arXiv:2606.03940v1 Announce Type: cross Abstract: In robotics systems, vast amounts of visual data are easily captured at high resolution using low-cost, low-power hardware. Yet, limited bandwidth and on-device compute resources prevent full utilization when transmitted via conventional codecs like JPEG/MPEG. Newer codecs, like AV1/AVIF, improve the rate-distortion trade-off, but demand far more resources for encoding, impractical without custom ASICs. Recent asymmetric autoencoders deliver high quality under extreme power and bandwidth constraints, but add prohibitive decoding cost and use be
The rapid deployment of robotics systems and increased emphasis on edge computing are driving the need for more efficient data compression and transmission solutions.
This research addresses a critical bottleneck in robotics and distributed AI: the efficient utilization of high-resolution sensor data under severe power and bandwidth constraints.
New autoencoding methods could enable richer, more capable robotic systems and AI agents by overcoming current data transmission and processing limitations on devices.
- · Robotics companies
- · Edge AI providers
- · Sensor manufacturers
- · Autonomous systems developers
- · Manufacturers of conventional codecs
- · Cloud-centric AI architectures
More sophisticated and data-rich sensor feedback becomes viable for on-device processing in robotics.
Reduced latency and improved autonomy for mobile robotic platforms operating in bandwidth-limited environments.
Proliferation of complex, general-purpose humanoid robots and AI agents due to improved on-device perception capabilities.
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Read at arXiv cs.LG