Learning Action Priors for Cross-embodiment Robot Manipulation
Most Vision-Language-Action (VLA) models build on a Vision-Language Model (VLM) backbone by attaching an action module and optimizing the full policy jointly. This design inherits strong visual and linguistic priors from the VLM, but leaves the action module to learn physical motion almost from scratch. As a result, the policy lacks an explicit motion prior, forcing early optimization to simultaneously discover temporal action dynamics and cross-modal alignment, a challenge further amplified in
Record details
Published: 24 June 2026
Source: arXiv
Category: Research
Topics: Regulation · Safety & alignment · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (24 June 2026), “Learning Action Priors for Cross-embodiment Robot Manipulation,” evidence record 602, https://ethics.ai/record/602 (originally published by arXiv).
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