Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

Robots that navigate among pedestrians use collision avoidance algorithms to enable safe and efficient operation. Recent works present deep reinforcement learning as a framework to model the complex interactions and cooperation. However, they are implemented using key assumptions about other agents' behavior that deviate from reality as the number of agents in the environment increases. This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules. This work also introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size. The proposed algorithm outperforms our previous approach in simulation as the number of agents increases, and the algorithm is demonstrated on a fully autonomous robotic vehicle traveling at human walking speed, without the use of a 3D Lidar

Robust federated learning with noisy communication

Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server. Nevertheless, it is impractical to achieve perfect acquisition of the local models in wireless communication due to the noise, which also brings serious effect on federated learning. To tackle this challenge in this paper, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and worst-case model. Due to the non-convexity of the problem, regularizer approximation method is proposed to make it tractable. Regarding the worst-case model, we utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function value are demonstrated via simulation for the proposed designs

Starch retrogradation in tuber : mechanisms and its implications on microstructure and glycaemic features of potatoes : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in School of Food and Advanced Technology at Massey University, Palmerston North, Manawatū, New Zealand

Figures are re-used under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence, or with the publisher's permission.An increase in the occurrence of diabetes mellitus, cardiovascular disease and obesity in recent years led to the project “Starch retrogradation in tuber: mechanisms and its implications on microstructure and glycaemic features of potatoes”. Potato products can play a role in mitigating these hyperglycaemic events, if starch in these processed products is slowly digested and/or starch-derived glucose is released into the circulation in a slower and more attenuated manner. Three stages were envisaged for the project with an aim to create slowly digestible starch in whole potato tuber (in tuber) through starch retrogradation. Plant-based whole food systems, such as potato tubers encompass different cell compartments, (e.g. cell wall, vacuole, cytoplasm and intracellular spaces) within which starch gelatinisation and retrogradation occur, subject to local interactions of other cell components and water availability. Structural changes of potato starch during retrogradation in tuber and its resulting digestibility were studied. Different water pools in a cooked whole tuber were discerned by the low-field NMR (LF-NMR), having relaxation times T20 at 400 ms. A significant reduction in eGI was observed after cooling and storage compared to freshly cooked tubers. Reheating of retrograded tuber restored some of the susceptibility to enzymatic hydrolysis and internal water mobility. Longer chilled storage (7 days) yet improved the stability of retrograded tuber against reheating effects (at 90 °C). Realignment of the gelatinised amylose and amylopectin changed the distribution of crystalline and amorphous regions during refrigerated storage and subsequent reheating, resulting in starch digestibility varying with treatment combination. Several, but not all, of time-temperature cycle processes were observed to induce stepwise nucleation and propagation, facilitating starch retrogradation in tuber more than did storage fixed at 4 °C. Sous vide processing (at 55 and 65°C), akin to annealing, combined with starch retrogradation in tuber, resulted in potatoes with intermediate eGI (40-72). After reheating at 60°C, the eGI of sous vide cooked-chill potatoes increased moderately, displaying a mixture of partially gelatinised starch and swollen granules. Food processing, i.e. optimum TTC process or sous vide process might facilitate the formation of retrograded starch in tuber, resulting in a reduced eGI (than freshly cooked tubers). To retain the resistance to digestive enzymes in retrograded starch in tuber, reheating at low temperatures (50-60°C) were needed

Socially Aware Motion Planning with Deep Reinforcement Learning

For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.Comment: 8 page