Developing whole-body tactile skins for robots remains a challenging task, as existing solutions often prioritize modular, one-size-fits-all designs, which, while versatile, fail to account for the robot's specific shape and the unique demands of its operational context. In this work, we introduce GenTact Toolbox, a computational pipeline for creating versatile wholebody tactile skins tailored to both robot shape and application domain. Our method includes procedural mesh generation for conforming to a robot's topology, task-driven simulation to refine sensor distribution, and multi-material 3D printing for shape-agnostic fabrication. We validate our approach by creating and deploying six capacitive sensing skins on a Franka Research 3 robot arm in a human-robot interaction scenario. This work represents a shift from “one-size-fits-all” tactile sensors toward context-driven, highly adaptable designs that can be customized for a wide range of robotic systems and applications. The project website is available at https://hiro-group.ronc.one/gentacttoolbox

Robot end effectors with proximity detection and contact sensing capabilities can reactively position the gripper to align objects and ensure successful grasps. In this paper, we introduce SonicFinger, an acoustic aura based sensing system capable of full-surface pre-touch and contact sensing. A single piezoelectric transducer embedded within a novel 3D printed finger is excited using a monotone to create an acoustic aura encompassing the finger; this enables pre-touch sensing and gripper alignment, while changes in finger-transducer acoustic coupling indicate contact. SonicFinger is low-cost, compact, and easy to manufacture and assemble. Sensing capabilities are evaluated using a set of objects with various physical properties such as optical reflectivity, dielectric constants, mechanical properties, and acoustic absorption. A dataset with over 8,000 proximity and contact events is collected. Our system shows a pre-touch detection true positive rate (TPR) of 92.4% and a true negative rate (TNR) of 95.3%. Contact detection experiments show a TPR of 93.7% and a TNR of 98.7%. Furthermore, pretouch detection information from Sonic Finger is used to adjust the robot grippers pose to align a target object at the center of both fingers.

Contact sensing and localization capabilities that mimic human skin are highly desirable for robots. In this paper, we introduce AcouSkin, an acoustic wave based full surface contact localization system. Acoustic waves produced by piezoelectric transceivers using a monotone are coupled to surfaces turning them into an active sensor. Our system leverages information from four piezoelectric transceivers mounted on the surface of an acrylic sheet and vacuum cleaner robot bumper to localize contacts to 18 unique segments. We first characterize acoustic wave propagation based on signal and material properties and then propose hardware and software methods to realize full surface contact localization. Our results show that AcouSkin can reliably localize contact on a flat acrylic sheet with 18 uniformly spaced locations across a 54cm length with mean absolute error (MAE) of ≤ 1 locations using maximum likelihood estimator (MLE) and multilayer perceptron (MLP) models. On the vacuum cleaner robot bumper AcouSkin shows a zero MAE. Further, the system is also able to localize contacts made using forces as low as 2N (Newtons) and as high as 20N. Overall, AcouSkin provides full surface contact localization while requiring minimal instrumentation with easy deployment on real-world robots.

Current motion planning approaches rely on binary collision checking to evaluate the validity of a state and thereby dictate where the robot is allowed to move. This approach leaves little room for robots to engage in contact with an object, as is often necessary when operating in densely cluttered spaces. In this work, we propose an alternative method that considers contact states as high-cost states that the robot should avoid but can traverse if necessary to complete a task. More specifically, we introduce Contact Admissible Transition-based Rapidly exploring Random Trees (CAT-RRT)11Supplementary video and open source code [1]., a planner that uses a novel per-link cost heuristic to find a path by traversing high-cost obstacle regions. Through extensive testing, we find that state-of-the-art optimization planners tend to over-explore low-cost states, which leads to slow and inefficient convergence to contact regions. Conversely, CAT-RRT searches both low and high-cost regions simultaneously with an adaptive thresholding mechanism carried out at each robot link. This leads to paths with a balance between efficiency, path length, and contact cost.

In this paper, we present AmbiSense, an acoustic field based sensing system that performs proximity detection and bearing estimation for safer physical human-robot interactions. A single low cost piezoelectric transducer is used to setup this novel acoustic sensing modality to create a blindspot-free sound field engulfing a robot arm. Two detection algorithms leveraging spectral information from reflected audio waves of objects entering the acoustic field are proposed to infer object presence and bearing. We also present a new receiver structure which improves signal to noise ratio (SNR). AmbiSense is paired with a collision avoidance inverse kinematic solver for real world deployment on a Kinova Gen3 robot. Validation is performed using ten test objects generating 2000 proximity and bearing estimation events in real world settings, we show that AmbiSense detects proximity with 93.8% sensitivity and 96.6 % specificity. It estimates bearing and maps it to three zones on a robot link with 100% sensitivity and specificity, while using fewer sensors than state of the art methods for similar coverage.

Real-time control is an essential aspect of safe robot operation in the real world with dynamic objects. We present a framework for the analysis of object-aware con-trollers, methods for altering a robot's motion to anticipate and avoid possible collisions. This framework is focused on three design considerations: kinematics, motion profiles, and virtual constraints. Additionally, the analysis in this work relies on verification of robot behaviors using fundamental robot-obstacle experimental scenarios. To showcase the effectiveness of our method we compare three representative object-aware controllers. The comparison uses metrics originating from the design considerations. From the analysis, we find that the design of object-aware controllers often lacks kinematic considerations, continuity of control points, and stability in movement profiles. We conclude that this framework can be used in the future to design, compare, and benchmark obstacle avoidance methods.

In this paper, we present a framework that unites obstacle avoidance and deliberate physical interaction for robotic manipulators. As humans and robots begin to coexist in work and household environments, pure collision avoidance is insufficient, as human–robot contact is inevitable and, in some situations, desired. Our work enables manipulators to anticipate, detect, and act on contact. To achieve this, we allow limited deviation from the robot’s original trajectory through velocity reduction and motion restrictions. Then, if contact occurs, a robot can detect it and maneuver based on a novel dynamic contact thresholding algorithm. The core contribution of this work is dynamic contact thresholding, which allows a manipulator with onboard proximity sensors to track nearby objects and reduce contact forces in anticipation of a collision. Our framework elicits natural behavior during physical human–robot interaction. We evaluate our system on a variety of scenarios using the Franka Emika Panda robot arm; collectively, our results demonstrate that our contribution is not only able to avoid and react on contact, but also anticipate it.

In this paper, we present an accelerometer-based kinematic calibration algorithm to accurately estimate the pose of multiple sensor units distributed along a robot body. Our approach is self-contained, can be used on any robot provided with a Denavit-Hartenberg kinematic model, and on any skin equipped with Inertial Measurement Units (IMUs). To validate the proposed method, we first conduct extensive experimentation in simulation and demonstrate a sub-cm positional error from ground truth data—an improvement of six times with respect to prior work; subsequently, we then perform a real-world evaluation on a seven degrees-of-freedom collaborative platform. For this purpose, we additionally introduce a novel design for a stand-alone artificial skin equipped with an IMU for use with the proposed algorithm and a proximity sensor for sensing distance to nearby objects. In conclusion, in this work, we demonstrate seamless integration between a novel hardware design, an accurate calibration method, and preliminary work on applications: the high positional accuracy effectively enables to locate distributed proximity data and allows for a distributed avoidance controller to safely avoid obstacles and people without the need of additional sensing.