arXiv:2605.20856v1 Announce Type: cross Abstract: Language-conditioned manipulation policies typically process instructions and observations through shared network parameters. This task-state entanglement provides a pathway for observation leakage -- networks learn scene-to-action shortcuts that bypass language grounding entirely. DISC eliminates this failure structurally. Rather than conditioning a universal policy on language, DISC uses a hypernetwork to generate the entire parameter set of a task-specific visuomotor policy from the instruction alone. The generated policy never directly acce
The proliferation of language models and rapid advances in robotic control architectures are converging, making novel approaches to instruction grounding critical for robust autonomous systems.
This research addresses a fundamental limitation in current language-conditioned manipulation policies, making AI agents more reliable and less susceptible to brittle, scene-specific shortcuts.
The method proposes a structural decoupling of instruction processing from state-conditioned control, fundamentally altering how AI agents could learn and generalize tasks in complex environments.
- · AI robotics research labs
- · Developers of embodied AI agents
- · Industries deploying complex autonomous manipulation systems
- · Developers relying solely on traditional end-to-end language-conditioned policie
More robust and generalizable AI policies for robotic manipulation will emerge.
This improved robustness will accelerate the deployment of AI-driven automation in unstructured environments.
The enhanced reliability of AI agents could lead to broader societal integration of robotics, impacting labor markets and human-robot interaction paradigms.
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Read at arXiv cs.LG