose or motion objectives as best as possible over a sequence of solutions, while also simultaneously avoiding collisions with static or dynamic obstacles in the environment. We cast our method as a weighted sum non-linear constrained optimization-based IK problem where each term in the objective function encodes a particular pose objective. We demonstrate how to effectively incorporate environment collision avoidance as a single term in this multi-objective, optimization-based IK structure, and provide solutions for how to spatially represent and organize external environments such that data can be efficiently passed to a real-time, performance-critical optimization loop. We demonstrate the effectiveness of our method by comparing it to various state-of-the-art methods in a testbed of simulation experiments and discuss the implications of our work based on our results.
  9. In this paper, we present a meta-algorithm intended to accelerate many existing path optimization algorithms. The central idea of our work is to strategically break up a waypoint path into consecutive groupings called ``pods,'' then optimize over various pods concurrently using parallel processing. Each pod is assigned a color, either blue or red, and the path is divided in such a way that adjacent pods of the same color have an appropriate buffer of the opposite color between them, reducing the risk of interference between concurrent computations. We present a path splitting algorithm to create blue and red pod groupings and detail steps for a meta-algorithm that optimizes over these pods in parallel. We assessed how our method works on a testbed of simulated path optimization scenarios using various optimization tasks and characterize how it scales with additional threads. We also compared our meta-algorithm on these tasks to other parallelization schemes. Our results show that our method more effectively utilizes concurrency compared to the alternatives, both in terms of speed and optimization quality.