454 research outputs found
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
For humans, the process of grasping an object relies heavily on rich tactile
feedback. Most recent robotic grasping work, however, has been based only on
visual input, and thus cannot easily benefit from feedback after initiating
contact. In this paper, we investigate how a robot can learn to use tactile
information to iteratively and efficiently adjust its grasp. To this end, we
propose an end-to-end action-conditional model that learns regrasping policies
from raw visuo-tactile data. This model -- a deep, multimodal convolutional
network -- predicts the outcome of a candidate grasp adjustment, and then
executes a grasp by iteratively selecting the most promising actions. Our
approach requires neither calibration of the tactile sensors, nor any
analytical modeling of contact forces, thus reducing the engineering effort
required to obtain efficient grasping policies. We train our model with data
from about 6,450 grasping trials on a two-finger gripper equipped with GelSight
high-resolution tactile sensors on each finger. Across extensive experiments,
our approach outperforms a variety of baselines at (i) estimating grasp
adjustment outcomes, (ii) selecting efficient grasp adjustments for quick
grasping, and (iii) reducing the amount of force applied at the fingers, while
maintaining competitive performance. Finally, we study the choices made by our
model and show that it has successfully acquired useful and interpretable
grasping behaviors.Comment: 8 pages. Published on IEEE Robotics and Automation Letters (RAL).
Website: https://sites.google.com/view/more-than-a-feelin
Enactivism and Robotic Language Acquisition: A Report from the Frontier
In this article, I assess an existing language acquisition architecture, which was deployed in linguistically unconstrained human–robot interaction, together with experimental design decisions with regard to their enactivist credentials. Despite initial scepticism with respect to enactivism’s applicability to the social domain, the introduction of the notion of participatory sense-making in the more recent enactive literature extends the framework’s reach to encompass this domain. With some exceptions, both our architecture and form of experimentation appear to be largely compatible with enactivist tenets. I analyse the architecture and design decisions along the five enactivist core themes of autonomy, embodiment, emergence, sense-making, and experience, and discuss the role of affect due to its central role within our acquisition experiments. In conclusion, I join some enactivists in demanding that interaction is taken seriously as an irreducible and independent subject of scientific investigation, and go further by hypothesising its potential value to machine learning.Peer reviewedFinal Published versio
Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems
In this article, we propose a backpropagation-free approach to robotic
control through the neuro-cognitive computational framework of neural
generative coding (NGC), designing an agent built completely from powerful
predictive coding/processing circuits that facilitate dynamic, online learning
from sparse rewards, embodying the principles of planning-as-inference.
Concretely, we craft an adaptive agent system, which we call active predictive
coding (ActPC), that balances an internally-generated epistemic signal (meant
to encourage intelligent exploration) with an internally-generated instrumental
signal (meant to encourage goal-seeking behavior) to ultimately learn how to
control various simulated robotic systems as well as a complex robotic arm
using a realistic robotics simulator, i.e., the Surreal Robotics Suite, for the
block lifting task and can pick-and-place problems. Notably, our experimental
results demonstrate that our proposed ActPC agent performs well in the face of
sparse (extrinsic) reward signals and is competitive with or outperforms
several powerful backprop-based RL approaches.Comment: Contains appendix with pseudocode and additional detail
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