TL;DR
This work investigates whether Cosmos Policy can serve as a teacher to train IL and RL policies on specialized tasks, which would remove all human intervention from the loop and simplify robotics deployment.
Desc descriptive evidence is provided that long-horizon multi-tool post-training can change ways of working that transfer beyond its training domain, and both software-engineering benchmarks improve despite the training collection containing no software-engineering tasks.
Despite rapid advances in artificial intelligence, robotic systems remain limited by poor generalisation across unstructured environments and fragile training pipelines. Reinforcement learning (RL) has shown promise in training robotics, yet its effectiveness is often constrained by manually engineered reward mechanism...
This work proposes QWM, a framework that leverages world models to perform test-time search over imagined trajectories on top of Q-learning to select high-value actions during both online rollouts and evaluation, and significantly outperforms strong prior state-of-the-art methods on both sample efficiency and performan...
Visually-Grounded Reward Synthesis (VGRS), which uses slow foundation models during training to produce fast robotic control policies, is proposed and achieves success rates above 55% on challenging long-horizon tasks while deploying successfully to real robots.
A unified framework that combines centralized training with decentralized execution (CTDE) and a Hybrid Reward Architecture (HRA) is introduced that enables multiple actors to share a centralized multi-head critic and substantially improves both sample efficiency and policy performance.
Overall, the findings indicate that VLA offers practical advantages for changing tasks by reducing programming effort and enabling useful performance with minimal data, while DQN provides a viable baseline when sufficient tuning effort is acceptable.
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