nthropomimetic robots sense, behave, interact, and feel like humans. By this definition, they require human-like physical hardware and actuation but also brain-like control and sensing. The most self-evident realization to meet those requirements would be a human-like musculoskeletal robot with a brain-like neural controller. While both musculoskeletal robotic hardware and neural control software have existed for decades, a scalable approach that could be used to build and control an anthropomimetic human-scale robot has not yet been demonstrated. Combining Myorobotics, a framework for musculoskeletal robot development, with SpiNNaker, a neuromorphic computing platform, we present the proof of principle of a system that can scale to dozens of neurally controlled, physically compliant joints. At its core, it implements a closed-loop cerebellar model that provides real-time, low-level, neural control at minimal power consumption and maximal Musculoskeletal Robots
Abstract-Robots that interact with humans in everyday situations, need to be able to interpret the nonverbal social cues of their human interaction partners. We show that humans use body posture and head pose as social signals to initiate and terminate interaction when ordering drinks at a bar. For that, we record and analyze 108 interactions of humans interacting with a human bartender. Based on these findings, we train a Hidden Markov Model (HMM) using automatic body posture and head pose estimation. With this model, the bartender robot of the project JAMES can recognize typical social behaviors of human customers. Evaluation shows a recognition rate of 82.9 % for all implemented social behaviors and in particular a recognition rate of 91.2 % for bartender attention requests, which will allow the robot to interact with multiple humans in a robust and socially appropriate way.
Abstract-For intelligent robots to solve real-world tasks, they need to manipulate multiple objects, and perform diverse manipulation actions apart from rigid transfers, such as pushing and sliding. Planning these tasks requires discrete changes between actions, and continuous, collision-free paths that fulfill action-specific constraints. In this work, we propose a multi-modal path planner, named MOPL, which accepts generic definitions of primitive actions with different types of contact manifolds, and randomly spans its search trees through these subspaces. Our evaluation shows that this generic search technique allows MOPL to solve several challenging scenarios over different types of kinematics and tools with reasonable performance. Furthermore, we demonstrate MOPL by solving and executing plans in two real-world experimental setups.
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