arXiv:2511.14427v4 Announce Type: replace-cross Abstract: Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception. However, Reinforcement Learning agents struggle to learn in such multisensory settings, especially amidst sensory noise and dynamic changes. We propose MultiSensory Dynamic Pretraining (MSDP), a novel framework for learning expressive multisensory representations tailored for task-oriented policy learning. MSDP is based on masked autoencoding and trains a transformer-based encoder by reconstructing multisensory observations
The increasing complexity of robotic tasks and the drive towards autonomous systems necessitate more robust and adaptable learning frameworks for contact-rich manipulation.
This research addresses a critical bottleneck in robotics by enabling more effective learning in complex, multisensory environments, which is essential for advancing general-purpose robot capabilities.
The ability of robots to learn and operate effectively in real-world, contact-rich scenarios with sensory noise will be significantly enhanced, paving the way for more dexterous and adaptable robotic systems.
- · Robotics companies
- · AI research labs
- · Manufacturing sector
- · Logistics and supply chain
- · Companies relying on manual labor for complex manipulation tasks
- · Robotics approaches lacking multisensory integration
Improved robot dexterity and adaptability in contact-rich tasks.
Accelerated development and deployment of humanoid and industrial robots for complex physical interactions.
Reduced operational costs and increased automation in sectors requiring fine manipulation, potentially impacting labor markets in new ways.
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