IDM
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ASH: Agents that Self-Hone via Embodied Learning
Announce Type: replace Abstract: Long-horizon embodied tasks remain a fundamental challenge in AI, as current methods rely on hand-engineered rewards or action-labeled demonstrations, neither of which scales. We introduce ASH, an agentic system that learns an embodied policy from unlabeled, noisy internet video, without reward shaping or expert annotation. ASH follows a self-improvement loop; when it gets stuck, ASH learns an Inverse Dynamics Model (IDM) from its own trajectories, and uses...
ASH: Agents that Self-Hone via Embodied Learning
Announce Type: replace Abstract: Long-horizon embodied tasks remain a fundamental challenge in AI, as current methods rely on hand-engineered rewards or action-labeled demonstrations, neither of which scales. We introduce ASH, an agentic system that learns an embodied policy from unlabeled, noisy internet video, without reward shaping or expert annotation. ASH follows a self-improvement loop; when it gets stuck, ASH learns an Inverse Dynamics Model (IDM) from its own trajectories, and uses...
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In the fall of 2006, I decided emo was out and IDM was in. Fueled by the hope of becoming the next Four Tet or Aphex Twin, I marched into my local Guitar Center and purchased an audio interface to convert my guitar and vocals into ones and zeroes, then mangle them in Ableton Live. When I got home, I plugged a brand-new M-Audio Fast Track Pro into my Windows desktop and immediately hit a brick wall of audio driver configuration hell.
When Does Predictive Inverse Dynamics Outperform Behavior Cloning?
arXiv:2601.21718v2 Announce Type: replace Abstract: Behavior cloning (BC) is a practical offline imitation learning method, but it often fails when expert demonstrations are limited. Recent works have introduced a class of architectures named predictive inverse dynamics models (PIDM) that combine a future state predictor with an inverse dynamics model. While PIDM often outperforms BC, the reasons behind its benefits remain unclear.
Latent Geometry Beyond Search: Amortizing Planning in World Models
arXiv:2605.08732v2 Announce Type: replace Abstract: Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these spaces remains challenging. This raises a central question: when does a learned representation simplify control, rather than merely enabling prediction? We study this question in a pretrained LeWorldModel, whose latent geometry is regularized for smoothness and uniformity.