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MIT creates method to force AI to comply with safety rules

MIT researchers published HardFlow, a method enforcing hard safety constraints on flow-matching generative models' final outputs without retraining.

MIT researchers led by Zeyang Li and Navid Azizan developed HardFlow, a trajectory-optimization method that enforces strict, non-negotiable constraints on flow-matching generative models by checking rule satisfaction only at the final generation step. Published in IEEE TPAMI, it outperformed six rival projection and guidance methods on four simulated benchmarks including D3IL robotic manipulation, Maze2D, physical process control, and image editing. All results are simulation-only, with no independent reproduction yet reported.

Hacker News · securitySep 14, 16:18 UTC · 2d agoAI research HN 24↑ · 25 comments

Reward AI Releases OM-1: A Robot Policy Trained on Human Demonstrations Only, With No Teleoperation or On-Robot Data

Reward AI released OM-1, a general-purpose manipulation policy trained solely on human demonstrations from a sensorized glove, with no teleoperation or robot data.

Reward AI announced OM-1 (Omnibody Model 1), a general-purpose robot manipulation policy trained only on human demonstrations captured via Omnibody Hand, a 7-DoF wearable glove with tactile, proximity, and in-hand camera sensing. The system uses electromagnetic hand-pose tracking, cutting mean overshoot error to 9.5 mm versus 24.9 mm for visual-inertial at 67 cm/s (a 60% reduction), and reportedly learns brand-new tasks from under 30 minutes of human data. A separate RL-trained control layer runs on its own clock so policy inference latency never stalls motion, and the policy spans industrial arms, legged humanoids, and wheeled mobile manipulators. No weights, code, dataset, API, paper, or benchmark comparisons have been released, so claims are demonstration-backed only.

MarkTechPostSep 14, 21:08 UTC · 2d agoAI research