Learning Excavation of Rigid Objects with Offline Reinforcement Learning

Autonomous excavation is a challenging task. The unknown contact dynamics between the excavator bucket and the terrain could easily result in large contact forces and jamming problems during excavation. Traditional model-based methods struggle to handle such problems due to complex dynamic modeling. In this paper, we formulate the excavation skills with three novel manipulation primitives. We propose to learn the manipulation primitives with offline reinforcement learning (RL) to avoid large amounts of online robot interactions. The proposed method can learn efficient penetration skills from sub-optimal demonstrations, which contain sub-trajectories that can be ``stitched" together to formulate an optimal trajectory without causing jamming. We evaluate the proposed method with extensive experiments on excavating a variety of rigid objects and demonstrate that the learned policy outperforms the demonstrations. We also show that the learned policy can quickly adapt to unseen and challenging fragmented rocks with online fine-tuning.Comment: Submitted to IROS 202

Intermittent Antibiotic Treatment Accelerated the Development of Colitis in IL-10 Knockout Mice

Many epidemiological studies suggest an association between antibiotic exposure and the development of inflammatory bowel disease [IBD]. However, the majority of these studies are observational and still the question remains, “Does the specific antibiotic administration regimen play a role in the development of colitis?” This study aimed to compare the possible effects of continuous and intermittent antibiotic exposure on the development of colitis using a colitis-susceptible IL-10 knockout [IL-10–/–] mouse model

GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning

In this work, we first formulate the problem of robotic water scooping using goal-conditioned reinforcement learning. This task is particularly challenging due to the complex dynamics of fluids and the need to achieve multi-modal goals. The policy is required to successfully reach both position goals and water amount goals, which leads to a large convoluted goal state space. To overcome these challenges, we introduce Goal Sampling Adaptation for Scooping (GOATS), a curriculum reinforcement learning method that can learn an effective and generalizable policy for robot scooping tasks. Specifically, we use a goal-factorized reward formulation and interpolate position goal distributions and amount goal distributions to create curriculum throughout the learning process. As a result, our proposed method can outperform the baselines in simulation and achieves 5.46% and 8.71% amount errors on bowl scooping and bucket scooping tasks, respectively, under 1000 variations of initial water states in the tank and a large goal state space. Besides being effective in simulation environments, our method can efficiently adapt to noisy real-robot water-scooping scenarios with diverse physical configurations and unseen settings, demonstrating superior efficacy and generalizability. The videos of this work are available on our project page: https://sites.google.com/view/goatscooping

A novel MR device with variable stiffness and damping capability

This paper proposes a novel device based on the Magnetorheological (MR) fluid which has the capability to change stiffness and damping under control. MR fluid is a type of smart material whose properties could be controlled by the external magnetic field. Most of MR devices are MR dampers, which normally are used as variable damping devices. The presented device consists of two hydro-cylinder-spring structures and one MR valve linking these two structures. The rheological characteristics of MR fluid in the fluid flow channels of MR valve are controlled by the strength of magnetic fields, which directly affect the link conditions. The equivalent stiffness and damping coefficients of the device thus varies with the rheological characteristics of MR fluid simultaneously. A mathematical model is established to describe the properties of the proposed device based on the Bouc-wen model. The mathematical model the simulation results indicate that the proposed device can control both the stiffness and damping which has potential to be applied for restrain vibration mitigation efficiently

Encapsulation kinetics and dynamics of carbon monoxide in clathrate hydrate.

Carbon monoxide clathrate hydrate is a potentially important constituent in the solar system. In contrast to the well-established relation between the size of gaseous molecule and hydrate structure, previous work showed that carbon monoxide molecules preferentially form structure-I rather than structure-II gas hydrate. Resolving this discrepancy is fundamentally important to understanding clathrate formation, structure stabilization and the role the dipole moment/molecular polarizability plays in these processes. Here we report the synthesis of structure-II carbon monoxide hydrate under moderate high-pressure/low-temperature conditions. We demonstrate that the relative stability between structure-I and structure-II hydrates is primarily determined by kinetically controlled cage filling and associated binding energies. Within hexakaidecahedral cage, molecular dynamic simulations of density distributions reveal eight low-energy wells forming a cubic geometry in favour of the occupancy of carbon monoxide molecules, suggesting that the carbon monoxide-water and carbon monoxide-carbon monoxide interactions with adjacent cages provide a significant source of stability for the structure-II clathrate framework

Modifying Adhesive Materials to Improve the Longevity of Resinous Restorations

Dental caries is a common disease on a global scale. Resin composites are the most popular materials to restore caries by bonding to tooth tissues via adhesives. However, multiple factors, such as microleakage and recurrent caries, impair the durability of resinous restorations. Various innovative methods have been applied to develop adhesives with particular functions to tackle these problems, such as incorporating matrix metalloproteinase inhibitors, antibacterial or remineralizing agents into bonding systems, as well as improving the mechanical/chemical properties of adhesives, even combining these methods. This review will sum up the latest achievements in this field