We propose a novel excavation (i.e., digging) trajectory planning framework for industrial autonomous robotic excavators, which emulates the strategies of human expert operators to optimize the excavation of (complex/unmodellable) soils while also upholding robustness and safety in practice. First, we encode the trajectory with dynamic movement primitives (DMP), which is known to robustly preserve qualitative shape of the trajectory and attraction to (variable) end-points (i.e., start-points of swing/dumping), while also being data-efficient due to its structure, thus, suitable for our purpose, where expert data collection is expensive. We further shape this DMPbased trajectory to be expert-emulating, by learning the shaping force of the DMP-dynamics from the real expert excavation data via a neural network (i.e., MLP (multi-layer perceptron)). To cope with (possibly dangerous) underground uncertainties (e.g., pipes, rocks), we also real-time modulate the expert-emulating (nominal) trajectory to prevent excessive build-up of excavation force by using the feedback of its online estimation. The proposed framework is then validated/demonstrated by using an industrial-scale autonomous robotic excavator, with the associated data also presented here.

Tactile information in a palm is a necessary component in manipulating and perceiving large or heavy objects. Noting this, we investigate human sensitivity to tactile haptic feedback in a palm for an improved user interface design. To provide distributed tactile pattern, we propose an ungrounded haptic interface, which can stimulate multiple locations in a palm, independently. Two experiments were conducted to evaluate human sensitivity to distributed tactile patterns. The first experiment tested participants' sensitivity to tactile patterns by sub-sections in a palm, and a significant effect of the sub-section on the sensitivity was observed. In the second experiment, participants identified pressure distribution patterns in the palm collected from real-life objects with the percent correct of 71.4 % and IT (information transfer) was 1.58 bits.

When one manipulates a large or bulky object, s/he utilizes tactile information at both fingers and the palm. Our goal is to efficiently convey contact information to a user's hand during interaction with a virtual object. We propose a haptic system that can provide haptic feedback to thumb/middle finger/index finger and on a palm. Our interface design utilizes a novel compact mechanism to provide haptic information to the palm. Also, we propose a haptic rendering strategy to calculate haptic feedback continuously. We demonstrate that cutaneous feedback on the palm improves the haptic perception of a large virtual object compared to when there is only kinesthetic feedback to the fingers.