13,024 research outputs found
Robot control based on qualitative representation of human trajectories
A major challenge for future social robots is the high-level interpretation of human motion, and the consequent generation of appropriate robot actions. This paper describes some fundamental steps towards the real-time implementation of a system that allows a mobile robot to transform quantitative information about human trajectories (i.e. coordinates and speed) into qualitative concepts, and from these to generate appropriate control commands. The problem is formulated using a simple version of qualitative trajectory calculus, then solved using an inference engine based on fuzzy temporal logic and situation graph trees. Preliminary results are discussed and future directions of the current research are drawn
Qualitative design and implementation of human-robot spatial interactions
Despite the large number of navigation algorithms available for mobile robots, in many social contexts they often exhibit inopportune motion behaviours in proximity of people, often with very "unnatural" movements due to the execution of segmented trajectories or the sudden activation of safety mechanisms (e.g., for obstacle avoidance). We argue that the reason of the problem is not only the difficulty of modelling human behaviours and generating opportune robot control policies, but also the way human-robot spatial interactions are represented and implemented.
In this paper we propose a new methodology based on a qualitative representation of spatial interactions, which is both flexible and compact, adopting the well-defined and coherent formalization of Qualitative Trajectory Calculus (QTC). We show the potential of a QTC-based approach to abstract and design complex robot behaviours, where the desired robot's behaviour is represented together with its actual performance in one coherent approach, focusing on spatial interactions rather than pure navigation problems
Deep Predictive Policy Training using Reinforcement Learning
Skilled robot task learning is best implemented by predictive action policies
due to the inherent latency of sensorimotor processes. However, training such
predictive policies is challenging as it involves finding a trajectory of motor
activations for the full duration of the action. We propose a data-efficient
deep predictive policy training (DPPT) framework with a deep neural network
policy architecture which maps an image observation to a sequence of motor
activations. The architecture consists of three sub-networks referred to as the
perception, policy and behavior super-layers. The perception and behavior
super-layers force an abstraction of visual and motor data trained with
synthetic and simulated training samples, respectively. The policy super-layer
is a small sub-network with fewer parameters that maps data in-between the
abstracted manifolds. It is trained for each task using methods for policy
search reinforcement learning. We demonstrate the suitability of the proposed
architecture and learning framework by training predictive policies for skilled
object grasping and ball throwing on a PR2 robot. The effectiveness of the
method is illustrated by the fact that these tasks are trained using only about
180 real robot attempts with qualitative terminal rewards.Comment: This work is submitted to IEEE/RSJ International Conference on
Intelligent Robots and Systems 2017 (IROS2017
Social Attention: Modeling Attention in Human Crowds
Robots that navigate through human crowds need to be able to plan safe,
efficient, and human predictable trajectories. This is a particularly
challenging problem as it requires the robot to predict future human
trajectories within a crowd where everyone implicitly cooperates with each
other to avoid collisions. Previous approaches to human trajectory prediction
have modeled the interactions between humans as a function of proximity.
However, that is not necessarily true as some people in our immediate vicinity
moving in the same direction might not be as important as other people that are
further away, but that might collide with us in the future. In this work, we
propose Social Attention, a novel trajectory prediction model that captures the
relative importance of each person when navigating in the crowd, irrespective
of their proximity. We demonstrate the performance of our method against a
state-of-the-art approach on two publicly available crowd datasets and analyze
the trained attention model to gain a better understanding of which surrounding
agents humans attend to, when navigating in a crowd
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