QDTraj uses Quality-Diversity algorithms with sparse rewards to produce at least five times more diverse high-performing trajectories for articulated object manipulation than compared methods, validated across 30 objects with hundreds of trajectories per task.
Adaptive Articulated Object Manipulation On The Fly with Foundation Model Reasoning and Part Grounding
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RelAfford6D constructs relational 6D affordance graphs from instructions, uses vision foundation models for metric poses, and executes via closed-loop kinematic constraint tracking to achieve claimed superior zero-shot generalization on articulated objects.
citing papers explorer
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QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation
QDTraj uses Quality-Diversity algorithms with sparse rewards to produce at least five times more diverse high-performing trajectories for articulated object manipulation than compared methods, validated across 30 objects with hundreds of trajectories per task.
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RelAfford6D: Relational 6D Affordance Graphs for Constraint-Driven Robotic Manipulation
RelAfford6D constructs relational 6D affordance graphs from instructions, uses vision foundation models for metric poses, and executes via closed-loop kinematic constraint tracking to achieve claimed superior zero-shot generalization on articulated objects.