73 research outputs found
Human-Inspired Multi-Agent Navigation using Knowledge Distillation
Despite significant advancements in the field of multi-agent navigation,
agents still lack the sophistication and intelligence that humans exhibit in
multi-agent settings. In this paper, we propose a framework for learning a
human-like general collision avoidance policy for agent-agent interactions in
fully decentralized, multi-agent environments. Our approach uses knowledge
distillation with reinforcement learning to shape the reward function based on
expert policies extracted from human trajectory demonstrations through behavior
cloning. We show that agents trained with our approach can take human-like
trajectories in collision avoidance and goal-directed steering tasks not
provided by the demonstrations, outperforming the experts as well as
learning-based agents trained without knowledge distillation
SocialVAE: Human Trajectory Prediction using Timewise Latents
Predicting pedestrian movement is critical for human behavior analysis and
also for safe and efficient human-agent interactions. However, despite
significant advancements, it is still challenging for existing approaches to
capture the uncertainty and multimodality of human navigation decision making.
In this paper, we propose SocialVAE, a novel approach for human trajectory
prediction. The core of SocialVAE is a timewise variational autoencoder
architecture that exploits stochastic recurrent neural networks to perform
prediction, combined with a social attention mechanism and backward posterior
approximation to allow for better extraction of pedestrian navigation
strategies. We show that SocialVAE improves current state-of-the-art
performance on several pedestrian trajectory prediction benchmarks, including
the ETH/UCY benchmark, the Stanford Drone Dataset and SportVU NBA movement
dataset. Code is available at: https://github.com/xupei0610/SocialVAE
AdaptNet: Policy Adaptation for Physics-Based Character Control
Motivated by humans' ability to adapt skills in the learning of new ones,
this paper presents AdaptNet, an approach for modifying the latent space of
existing policies to allow new behaviors to be quickly learned from like tasks
in comparison to learning from scratch. Building on top of a given
reinforcement learning controller, AdaptNet uses a two-tier hierarchy that
augments the original state embedding to support modest changes in a behavior
and further modifies the policy network layers to make more substantive
changes. The technique is shown to be effective for adapting existing
physics-based controllers to a wide range of new styles for locomotion, new
task targets, changes in character morphology and extensive changes in
environment. Furthermore, it exhibits significant increase in learning
efficiency, as indicated by greatly reduced training times when compared to
training from scratch or using other approaches that modify existing policies.
Code is available at https://motion-lab.github.io/AdaptNet.Comment: SIGGRAPH Asia 2023. Video: https://youtu.be/WxmJSCNFb28. Website:
https://motion-lab.github.io/AdaptNet, https://pei-xu.github.io/AdaptNe
Dense pedestrian crowds versus granular packings: An analogy of sorts
Analogies between the dynamics of pedestrian crowds and granular media have
long been hinted at.They seem all the more promising as the crowd is (very)
dense, in which case the mechanical constraints prohibiting overlapsmight
prevail over the decisional component of pedestrian dynamics. These analogies
and their origins are probed in two distinct settings, (i) a flow through a
narrow bottleneck and (ii) crossing of a static assembly by an intruder.
Several quantitative similarities have been reported for the former setting and
are discussed here, while setting (ii) reveals discrepancies in the response
pattern, which areascribed to the pedestrians' ability to perceive, anticipate
and self-propel
Informative scene decomposition for crowd analysis, comparison and simulation guidance
Crowd simulation is a central topic in several fields including graphics. To achieve high-fidelity simulations, data has been increasingly relied upon for analysis and simulation guidance. However, the information in real-world data is often noisy, mixed and unstructured, making it difficult for effective analysis, therefore has not been fully utilized. With the fast-growing volume of crowd data, such a bottleneck needs to be addressed. In this paper, we propose a new framework which comprehensively tackles this problem. It centers at an unsupervised method for analysis. The method takes as input raw and noisy data with highly mixed multi-dimensional (space, time and dynamics) information, and automatically structure it by learning the correlations among these dimensions. The dimensions together with their correlations fully describe the scene semantics which consists of recurring activity patterns in a scene, manifested as space flows with temporal and dynamics profiles. The effectiveness and robustness of the analysis have been tested on datasets with great variations in volume, duration, environment and crowd dynamics. Based on the analysis, new methods for data visualization, simulation evaluation and simulation guidance are also proposed. Together, our framework establishes a highly automated pipeline from raw data to crowd analysis, comparison and simulation guidance. Extensive experiments and evaluations have been conducted to show the flexibility, versatility and intuitiveness of our framework
Modelling social identification and helping in evacuation simulation
Social scientists have criticised computer models of pedestrian streams for their treatment of psychological crowds as mere aggregations of individuals. Indeed most models for evacuation dynamics use analogies from physics where pedestrians are considered as particles. Although this ensures that the results of the simulation match important physical phenomena, such as the deceleration of the crowd with increasing density, social phenomena such as group processes are ignored. In particular, people in a crowd have social identities and share those social identities with the others in the crowd. The process of self categorisation determines norms within the crowd and influences how people will behave in evacuation situations. We formulate the application of social identity in pedestrian simulation algorithmically. The goal is to examine whether it is possible to carry over the psychological model to computer models of pedestrian motion so that simulation results correspond to observations from crowd psychology. That is, we quantify and formalise empirical research on and verbal descriptions of the effect of group identity on behaviour. We use uncertainty quantification to analyse the model’s behaviour when we vary crucial model parameters. In this first approach we restrict ourselves to a specific scenario that was thoroughly investigated by crowd psychologists and where some quantitative data is available: the bombing and subsequent evacuation of a London underground tube carriage on July 7th 2005
Data Driven Crowd Motion Control with Multi-touch Gestures
Controlling a crowd using multi‐touch devices appeals to the computer games and animation industries, as such devices provide a high‐dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre‐defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data‐driven gesture‐based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model. Given a run‐time gesture, our system extracts the K nearest gestures from the database and interpolates the corresponding crowd motions in order to generate the run‐time control. Our system is accurate and efficient, making it suitable for real‐time applications such as real‐time strategy games and interactive animation controls
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