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Register a new userDeep Neural Object Analysis by Interactive Auditory Exploration with a Humanoid Robot
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Added by
Manfred Eppe
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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We present a novel approach for interactive auditory object analysis with a humanoid robot. The robot elicits sensory information by physically shaking visually indistinguishable plastic capsules. It gathers the resulting audio signals from microphones that are embedded into the robotic ears. A neural network architecture learns from these signals to analyze properties of the contents of the containers. Specifically, we evaluate the material classification and weight prediction accuracy and demonstrate that the framework is fairly robust to acoustic real-world noise.
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In order to detect and correct physical exercises, a Grow-When-Required Network (GWR) with recurrent connections, episodic memory and a novel subnode mechanism is developed in order to learn spatiotemporal relationships of body movements and poses. Once an exercise is performed, the information of pose and movement per frame is stored in the GWR. For every frame, the current pose and motion pair is compared against a predicted output of the GWR, allowing for feedback not only on the pose but also on the velocity of the motion. In a practical scenario, a physical exercise is performed by an expert like a physiotherapist and then used as a reference for a humanoid robot like Pepper to give feedback on a patients execution of the same exercise. This approach, however, comes with two challenges. First, the distance from the humanoid robot and the position of the user in the cameras view of the humanoid robot have to be considered by the GWR as well, requiring a robustness against the users positioning in the field of view of the humanoid robot. Second, since both the pose and motion are dependent on the body measurements of the original performer, the experts exercise cannot be easily used as a reference. This paper tackles the first challenge by designing an architecture that allows for tolerances in translation and rotations regarding the center of the field of view. For the second challenge, we allow the GWR to grow online on incremental data. For evaluation, we created a novel exercise dataset with virtual avatars called the Virtual-Squat dataset. Overall, we claim that our novel architecture based on the GWR can use a learned exercise reference for different body variations through continual online learning, while preventing catastrophic forgetting, enabling for an engaging long-term human-robot interaction with a humanoid robot.
The mechanisms of infant development are far from understood. Learning about ones own body is likely a foundation for subsequent development. Here we look specifically at the problem of how spontaneous touches to the body in early infancy may give rise to first body models and bootstrap further development such as reaching competence. Unlike visually elicited reaching, reaching to own body requires connections of the tactile and motor space only, bypassing vision. Still, the problems of high dimensionality and redundancy of the motor system persist. In this work, we present an embodied computational model on a simulated humanoid robot with artificial sensitive skin on large areas of its body. The robot should autonomously develop the capacity to reach for every tactile sensor on its body. To do this efficiently, we employ the computational framework of intrinsic motivations and variants of goal babbling, as opposed to motor babbling, that prove to make the exploration process faster and alleviate the ill-posedness of learning inverse kinematics. Based on our results, we discuss the next steps in relation to infant studies: what information will be necessary to further ground this computational model in behavioral data.
In the current level of evolution of Soccer 3D, motion control is a key factor in teams performance. Recent works takes advantages of model-free approaches based on Machine Learning to exploit robot dynamics in order to obtain faster locomotion skills, achieving running policies and, therefore, opening a new research direction in the Soccer 3D environment. In this work, we present a methodology based on Deep Reinforcement Learning that learns running skills without any prior knowledge, using a neural network whose inputs are related to robots dynamics. Our results outperformed the previous state-of-the-art sprint velocity reported in Soccer 3D literature by a significant margin. It also demonstrated improvement in sample efficiency, being able to learn how to run in just few hours. We reported our results analyzing the training procedure and also evaluating the policies in terms of speed, reliability and human similarity. Finally, we presented key factors that lead us to improve previous results and shared some ideas for future work.
Picking objects in a narrow space such as shelf bins is an important task for humanoid to extract target object from environment. In those situations, however, there are many occlusions between the camera and objects, and this makes it difficult to segment the target object three dimensionally because of the lack of three dimentional sensor inputs. We address this problem with accumulating segmentation result with multiple camera angles, and generating voxel model of the target object. Our approach consists of two components: first is object probability prediction for input image with convolutional networks, and second is generating voxel grid map which is designed for object segmentation. We evaluated the method with the picking task experiment for target objects in narrow shelf bins. Our method generates dense 3D object segments even with occlusions, and the real robot successfuly picked target objects from the narrow space.
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, but also be aware that its human teammates expectation of itself. Being aware of the human teammates expectation leads to robot behaviors that better align with human expectation, thus facilitating more efficient and potentially safer teams. Our work addresses the problem of human-robot cooperation with the consideration of such teammate models in sequential domains by leveraging the concept of plan explicability. In plan explicability, however, the human is considered solely as an observer. In this paper, we extend plan explicability to consider interactive settings where human and robot behaviors can influence each other. We term this new measure as Interactive Plan Explicability. We compare the joint plan generated with the consideration of this measure using the fast forward planner (FF) with the plan created by FF without such consideration, as well as the plan created with actual human subjects. Results indicate that the explicability score of plans generated by our algorithm is comparable to the human plan, and better than the plan created by FF without considering the measure, implying that the plans created by our algorithms align better with expected joint plans of the human during execution. This can lead to more efficient collaboration in practice.
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