Navigation Around Humans Hey!! How you Do' in Importance, Approaches and the Future!!!
WHY IS IT IMPORTANT?? That’s Why
Papers Presented and Discussed 1. Adaptive Human aware Navigation based on Motion Pattern Analysis (Søren Tranberg Hansen, Mikael Svenstrup, Hans Do It Like a BOSS!!! Jørgen Andersen and Thomas Bak) 2. Socially-Aware Robot Navigation: A Learning Approach (Matthias Luber, Luciano Spinello, Jens Silva, Kai O. Arras)
Peek-a-boo Paper 1 Abstract • —Respecting people’s social spaces is an important prerequisite for acceptable and natural robot navigation in human environments. • In this paper, we describe an adaptive system for mobile robot navigation based on estimates of whether a person seeks to interact with the robot or not. The estimates are based on run- time motion pattern analysis compared to stored experience in a database. • Using a potential field centered around the person, the robot positions itself at the most appropriate place relative to the person and the interaction status. The system is validated through qualitative tests in a real world setting.
Components of the Paper 1. Introduction 2. Material and Methods 3. Experimental Setup 4. Results 5. Discussion 6. Conclusion
Umm… Hi! Introduction • Need to develop techniques that determine whether the user wants to interact with the robot or not in that current situation(example: robots supporting care assistants) • Evaluating and Predicting whether the user wants to interact with the robot based on the position and pose of the user. • Use the CBR(Case Based Reasoning) in real world environment scenario to determine whether the user would like to interact or not • CBR allows recalling and interpreting past experiences, as well as generating new cases to represent knowledge from new experiences
I told you!! Materials and Methods she was not interested. • Divide the Zone around People into four zones: 1. the public zone > 3.6m 2. the social zone > 1.2m 3. the personal zone > 0.45m 4. the intimate zone < 0.45m • If likely that the person does not seek to interact, then do not violate the personal zone and stay in the public or the social zone. • If likely that the person does seek to interact, then try to move into the personal zone.
YO bro!! You thinking? No bro!! Evaluating Materials and Methods(Continued ) • Evaluator used to predict this behaviour. • Philosophy of the Evaluator: a. human motion pattern is relative to the robot and the chances of an interaction b. Can be estimated based on the pose and position of the human and the corresponding stored interaction information for the same from the previous data. • Use this data to control the robot and set its objectives accordingly.
Materials and Methods(Still going on) • Evaluating Human robot encounters: 1. Variables: a. PI: fuzzy logic used to determine chances of interaction. (1=close interaction; 0=no close Interaction) b. X,Y: 2D coordinates of the human’s position in respect to the robot being the origin for the plane. c. Theta: angle of pose in respect to robot. d. W: weight assigned to PI according to proximity to the robot of the human(closer proximity to robot, more the weight).
Materials And Methods • Two Stages of Robot Interaction: 1. A person encounters the robot, and the robot evaluates the person given all the previous experiences from the database. 2. the robot updates the database according to the person encounter, which has just passed.
ZZZZZZZZ Materials and methods(Conclusion) • Algorithm I • if (Interested) then PI = PI + wL • if PI > 1 then PI = 1 • else if (Not Interested) then PI = PI - wL • if PI < 0 then PI = 0 • For modeling the robots navigation system, a person centered potential field is introduced. • The potential field is calculated by the weighted sum of four Gaussian distributions of which one is negated. The covariance of the distributions are used to adapt the potential field according to PI.
Materials And methods(Conclusion continued) • robot will navigate in such a way to reach the dark blue region and stay in it( Steepest Descent Approach). • The potential field has varying color zones according to the values of PI.
Experimental Setup • Evaluation of method performed through two experiments. • Experiment 1: a. the objective was to see if estimation of PI can be obtained based on interaction experience from different persons. b. A total of five test persons were asked to approach or pass the robot using different motion patterns c. The starting and end point of each trajectory were selected randomly, while the specific route was left to the own devices of the test person d. Random selection designed such that the cases with interaction were 50% and cases with no interaction also 50%. e. The output values (PI), the input values (position and pose), and the database were logged for later analysis.
He he he… Perfect alignment Experimental Setup • Experiment 2 a. Objective: test the adaptiveness of the method. The system should be able to change its estimation of PI over time for related behavior patterns. b. Total of 36 test approaches performed with one test person. c. The test person would start randomly from any three random positions and end his trajectory in fixed position. d. First 18 encounters, test person shows interest in interaction. Last 18 encounters, test person does not show interest. e. The output values (PI), and the input values (position and pose) were logged for later analysis.
Experimental Setup
Results • Experiment 1 a. The starting point of the CBR is an empty database. As the robot- human encounters take place, the database gets gradually filled up. b. We use 5 test persons for the database development and evaluation. c. We use 4D plots to display the results of our experiments. d. 2 dimensions for 2D position of the human with respect to the robot. e. 1 dimension to portray the direction or pose of the human. f. 1 dimension to depict the probability of interaction.
Results(Continued)
Results(conclusion) • Experiment 2 a. Objective of the experiment was to show that estimation of PI will adapt based on our observations. b. The values of PI in data are averages of PI for three areas.
Discussion • What Did we like about the Paper?? • What We didn’t Like?? • Where can we employ this approach?? • Any other Suggestions for the paper.
Paper 2 Abstract • The ability to act in a socially-aware way is a key skill for robots that share a space with humans. • In this paper we address the problem of socially-aware navigation among people that meets objective criteria such as travel time or path length as well as subjective criteria such as social comfort. • Opposed to model based approaches typically taken in related work, we pose the problem as an unsupervised learning problem. • We learn a set of dynamic motion prototypes from observations of relative motion behavior of humans found in publicly available surveillance data sets. • The learned motion prototypes are then used to compute dynamic cost maps for path planning using an any-angle A* algorithm.
Introduction • Research in the area of socially-aware navigation and manipulation is typically taking a model based approach, either with manually designed models or models from social psychology and cognitive science • However, there is a methodological gap as all these models have been tested or evaluated in controlled environments. • The people who wrote the paper believe, that the socially aware behaviour of the system should be learned from real world data. • the authors address the problem of learning a planning strategy through streams of pedestrians rather than learning continuous sensory- motor motions. • we take annotated surveillance data sets collected from overhead cameras, extract the pedestrian paths, and transform them into a 3D representation
Components of the Paper • Learning Relative Motion Prototypes • Planning with RMPs • Experiments • Conclusion
Learning Relative Motion Prototypes • In this section we present the theory for learning socially-aware relative motion prototypes (RMP). • Because of various complexities involved in handling situations with multiple humans, we break down the problem into pairwise evaluations. • A relative motion prototype Ri,j describes a relative motion between person π i and person π j. • Given the two observation sequences zi and zj of their (x,y)-positions over time t, we define • di,j(t) = MOD (kzi(t) − zj(t)k) • Ri,j = [di,j(ts), ... ,di,j(te)] • Ri,j = {di,j(ts),...,di,j(te)}
Learning Relative Motion Prototypes • Next step is to cluster the results. • As relative movement sequences can di fg er in duration and relative speed, we have to define an appropriate distance function able to group similar motion behaviors into the same cluster • Cluster=Prototype • ADTW technique employed for the same • Extension of DTW • Used for matching sequences of words spoken at different speeds.
Learning Relative Motion Prototypes
Learning Relative Motion Prototypes • Results after first step are clusters that are meaningless as the goal and other variables that influence human motion are not considered yet. • One aspect that we use to distinguish between the various clusters is the angle of approach criterion.
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