Kishan Chandan

AAMAS Conference 2022 Conference Paper

Augmented Reality Visualizations using Imitation Learning for Collaborative Warehouse Robots

• Kishan Chandan
• Jack Albertson
• Shiqi Zhang

Augmented reality (AR) technologies have been applied to humanrobot collaboration (HRC) domains to enable people to visualize the state of the robots. Current AR-based visualization strategies are manually designed. This design process requires a lot of human efforts, and domain knowledge. When too little information is visualized, human users find the AR interface not useful; when too much is visualized, they find it difficult to process the visualized information. In this paper, we develop an intelligent AR agent that learns visualization policies (what to visualize, when, and how) from demonstrations. We developed a Unity-based platform for simulating warehouse environments where human-robot teammates work on collaborative delivery tasks. We have collected a dataset that includes 6000 demonstrations of visualizing robots’ current and planned behaviors. Our results from experiments with real human participants show that, compared with competitive baselines from the literature, our learned visualization strategy significantly increases the efficiency of human-robot teams in delivery tasks.

ICRA Conference 2021 Conference Paper

ARROCH: Augmented Reality for Robots Collaborating with a Human

• Kishan Chandan
• Vidisha Kudalkar
• Xiang Li 0102
• Shiqi Zhang 0001

Human-robot collaboration frequently requires extensive communication, e. g. , using natural language and gesture. Augmented reality (AR) has provided an alternative way of bridging the communication gap between robots and people. However, most current AR-based human-robot communication methods are unidirectional, focusing on how the human adapts to robot behaviors, and are limited to single-robot domains. In this paper, we develop AR for Robots Collaborating with a Human (ARROCH), a novel algorithm and system that supports bidirectional, multi-turn, human-multi-robot communication in indoor multi-room environments. The human can see through obstacles to observe the robots’ current states and intentions, and provide feedback, while the robots’ behaviors are then adjusted toward human-multi-robot teamwork. Experiments have been conducted with real robots and human participants using collaborative delivery tasks. Results show that ARROCH outperformed a standard non-AR approach in both user experience and teamwork efficiency. In addition, we have developed a novel simulation environment using Unity (for AR and human simulation) and Gazebo (for robot simulation). Results in simulation demonstrate ARROCH’s superiority over AR-based baselines in human-robot collaboration.

ICAPS Conference 2021 Conference Paper

Guiding Robot Exploration in Reinforcement Learning via Automated Planning

• Yohei Hayamizu
• Saeid Amiri
• Kishan Chandan
• Keiki Takadama
• Shiqi Zhang 0001

Reinforcement learning (RL) enables an agent to learn from trial-and-error experiences toward achieving long-term goals; automated planning aims to compute plans for accomplishing tasks using action knowledge. Despite their shared goal of completing complex tasks, the development of RL and automated planning has been largely isolated due to their different computational modalities. Focusing on improving RL agents' learning efficiency, we develop Guided Dyna-Q (GDQ) to enable RL agents to reason with action knowledge to avoid exploring less-relevant states. The action knowledge is used for generating artificial experiences from an optimistic simulation. GDQ has been evaluated in simulation and using a mobile robot conducting navigation tasks in a multi-room office environment. Compared with competitive baselines, GDQ significantly reduces the effort in exploration while improving the quality of learned policies.

PRL Workshop 2021 Workshop Paper

Guiding Robot Exploration in Reinforcement Learning via Automated Planning: an Abstract

• Yohei Hayamizu
• Saeid Amiri
• Kishan Chandan
• Keiki Takadama
• Shiqi Zhang

IROS Conference 2021 Conference Paper

Learning to Guide Human Attention on Mobile Telepresence Robots with 360° Vision

• Kishan Chandan
• Jack Albertson
• Xiaohan Zhang 0002
• Xiaoyang Zhang
• Yao Liu 0001
• Shiqi Zhang 0001

Mobile telepresence robots (MTRs) allow people to navigate and interact with a remote environment that is in a place other than the person’s true location. Thanks to the recent advances in 360° vision, many MTRs are now equipped with an all-degree visual perception capability. However, people’s visual field horizontally spans only about 120° of the visual field captured by the robot. To bridge this observability gap toward human-MTR shared autonomy, we have developed a framework, called GHAL360, to enable the MTR to learn a goal-oriented policy from reinforcements for guiding human attention using visual indicators. Three telepresence environments were constructed using datasets that are extracted from Matterport3D and collected from a real robot respectively. Experimental results show that GHAL360 outperformed the baselines from the literature in the efficiency of a human-MTR team completing target search tasks. A demo video is available: https://youtu.be/aGbTxCGJSDM