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Institute of Automation, Chinese Academy of Sciences, D VAE for spatiotemporal latent encoding, whereas OpenSora typically relies on more sampling steps and a, Zhongguancun Academy
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Fine-tuning generative policies for robotics doesn't have to be a nightmare: POCO offers a stable and efficient RL framework that actually works on real robots.
Agentic RL agents can learn faster and perform better by dynamically maintaining a skill bank that combines high-level task guidance with low-level step-by-step decision support.
Ditch expensive, rendering-based RL for autonomous driving: PerlAD uses offline data to train agents in a fast, vector-space pseudo-simulation, outperforming prior methods by 10% on driving score.
Autonomous driving models can learn to avoid accidents *before* they happen by training on expert interventions and anticipating errors.
Achieve up to 28% better success rates in whole-body mobile manipulation by decoupling base and arm control while intelligently allocating perceptual attention.
Key contribution not extracted.
Imagine training robots to manipulate objects in the real world, but entirely within a high-fidelity, diffusion-based dream.
