PACT: Self-Evolving Physical Safety Alignment for Diffusion Policies in Embodied Manipulation
arXiv:2606.08414v1 Announce Type: cross Abstract: Diffusion policies have achieved remarkable success in robotic manipulation, yet they often fail to satisfy strict physical constraints required for safe deployment. Existing approaches impose safety either prematurely during training or reactively via external guardrails at test time, limiting policy expressivity and overall scalability. We propose Physical safety Alignment for Constrained Trajectories (PACT), a self-evolving post-training framework that projects pretrained diffusion policies onto constraint-feasible regions without accessing
The rapid advancement of diffusion policies in robotic manipulation necessitates robust safety mechanisms to enable real-world deployment, leading to new research focusing on post-training safety alignment.
Ensuring physical safety is paramount for the widespread adoption and integration of robotic systems, particularly those using advanced AI, into industrial and public settings.
The ability to post-train safety into diffusion policies allows for greater policy expressivity while still meeting stringent safety requirements, potentially accelerating the deployment of complex AI-driven robots.
- · Robotics companies
- · AI safety researchers
- · Manufacturing sector
- · Companies with less sophisticated safety integration in robotics
- · Reactive safety systems
Increased reliability and trustworthiness of autonomous robotic systems in real-world applications.
Faster commercialization and broader adoption of AI-powered manipulation robots in sensitive environments.
Reduced barriers to entry for developing complex robotic tasks, fostering innovation in automation.
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Read at arXiv cs.AI