Robert Howe

Rui Ouyang, Robert Howe

Commercial six-axis force-torque sensors suffer from being some combination of expensive, fragile, and hard-to-use. We propose a new fiducial-based design which addresses all three points. The sensor uses an inexpensive webcam and can be fabricated using a consumer-grade 3D printer. Open-source software is used to estimate the 3D pose of the fiducials on the sensor, which is then used to calculate the applied force-torque. A browser-based (installation free) interface demonstrates ease-of-use. The sensor is very light and can be dropped or thrown with little concern. We characterize our prototype in dynamic conditions under compound loading, finding a mean $R^2$ of over 0.99 for the $F_x, F_y, M_x$, and $M_y$ axes, and over 0.87 and 0.90 for the $F_z$ and $M_z$ axes respectively. The open source design files allow the sensor to be adapted for diverse applications ranging from robot fingers to human-computer interfaces, while the sdesign principle allows for quick changes with minimal technical expertise. This approach promises to bring six-axis force-torque sensing to new applications where the precision, cost, and fragility of traditional strain-gauge based sensors are not appropriate. The open-source sensor design can be viewed at http://sites.google.com/view/fiducialforcesensor.

Alexander Koenig, Zixi Liu, Lucas Janson et al.

A long-standing question in robot hand design is how accurate tactile sensing must be. This paper uses simulated tactile signals and the reinforcement learning (RL) framework to study the sensing needs in grasping systems. Our first experiment investigates the need for rich tactile sensing in the rewards of RL-based grasp refinement algorithms for multi-fingered robotic hands. We systematically integrate different levels of tactile data into the rewards using analytic grasp stability metrics. We find that combining information on contact positions, normals, and forces in the reward yields the highest average success rates of 95.4% for cuboids, 93.1% for cylinders, and 62.3% for spheres across wrist position errors between 0 and 7 centimeters and rotational errors between 0 and 14 degrees. This contact-based reward outperforms a non-tactile binary-reward baseline by 42.9%. Our follow-up experiment shows that when training with tactile-enabled rewards, the use of tactile information in the control policy's state vector is drastically reducible at only a slight performance decrease of at most 6.6% for no tactile sensing in the state. Since policies do not require access to the reward signal at test time, our work implies that models trained on tactile-enabled hands are deployable to robotic hands with a smaller sensor suite, potentially reducing cost dramatically.