In recent years, the development of robotics and artificial intelligence (AI) systems has been nothing short of remarkable. As these systems continue to evolve, they are being utilized in increasingly complex and unstructured environments, such as autonomous driving, aerial robotics, and natural language processing. As a consequence, programming their behaviors manually or defining their behavior through the reward functions [as done in reinforcement learning (RL)] has become exceedingly difficult. This is because such environments require a high degree of flexibility and adaptability, making it challenging to specify an optimal set of rules or reward signals that can account for all the possible situations. In such environments, learning from an expert's behavior through imitation is often more appealing. This is where imitation learning (IL) comes into play - a process where desired behavior is learned by imitating an expert's behavior, which is provided through demonstrations.This article aims to provide an introduction to IL and an overview of its underlying assumptions and approaches. It also offers a detailed description of recent advances and emerging areas of research in the field. Additionally, this article discusses how researchers have addressed common challenges associated with IL and provides potential directions for future research. Overall, the goal of this article is to provide a comprehensive guide to the growing field of IL in robotics and AI.

This paper presents MDP policy learning for agents to learn strategic behavior-how to play board games-during multimodal dialogues.Policies are trained offline in simulation, with dialogues carried out in a formal language.The agent has a temporary belief state for the dialogue, and a persistent knowledge store represented as an extensive-form game tree.How well the agent learns a new game from a dialogue with a simulated partner is evaluated by how well it plays the game, given its dialoguefinal knowledge state.During policy training, we control for the simulated dialogue partner's level of informativeness in responding to questions.The agent learns best when its trained policy matches the current dialogue partner's informativeness.We also present a novel data collection for training natural language modules.Human subjects who engaged in dialogues with a baseline system rated the system's language skills as above average.Further, results confirm that human dialogue partners also vary in their informativeness.

We present an approach for robot learning from demonstration and communication applied to simple board games like Connect Four. In such games, a visual representation of a winning condition on the board can be converted to an extensive form representation that can then support computation of a winning strategy. We present a robot that can learn simple games from responses to visual questions based on synthesized images, or to verbal questions. We illustrate how reliance on both modalities leads to more efficient learning.

Network on chip (NoC) is a scalable interconnection architecture for ever increasing communication demand between processing cores. However, in nanoscale technology size, NoC lifetime is limited due to aging processes of negative bias temperature instability, hot carrier injection, and electromigration. Usually, because of unbalanced utilization of NoC resources, some parts of the network experience more thermal stress and duty cycle in comparison with other parts, which may accelerate chip failure. To slow down the aging rate of NoC, this paper proposes an oblivious routing algorithm called location-based aging-resilient Xy-Yx (LAXY) to distribute packet flow over entire network. LAXY is based on the fact that dimension-ordered routing algorithms imposes the highest traffic load on the central nodes in mesh topologies. To balance the traffic over the network, certain routers at the east and the west of NoC, with dimension-order XY routing, statically are configured as YX. Various configurations have been explored for LAXY and the simulations show a specific configuration, called Fishtail, increases mean time to failure of the routers and interconnects by about 42% and 56%, respectively. Moreover, by balancing the load over the network, LAXY improves overall packet latency by about 7% in average, with negligible area overhead.

This paper quantifies the difference in resource demand between modern and classic NoC workloads. In the paper, we show that modern workloads are able to better utilize higher numbers of VCs and smaller C factors in order to attain performance and energy efficiency. This is because of the high throughput and possible local congestions in their traffic pattern. As a result, such workloads are more suitable for concurrency and redundancy energy reduction techniques where the voltage and frequency are reduced simultaneously and the increased power budget is used for introducing additional resources to the network in order to improve the performance.

This paper presents a new power-efficient electrocardiogram acquisition system that uses a fully digital architecture to reduce the power consumption and chip area. The proposed architecture is compatible with digital CMOS technology and is capable of operating with a low supply voltage of 0.5 V. In this architecture, no analog block, e.g., low-noise amplifier (LNA), and filters, and no passive elements, such as ac coupling capacitors, are used. A moving average voltage-to-time converter is used, which behaves instead of the LNA and antialiasing filter. A digital feedback loop is employed to cancel the impact of the dc offset on the circuit, which eliminates the need for coupling capacitors. The circuit is implemented in 0.18-um CMOS process. The simulation results show that the front-end circuit consumes 274 nW of power.