Recent approaches in robot perception follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term Interactive Perception (IP). This view of perception provides the following benefits. First, interaction with the environment creates a rich sensory signal that would otherwise not be present. Second, knowledge of the regularity in the combined space of sensory data and action parameters facilitates the prediction and interpretation of the sensory signal. In this survey, we postulate this as a principle for robot perception and collect evidence in its support by analyzing and categorizing existing work in this area. We also provide an overview of the most important applications of IP. We close this survey by discussing remaining open questions. With this survey, we hope to help define the field of Interactive Perception and to provide a valuable resource for future research.

Interactive Perception: Leveraging Action in Perception and Perception in Action

This article reviews the technology behind creating artificial touch sensations and the relevant aspects of human touch We focus on the design and control of haptic devices and discuss the best practices for generating distinct and effective touch sensations Artificial haptic sensations can present information to users, help them complete a task, augment or replace the other senses, and add immersiveness and realism to virtual interactions We examine these applications in the context of different haptic feedback modalities and the forms that haptic devices can take We discuss the prior work, limitations, and design considerations of each feedback modality and individual haptic technology We also address the need to consider the neuroscience and perception behind the human sense of touch in the design and control of haptic devices

Haptics: The Present and Future of Artificial Touch Sensation

When a tool is tapped on or dragged over an object surface, vibrations are induced in the tool, which can be captured using acceleration sensors. The tool-surface interaction additionally creates audible sound waves, which can be recorded using microphones. Features extracted from camera images provide additional information about the surfaces. We present an approach for tool-mediated surface classification that combines these signals and demonstrate that the proposed method is robust against variable scan-time parameters. We examine freehand recordings of 69 textured surfaces recorded by different users and propose a classification system that uses perception-related features, such as hardness, roughness, and friction; selected features adapted from speech recognition, such as modified cepstral coefficients applied to our acceleration signals; and surface texture-related image features. We focus on mitigating the effect of variable contact force and exploration velocity conditions on these features as a prerequisite for a robust machine-learning-based approach for surface classification. The proposed system works without explicit scan force and velocity measurements. Experimental results show that our proposed approach allows for successful classification of textured surfaces under variable freehand movement conditions, exerted by different human operators. The proposed subset of six features, selected from the described sound, image, friction force, and acceleration features, leads to a classification accuracy of 74 percent in our experiments when combined with a Naive Bayes classifier.

Multimodal Feature-Based Surface Material Classification

The Tactile Internet will enable users to physically explore remote environments and to make their skills available across distances. An important technological aspect in this context is the acquisition, compression, transmission, and display of haptic information. In this paper, we present the fundamentals and state of the art in haptic codec design for the Tactile Internet. The discussion covers both kinesthetic data reduction and tactile signal compression approaches. We put a special focus on how limitations of the human haptic perception system can be exploited for efficient perceptual coding of kinesthetic and tactile information. Further aspects addressed in this paper are the multiplexing of audio and video with haptic information and the quality evaluation of haptic communication solutions. Finally, we describe the current status of the ongoing IEEE standardization activity P1918.1.1 which has the ambition to standardize the first set of codecs for kinesthetic and tactile information exchange across communication networks.

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Haptic Codecs for the Tactile Internet

This paper introduces the Penn Haptic Texture Toolkit (HaTT), a publicly available repository of haptic texture models for use by the research community. HaTT includes 100 haptic texture and friction models, the recorded data from which the models were made, images of the textures, and the code and methods necessary to render these textures using an impedance-type haptic interface such as a SensAble Phantom Omni. This paper reviews our previously developed methods for modeling haptic virtual textures, describes our technique for modeling Coulomb friction between a tooltip and a surface, discusses the adaptation of our rendering methods for display using an impedance-type haptic device, and provides an overview of the information included in the toolkit. Each texture and friction model was based on a ten-second recording of the force, speed, and high-frequency acceleration experienced by a handheld tool moved by an experimenter against the surface in a natural manner. We modeled each texture's recorded acceleration signal as a piecewise autoregressive (AR) process and stored the individual AR models in a Delaunay triangulation as a function of the force and speed used when recording the data. To increase the adaptability and utility of HaTT, we developed a method for resampling the texture models so they can be rendered at a sampling rate other than the 10 kHz used when recording data. Measurements of the user's instantaneous normal force and tangential speed are used to synthesize texture vibrations in real time. These vibrations are transformed into a texture force vector that is added to the friction and normal force vectors for display to the user.

One hundred data-driven haptic texture models and open-source methods for rendering on 3D objects

Texture gives real objects an important perceptual dimension that is largely missing from virtual haptic interactions due to limitations of standard modeling and rendering approaches. This paper presents a set of methods for creating a haptic texture model from tool-surface interaction data recorded by a human in a natural and unconstrained manner. The recorded high-frequency tool acceleration signal, which varies as a function of normal force and scanning speed, is segmented and modeled as a piecewise autoregressive (AR) model. Each AR model is labeled with the source segment's median force and speed values and stored in a Delaunay triangulation to create a model set for a given texture. We use these texture model sets to render synthetic vibration signals in real time as a user interacts with our TexturePad system, which includes a Wacom tablet and a stylus augmented with a Haptuator. We ran a human-subject study with two sets of ten participants to evaluate the realism of our virtual textures and the strengths and weaknesses of this approach. The results indicated that our virtual textures accurately capture and recreate the roughness of real textures, but other modeling and rendering approaches are required to completely match surface hardness and slipperiness.

Juliette Unwin

Papers

Modeling and Rendering Realistic Textures from Unconstrained Tool-Surface Interactions