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When the human-robot interactions become ubiquitous, the environment surrounding these interactions will have significant impact on the safety and comfort of the human and the effectiveness and efficiency of the robot. Although most robots are designed to work in the spaces created for humans, many environments, such as living rooms and offices, can be and should be redesigned to enhance and improve human-robot collaboration and interactions. This work uses autonomous wheelchair as an example and investigates the computational design in the human-robot coexistence spaces. Given the room size and the objects $O$ in the room, the proposed framework computes the optimal layouts of $O$ that satisfy both human preferences and navigation constraints of the wheelchair. The key enabling technique is a motion planner that can efficiently evaluate hundreds of similar motion planning problems. Our implementation shows that the proposed framework can produce a design around three to five minutes on average comparing to 10 to 20 minutes without the proposed motion planner. Our results also show that the proposed method produces reasonable designs even for tight spaces and for users with different preferences.
Human input has enabled autonomous systems to improve their capabilities and achieve complex behaviors that are otherwise challenging to generate automatically. Recent work focuses on how robots can use such input - like demonstrations or corrections
Human-robot teaming is one of the most important applications of artificial intelligence in the fast-growing field of robotics. For effective teaming, a robot must not only maintain a behavioral model of its human teammates to project the team status
In this paper, we present an approach for robot learning of social affordance from human activity videos. We consider the problem in the context of human-robot interaction: Our approach learns structural representations of human-human (and human-obje
Teaching an anthropomorphic robot from human example offers the opportunity to impart humanlike qualities on its movement. In this work we present a reinforcement learning based method for teaching a real world bipedal robot to perform movements dire
The current dominant paradigm for robotic manipulation involves two separate stages: manipulator design and control. Because the robots morphology and how it can be controlled are intimately linked, joint optimization of design and control can signif