Recently, much attention has been devoted to the development
of generative network traces and their potential use in
supplementing real-world data for a variety of data-driven networking
tasks. Yet, the utility of existing synthetic traffic approaches
are limited by their low fidelity: low feature granularity,
insufficient adherence to task constraints, and subpar class
coverage. As effective network tasks are increasingly reliant
on raw packet captures, we advocate for a paradigm shift from
coarse-grained to fine-grained traffic generation compliant
to constraints. We explore this path employing controllable
diffusion-based methods. Our preliminary results suggest its
effectiveness in generating realistic and fine-grained network
traces that mirror the complexity and variety of real network
traffic required for accurate service recognition. We further
outline the challenges and opportunities of this approach, and
discuss a research agenda towards text-to-traffic synthesis.
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Deep Coverage: Motion Synthesis In The Data-Driven Era
Effective robotic systems must be able to produce desired motion in a sufficiently broad variety of robot states and environmental contexts. Classic control and planning methods achieve such coverage through the synthesis of model-based components. New applications and platforms, such as soft robots, present novel challenges, ranging from richer dynamical behaviors to increasingly unstructured environments. In these setups, derived models frequently fail to express important real-world subtleties. An increasingly popular approach to deal with this issue corresponds to end-to-end machine learning architectures, which adapt to such complexities through a data-driven process. Unfortunately, however, data are not always available for all regions of the operational space, which complicates the extensibility of these solutions. In light of these issues, this paper proposes a reconciliation of classic motion synthesis with modern data-driven tools towards the objective of ``deep coverage''. This notion utilizes the concept of composability, a feature of traditional control and planning methods, over data-derived ``motion elements'', towards generalizable and scalable solutions that adapt to real-world experience.
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- NSF-PAR ID:
- 10086341
- Date Published:
- Journal Name:
- International Symposium on Robotics Research (ISRR)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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