ReKep encodes robotic tasks as optimizable Python functions over 3D keypoints that are generated automatically from language and RGB-D input, enabling real-time hierarchical planning on single- and dual-arm platforms without task-specific data.
arXiv preprint arXiv:2309.16118 (2023)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 2roles
background 1polarities
background 1representative citing papers
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
-
ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation
ReKep encodes robotic tasks as optimizable Python functions over 3D keypoints that are generated automatically from language and RGB-D input, enabling real-time hierarchical planning on single- and dual-arm platforms without task-specific data.
-
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.