Decision-Theoretic Planning with Person Trajectory Prediction for Social Navigation

Robots navigating in a social way should reason about people intentions when acting. For instance, in applications like robot guidance or meeting with a person, the robot has to consider the goals of the people. Intentions are inherently nonobservable, and thus we propose Partially Observable Markov Decision Processes (POMDPs) as a decision-making tool for these applications. One of the issues with POMDPs is that the prediction models are usually handcrafted. In this paper, we use machine learning techniques to build prediction models from observations. A novel technique is employed to discover points of interest (goals) in the environment, and a variant of Growing Hidden Markov Models (GHMMs) is used to learn the transition probabilities of the POMDP. The approach is applied to an autonomous telepresence robot

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Human Activity Recognition System Based-on Sequential Logic Circuits and Statistical Models

this research proposed the human activityrecognition system that described complete flow of processes fromlowest process (dealing with images) to highest process (recognizehuman activity). We proposed human action recognition thatmanage image sequence then recognize human action with simplehuman model by model-based recognition technique. Theexperimental result shows good accuracy which up to 93%correctly recognized. We proposed the human activity processwith 3 methods that consecutive improved. All of those methodscan use the result of action recognition as inputs. First method isFSM recognizer. The human model in Finite State Machine (FSM)recognizer can be modeled by rational condition that make it easyto understand and consume low computation cost but it hard todefine complex activity condition so it is unsuitable method forcomplex activity. The second recognizer applied Hidden MarkovModel (HMM) for activity modeling. The HMM recognizer candealing with much more complex activity and give fair recognitionrate. However, HMM recognizer is not involve feature prioritythat should has effect to accuracy so we proposed the thirdrecognizer that used graph similarity measurement for activitymodeling and activity classification. The third one, GraphSimilarity Measurement (GSM) recognizer involved featurepriority for recognition method then show better result thanHMM in most measurement. GSM recognizer has ~84% accuracyin average. FSM recognizer is suitable for simple activity with lowcomputation cost while HMM is suitable for much more complexactivity and use single feature for recognition process. However,HMM method may not give best result for the activity that usemultiple features. GSM is also suitable for complex activity and,furthermore, give better result than HMM for the activity thattrained from multiple features

Discovery and recognition of motion primitives in human activities

We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the `motion flux', a quantity which captures the motion variation of a group of skeletal joints. A normalization of the primitives is proposed in order to make them invariant with respect to a subject anatomical variations and data sampling rate. The discovered primitives are unknown and unlabeled and are unsupervisedly collected into classes via a hierarchical non-parametric Bayes mixture model. Once classes are determined and labeled they are further analyzed for establishing models for recognizing discovered primitives. Each primitive model is defined by a set of learned parameters. Given new video data and given the estimated pose of the subject appearing on the video, the motion is segmented into primitives, which are recognized with a probability given according to the parameters of the learned models. Using our framework we build a publicly available dataset of human motion primitives, using sequences taken from well-known motion capture datasets. We expect that our framework, by providing an objective way for discovering and categorizing human motion, will be a useful tool in numerous research fields including video analysis, human inspired motion generation, learning by demonstration, intuitive human-robot interaction, and human behavior analysis