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ODEWorld achieves high-quality long-horizon predictions and planning-oriented dynamics by seamlessly integrating continuous-time modeling with ODE-based representations.
Action QFormer boosts navigation success rates from 18.8% to 56.3% by intelligently reorganizing multimodal information under action supervision.
Synthesizing 48,000 interaction trajectories without human input enables a humanoid robot to learn complex loco-manipulation tasks effectively.
Steering worst-case trajectories with an adversarial network and Boltzmann reweighting dynamics ensembles yields a surprisingly stable and efficient approach to robust RL under dynamics uncertainty.
Achieve natural and dynamically feasible humanoid robot motion by retargeting human motion data with a skeleton-aligned approach, significantly reducing inverse kinematics error compared to task-space methods.
