187,297 research outputs found
Perceptual Perspective Taking and Action Recognition
Robots that operate in social environments need to be able to recognise and understand the actions of other robots, and humans, in order to facilitate learning through imitation and collaboration. The success of the simulation theory approach to action recognition and imitation relies on the ability to take the perspective of other people, so as to generate simulated actions from their point of view. In this paper, simulation of visual perception is used to re-create the visual egocentric sensory space and egocentric behaviour space of an observed agent, and through this increase the accuracy of action recognition. To demonstrate the approach, experiments are performed with a robot attributing perceptions to and recognising the actions of a second robot
TrackAgent: 6D Object Tracking via Reinforcement Learning
Tracking an object's 6D pose, while either the object itself or the observing
camera is moving, is important for many robotics and augmented reality
applications. While exploiting temporal priors eases this problem,
object-specific knowledge is required to recover when tracking is lost. Under
the tight time constraints of the tracking task, RGB(D)-based methods are often
conceptionally complex or rely on heuristic motion models. In comparison, we
propose to simplify object tracking to a reinforced point cloud (depth only)
alignment task. This allows us to train a streamlined approach from scratch
with limited amounts of sparse 3D point clouds, compared to the large datasets
of diverse RGBD sequences required in previous works. We incorporate temporal
frame-to-frame registration with object-based recovery by frame-to-model
refinement using a reinforcement learning (RL) agent that jointly solves for
both objectives. We also show that the RL agent's uncertainty and a
rendering-based mask propagation are effective reinitialization triggers.Comment: International Conference on Computer Vision Systems (ICVS) 202
Don't Believe Everything You Hear : Preserving Relevant Information by Discarding Social Information
Integrating information gained by observing others via Social Bayesian Learning can be beneficial for an agent’s performance, but can also enable population wide information cascades that perpetuate false beliefs through the agent population. We show how agents can influence the observation network by changing their probability of observing others, and demonstrate the existence of a population-wide equilibrium, where the advantages and disadvantages of the Social Bayesian update are balanced. We also use the formalism of relevant information to illustrate how negative information cascades are characterized by processing increasing amounts of non-relevant informatio
Learning and Acting in Peripersonal Space: Moving, Reaching, and Grasping
The young infant explores its body, its sensorimotor system, and the
immediately accessible parts of its environment, over the course of a few
months creating a model of peripersonal space useful for reaching and grasping
objects around it. Drawing on constraints from the empirical literature on
infant behavior, we present a preliminary computational model of this learning
process, implemented and evaluated on a physical robot. The learning agent
explores the relationship between the configuration space of the arm, sensing
joint angles through proprioception, and its visual perceptions of the hand and
grippers. The resulting knowledge is represented as the peripersonal space
(PPS) graph, where nodes represent states of the arm, edges represent safe
movements, and paths represent safe trajectories from one pose to another. In
our model, the learning process is driven by intrinsic motivation. When
repeatedly performing an action, the agent learns the typical result, but also
detects unusual outcomes, and is motivated to learn how to make those unusual
results reliable. Arm motions typically leave the static background unchanged,
but occasionally bump an object, changing its static position. The reach action
is learned as a reliable way to bump and move an object in the environment.
Similarly, once a reliable reach action is learned, it typically makes a
quasi-static change in the environment, moving an object from one static
position to another. The unusual outcome is that the object is accidentally
grasped (thanks to the innate Palmar reflex), and thereafter moves dynamically
with the hand. Learning to make grasps reliable is more complex than for
reaches, but we demonstrate significant progress. Our current results are steps
toward autonomous sensorimotor learning of motion, reaching, and grasping in
peripersonal space, based on unguided exploration and intrinsic motivation.Comment: 35 pages, 13 figure
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