A three-level hierarchical RL framework uses pose affordances to guide navigation and interaction-point affordances to guide pedipulation, enabling autonomous object manipulation by quadrupeds in simulation and real-world tests.
Hierarchical adaptive motion planning with nonlinear model predictive control for safety-critical collaborative loco-manipulation,
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Affordance-Based Hierarchical Reinforcement Learning for Quadruped Pedipulation
A three-level hierarchical RL framework uses pose affordances to guide navigation and interaction-point affordances to guide pedipulation, enabling autonomous object manipulation by quadrupeds in simulation and real-world tests.