Socially Aware Motion Planning with Deep Reinforcement Learning

For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.Comment: 8 page

Dynamical strategies for obstacle avoidance during Dictyostelium discoideum aggregation: a Multi-agent system model

Chemotaxis, the movement of an organism in response to chemical stimuli, is a typical feature of many microbiological systems. In particular, the social amoeba \textit{Disctyostelium discoideum} is widely used as a model organism, but it is not still clear how it behaves in heterogeneous environments. A few models focusing on mechanical features have already addressed the question; however, we suggest that phenomenological models focusing on the population dynamics may provide new meaningful data. Consequently, by means of a specific Multi-agent system model, we study the dynamical features emerging from complex social interactions among individuals belonging to amoeba colonies.\\ After defining an appropriate metric to quantitatively estimate the gathering process, we find that: a) obstacles play the role of local topological perturbation, as they alter the flux of chemical signals; b) physical obstacles (blocking the cellular motion and the chemical flux) and purely chemical obstacles (only interfering with chemical flux) elicit similar dynamical behaviors; c) a minimal program for robustly gathering simulated cells does not involve mechanisms for obstacle sensing and avoidance; d) fluctuations of the dynamics concur in preventing multiple stable clusters. Comparing those findings with previous results, we speculate about the fact that chemotactic cells can avoid obstacles by simply following the altered chemical gradient. Social interactions are sufficient to guarantee the aggregation of the whole colony past numerous obstacles

Modelling shared space users via rule-based social force model

The promotion of space sharing in order to raise the quality of community living and safety of street surroundings is increasingly accepted feature of modern urban design. In this context, the development of a shared space simulation tool is essential in helping determine whether particular shared space schemes are suitable alternatives to traditional street layouts. A simulation tool that enables urban designers to visualise pedestrians and cars trajectories, extract flow and density relation in a new shared space design and achieve solutions for optimal design features before implementation. This paper presents a three-layered microscopic mathematical model which is capable of representing the behaviour of pedestrians and vehicles in shared space layouts and it is implemented in a traffic simulation tool. The top layer calculates route maps based on static obstacles in the environment. It plans the shortest path towards agents' respective destinations by generating one or more intermediate targets. In the second layer, the Social Force Model (SFM) is modified and extended for mixed traffic to produce feasible trajectories. Since vehicle movements are not as flexible as pedestrian movements, velocity angle constraints are included for vehicles. The conflicts described in the third layer are resolved by rule-based constraints for shared space users. An optimisation algorithm is applied to determine the interaction parameters of the force-based model for shared space users using empirical data. This new three-layer microscopic model can be used to simulate shared space environments and assess, for example, new street designs

Spinal Test Suites for Software Product Lines

A major challenge in testing software product lines is efficiency. In particular, testing a product line should take less effort than testing each and every product individually. We address this issue in the context of input-output conformance testing, which is a formal theory of model-based testing. We extend the notion of conformance testing on input-output featured transition systems with the novel concept of spinal test suites. We show how this concept dispenses with retesting the common behavior among different, but similar, products of a software product line.Comment: In Proceedings MBT 2014, arXiv:1403.704

A Neural Model of Visually Guided Steering, Obstacle Avoidance, and Route Selection

A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discotinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and VIP can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.Air Force Office of Scientific Research (F4960-01-1-0397); National Geospatial-Intelligence Agency (NMA201-01-1-2016); National Science Foundation (NSF SBE-0354378); Office of Naval Research (N00014-01-1-0624

Learning and memory in machines and animals : an AI model that accounts for some neurobiological data

The CEL model of learning and memory (Components of Episodic Learning) [Granger 1982, 1983a, 1983b] provides a process model of certain aspects of learning and memory in animals and humans. The model consists of a set of asynchronous and semi-independent functional operators that collectively create and modify memory traces as a result of experience. The model conforms to relevant results in the learning literature of psychology and neurobiology. There are two goals to this work: one is to create a set of working learning systems that will improve their performance on the basis of experience, and the other is to compare these systems' performance with that of living systems, as a step towards the eventual comparative characterizations of different learning systems.Parts of the model have been implemented in the CEL-0 program, which operates in a 'Maze-World' simulated maze environment. The program exhibits simple exploratory behavior that leads to the acquisition of predictive and discriminatory schemata. A number of interesting theoretical predictions have arisen in part from observation of the operation of the program, some of which are currently being tested in neurobiological experiments. In particular, some neurobiological evidence for the existence of multiple, seperable memory systems in humans and animals is interpreted in terms of the model, and some new experiments are suggested arising from the model's predictions

A Neural Model of Visually Guided Steering, Obstacle Avoidance, and Route Selection

A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discontinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and posterior parietal cortex can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.Air Force Office of Scientific Research (F4960-01-1-0397); National Geospatial-Intelligence Agency (NMA201-01-1-2016); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624