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During in-hand manipulation, robots must be able to continuously estimate the pose of the object in order to generate appropriate control actions. The performance of algorithms for pose estimation hinges on the robot's sensors being able to detect discriminative geometric object features, but previous sensing modalities are unable to make such measurements robustly. The robot's fingers can occlude the view of environment- or robot-mounted image sensors, and tactile sensors can only measure at the local areas of contact. Motivated by fingertip-embedded proximity sensors' robustness to occlusion and ability to measure beyond the local areas of contact, we present the first evaluation of proximity sensor based pose estimation for in-hand manipulation. We develop a novel two-fingered hand with fingertip-embedded optical time-of-flight proximity sensors as a testbed for pose estimation during planar in-hand manipulation. Here, the in-hand manipulation task consists of the robot moving a cylindrical object from one end of its workspace to the other. We demonstrate, with statistical significance, that proximity-sensor based pose estimation via particle filtering during in-hand manipulation: a) exhibits 50% lower average pose error than a tactile-sensor based baseline; b) empowers a model predictive controller to achieve 30% lower final positioning error compared to when using tactile-sensor based pose estimates. 

We describe a single fingertip-mounted sensing system for robot manipulation that provides proximity (pre-touch), contact detection (touch), and force sensing (post-touch). The sensor system consists of optical time-of-flight range measurement modules covered in a clear elastomer. Because the elastomer is clear, the sensor can detect and range nearby objects, as well as measure deformations caused by objects that are in contact with the sensor and thereby estimate the applied force. We examine how this sensor design can be improved with respect to invariance to object reflectivity, signal-to-noise ratio, and continuous operation when switching between the distance and force measurement regimes. By harnessing time-of-flight technology and optimizing the elastomer-air boundary to control the emitted light's path, we develop a sensor that is able to seamlessly transition between measuring distances of up to 50 mm and contact forces of up to 10 newtons. We demonstrate that our sensor improves manipulation accuracy in a block unstacking task. Thorough instructions for manufacturing the sensor from inexpensive, commercially available components are provided, as well as all relevant hardware design files and software sources. 

This paper presents methods for improved teleoperation in dynamic environments in which the objects to be manipulated are moving, but vision may not meet size, biocompatibility, or maneuverability requirements. In such situations, the object could be tracked through non-geometric means, such as heat, radioactivity, or other markers. In order to safely explore a region, we use an optical time-of-flight pretouch sensor to detect (and range) target objects prior to contact. Information from these sensors is presented to the user via haptic virtual fixtures. This combination of techniques allows the teleoperator to “feel” the object without an actual contact event between the robot and the target object. Thus it provides the perceptual benefits of touch interaction to the operator, without incurring the negative consequences of the robot contacting unknown geometrical structures; premature contact can lead to damage or unwanted displacement of the target. The authors propose that as the geometry of the scene transitions from completely unknown to partially explored, haptic virtual fixtures can both prevent collisions and guide the user towards areas of interest, thus improving exploration speed. Experimental results show that for situations that are not amenable to vision, haptically-presented pretouch sensor information allows operators to more effectively explore moving objects.