Differently from animals, robots can record its experience correctly for long time. We propose a novel algorithm that runs a particle filter on the time sequence of the experience. It can be applied to some teach-and-replay tasks. In a task, the trainer controls a robot, and the robot records its sensor readings and its actions. We name the sequence of the record an episode, which is derived from the episodic memory of animals. After that, the robot executes the particle filter so as to find a similar situation with the current one from the episode. If the robot chooses the action taken in the similar situation, it can replay the taught behavior. We name this algorithm the particle filter on episode (PFoE). The robot with PFoE shows not only a simple replay of a behavior but also recovery motion from skids and interruption. In this paper, we evaluate the properties of PFoE with a small mobile robot.
PFoE handles a decision making problem as a hidden Markov model (HMM). In an HMM @cite_7 @cite_1 , a finite number of states are defined, and state transitions of the system are hidden from an observer. Instead, the observer can receive a sequence of observations. Many algorithms on HMMs have been proposed for pattern recognition problems @cite_1 . Techniques for HMMs are also utilized for motion recognition or motion generation of robots @cite_16 @cite_24 . They have focused their attention on recognition of sequences of motion. To make a robot move in the real world with the recognition result, feedback control as researched in the above teach-and-replay methods will be required. In other research fields, combinations of an HMM model and a particle filter can be seen @cite_23 @cite_12 . Their purposes are also recognition.
@cite_7 @cite_1 @cite_24 @cite_23 @cite_16 @cite_12
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Learning by imitation renders a means for more natural human-robot interaction and is potentially the primary form of teaching. Imitation provides a potential means of automatically programming complex systems without extensive trials. This paper presents a method of imitation learning based on mapping the demonstrator's motion patterns to the observer self-posture. The self-posture is acquired through observing and recognizing the mirror image of its own body posture while performing the action in front of a mirror. First, different kinds of behavioural actions are learned through motor babbling by randomly performing different actions. The actions are learned through a novel probabilistic model called Topological Gaussian Adaptive Resonance Hidden Markov Model. During learning, the observer also extracts visual features through optical flow from self-observation in a mirror image. Second, after learning, a visuo-motor association is developed through novel Topological Gaussian Adaptive Resonance Associative Memory. Finally, after learning the demonstrator performs a similar action in front of the robot and the he robot recalls the corresponding motor command from the memory. In this paper we present a new method to provide situation awareness via the automatic recognition of behaviour in video. In contrast to many other approaches, the presented method does not require many training exemplars. We introduce Probabilistic Behaviour Signatures to represent the goals of a person agent as sets of features. We do not assume temporal ordering of observed actions is necessary. Inference is performed using an extension of the Rao-Blackwellised Particle Filter. We validate our approach using simulated image trajectories which represent three high-level behaviours. We compare performance to a trained Hidden Markov Model Particle Filter (HMM PF) and show that our approach achieves 92 accuracy at video frame rate. Our method is also significantly more robust than the HMM PF in the presence of noise. This paper presents a novel method for learning object manipulation such as rotating an object or placing one object on another. In this method, motions are learned using reference-point-dependent probabilistic models, which can be used for the generation and recognition of motions. The method estimates (i) the reference point, (ii) the intrinsic coordinate system type, which is the type of coordinate system intrinsic to a motion, and (iii) the probabilistic model parameters of the motion that is considered in the intrinsic coordinate system. Motion trajectories are modeled by a hidden Markov model (HMM), and an HMM-based method using static and dynamic features is used for trajectory generation. The method was evaluated in physical experiments in terms of motion generation and recognition. In the experiments, users demonstrated the manipulation of puppets and toys so that the motions could be learned. A recognition accuracy of 90 was obtained for a test set of motions performed by three subjects. Furthe... Obtaining accurate hidden Markov model (HMM) state sequences is a research challenge to warrant good system performance in particle filter (PF) compensation for noisy speech recognition. Instead of using specific knowledge at the model and state levels which is hard to estimate, we pool model states into clusters as side information. Since each cluster encompasses more statistics when compared to the original HMM states, there is a higher possibility that the newly formed probability density function at the cluster level can cover the underlying speech variation to generate appropriate PF samples for feature compensation. Testing the proposed PF-based compensation scheme on the Aurora 2 connected digit recognition task, we achieve an error reduction of 12.15 from the best multi-condition trained models using this integrated PF-HMM framework to estimate the cluster-based HMM state sequence information.