Kishan Chandan's scientific contributions
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Publications (10)
In human-robot collaboration domains, augmented reality (AR) technologies have enabled people to visualize the state of robots. Current AR-based visualization policies are designed manually, which 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 m...
Robot planning in partially observable domains is difficult, because a robot needs to estimate the current state and plan actions at the same time. When the domain includes many objects, reasoning about the objects and their relationships makes robot planning even more difficult. In this letter, we develop an algorithm called
scene analysis for ro...
Robot planning in partially observable domains is difficult, because a robot needs to estimate the current state and plan actions at the same time. When the domain includes many objects, reasoning about the objects and their relationships makes robot planning even more difficult. In this paper, we develop an algorithm called scene analysis for robo...
Human-robot collaboration frequently requires extensive communication, e.g., using natural language and gestures. 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...
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 degree vision, many MTRs are now equipped with an all-degree visual perception capability. However, people's visual field horizontally spans only about 120 d...
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 d...
Model-based reinforcement learning (RL) enables an agent to learn world models from trial-and-error experiences toward achieving long-term goals. Automated planning, on the other hand, can be used for accomplishing tasks through reasoning with declarative action knowledge. Despite their shared goal of completing complex tasks, the development of RL...
Effective human-robot collaboration (HRC) requires extensive communication among the human and robot teammates, because their actions can potentially produce conflicts, synergies, or both. In this paper, we develop an augmented reality-driven, negotiation-based (ARN) framework for HRC, where ARN supports planning-phase negotiations within human-rob...
Citations
... Scene graph is a common and popular way to describe scene understanding, first proposed for image retrieval tasks to search for images with similar descriptions in image datasets [8]. In addition, scene graphs are used for a wide range of vision applications, such as image retrieval [8,16], image captioning [3], visual reasoning [17], visual answering [5], and robotics [1]. Among these previous works, the generation of scene graphs from images has seldom been discussed. ...
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... The experimental evaluation proposed in [90] suggests that the proposed system can reduce the time required by operators to recover from operational failures and safety violations. AR for Robots Collaborating with a Human (ARROCH) [86] is a system enabling communication between humans and multiple robots. With ARROCH, an operator can use a tablet to visualize the robots' current states and planned actions (intentions). ...
