Flying over the Reality Gap: From Simulated to Real Indoor Airships

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Robot motor skills can be acquired by deep reinforcement learning as neural networks to reflect state-action mapping. The selection of states has been demonstrated to be crucial for successful robot motor learning. However, because of the complexity of neu ...

As the field of ethology advances, especially over the past two decades, the role of animal-robot interaction tools has increasingly become essential. This importance arises from the need for controlled, repetitive, repeatable, and long-duration experiment ...

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We apply inverse reinforcement learning (IRL) with a novel cost feature to the problem of robot navigation in human crowds. Consistent with prior empirical work on pedestrian behavior, the feature anticipates collisions between agents. We efficiently learn ...

Fully exploiting soft robots' capabilities requires devising strategies that can accurately control their movements with the limited amount of control sources available. This task is challenging for reasons including the hard-to-model dynamics, the system' ...

Model-based reinforcement learning for robot control offers the advantages of overcoming concerns on data collection and iterative processes for policy improvement in model-free methods. However, both methods use exploration strategy relying on heuristics ...

Flying over the Reality Gap: From Simulated to Real Indoor Airships

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Identifying important sensory feedback for learning locomotion skills

Challenges and approaches in bridging the biomimicry gap in biohybrid systems of fish and robots

Avian-Inspired Claws Enable Robot Perching or Walking

Morphological flexibility in robotic systems through physical polygon meshing

Liquid Metal-Based Sensor Skin Enabling Haptic Perception of Softness

Computational Models of Mutual Understanding for Human-Robot Collaborative Learning

Towards Efficient Gas Leak Detection in Built Environments: Data-Driven Plume Modeling for Gas Sensing Robots

Self-Correcting Quadratic Programming-Based Robot Control

Haptics based collaborative steering control in automated driving environments

A biohybrid interaction framework for the integration of robots in animal societies

Inverse Reinforcement Learning of Pedestrian-Robot Coordination

A Provably Stable Iterative Learning Controller for Continuum Soft Robots

Exploration-based model learning with self-attention for risk-sensitive robot control