@article{Venkata2023b,
title = {KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multi-Robot Systems},
author = {Sanjay Sarma Oruganti Venkata, Ramviyas Parasuraman, Ramana Pidaparti},
url = {https://ieeexplore.ieee.org/abstract/document/10183654},
doi = {10.1109/TRO.2023.3290449},
year = {2023},
date = {2023-07-13},
journal = {IEEE Transactions on Robotics},
volume = {30},
number = {5},
pages = {4114 - 4130},
abstract = {Multi-Robot and Multi-Agent Systems demonstrate collective (swarm) intelligence through systematic and distributed integration of local behaviors in a group. Agents sharing knowledge about the mission and environment can enhance performance at individual and mission levels. However, this is difficult to achieve, partly due to the lack of a generic framework for transferring part of the known knowledge (behaviors) between agents. This paper presents a new knowledge representation framework and a transfer strategy called KT-BT: Knowledge Transfer through Behavior Trees. The KT-BT framework follows
a query-response-update mechanism through an online Behavior Tree framework, where agents broadcast queries for unknown conditions and respond with appropriate knowledge using a condition-action-control sub-flow. We embed a novel grammar structure called stringBT that encodes knowledge, enabling behavior sharing. We theoretically investigate the properties of the KT-BT framework in achieving homogeneity of high knowledge across the entire group compared to a heterogeneous system without the capability of sharing their knowledge. We extensively verify our framework in a simulated multi-robot search and rescue problem. The results show successful knowledge transfers and improved group performance in various scenarios. We further study the effects of opportunities and communication
range on group performance, knowledge spread, and functional heterogeneity in a group of agents, presenting interesting insights.},
keywords = {autonomy, behavior-trees, heterogeneity, multi-robot, planning},
pubstate = {published},
tppubtype = {article}
}

@conference{Tahir2023,
title = {Mobile Robot Control and Autonomy Through Collaborative Twin},
author = {Nazish Tahir and Ramviyas Parasuraman},
doi = { 10.1109/PerComWorkshops56833.2023.10150325},
year = {2023},
date = {2023-03-17},
booktitle = {2023 IEEE PerCom - International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events},
abstract = {When a mobile robot lacks high onboard computing or networking capabilities, it can rely on remote computing architecture for its control and autonomy. In this paper, we introduce a novel collaborative twin strategy for control and autonomy on resource-constrained robots. The practical implementation of such a strategy entails a mobile robot system divided into a cyber (simulated) and physical (real) space separated over a communication channel where the physical robot resides on the site of operation guided by a simulated autonomous agent from a remote location maintained over a network. Building on top of the digital twin concept, our collaboration twin is capable of autonomous navigation through an advanced SLAM-based path planning algorithm, while the physical robot is capable of tracking the Simulated twin's velocity and communicating feedback generated through interaction with its environment. We proposed a prioritized path planning application to the test in a collaborative teleoperation system of a physical robot guided by Simulation Twin's autonomous navigation. We examine the performance of a physical robot led by autonomous navigation from the Collaborative Twin and assisted by a predicted force received from the physical robot. The experimental findings indicate the practicality of the proposed simulation-physical twinning approach and provide computational and network performance improvements compared to typical remote computing and digital twin approaches.},
keywords = {autonomy, cooperation, networking},
pubstate = {published},
tppubtype = {conference}
}

@conference{Yang2023b,
title = {A Strategy-Oriented Bayesian Soft Actor-Critic Model},
author = {Qin Yang and Ramviyas Parasuraman},
url = {https://www.sciencedirect.com/science/article/pii/S1877050923006063},
doi = {10.1016/j.procs.2023.03.071},
year = {2023},
date = {2023-03-17},
booktitle = {Procedia Computer Science},
journal = {Procedia Computer Science},
volume = {220},
pages = {561-566},
publisher = {Elsevier},
series = {ANT 2023},
abstract = {Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy decomposition approach based on the Bayesian chain rule to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method – soft actor-critic (SAC) and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. We compare the proposed BSAC method with the SAC and other state-of-the-art approaches such as TD3, DDPG, and PPO on the standard continuous control benchmarks – Hopper-v2, Walker2d-v2, and Humanoid-v2 – in MuJoCo with the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency.},
keywords = {autonomy, learning},
pubstate = {published},
tppubtype = {conference}
}

@workshop{Munir2022,
title = {Sharing Autonomy of Exploration and Exploitation via Control Interface},
author = {Aiman Munir and Ramviyas Parasuraman},
url = {https://sites.google.com/view/saphri-icra2022/contributions},
year = {2022},
date = {2022-05-23},
booktitle = {ICRA 2022 Workshop on Shared Autonomy in Physical Human-Robot Interaction: Adaptability and Trust},
abstract = {Shared autonomy is a control paradigm that refers to the adaptation of a robot’s autonomy level in dynamic environments while taking human intentions and status into account at the same time. Here, the autonomy level can be changed based on internal/external information and human input. However, there are no clear guidelines and studies that help understand ”when” should a robot adapt its autonomy level to different functionalities. Therefore, in this paper, we create a framework that helps to improve the human-robot control interface by allowing humans to adapt to the robots’ autonomy level as well as to create a study design to gather insights into human’s preference to switch autonomy levels based on the current situation. We create two high-level strategies - Exploration to gather more data
and Exploitation to make use of current data - for a search and rescue task. These two strategies can be achieved with human inputs or autonomous algorithms. We intend to understand the human preferences to the autonomy levels (and ”when” they want to switch) to these two strategies. The analysis is expected to provide insights into designing shared autonomy schemes and algorithms to consider human preferences in adaptively using autonomy levels of certain high-level strategies.},
keywords = {autonomy, human-robot interface, trust},
pubstate = {published},
tppubtype = {workshop}
}