Introduces ST-GCS graphs and ECD for time-optimal single- and multi-robot motion planning via best-first graph search with continuous optimization and prioritized coordination.
and Tedrake, Russ , year =
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
roles
background 2representative citing papers
Ambient Diffusion Policy enables better imitation learning from suboptimal robot data by leveraging spectral properties to restrict data usage to specific diffusion times.
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.
Framework encodes key-door precedence logic in a layered augmented graph of convex sets for simultaneous optimal sequence selection and continuous trajectory computation with STL specs.
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
citing papers explorer
-
Search-Based Spatiotemporal and Multi-Robot Motion Planning on Graphs of Space-Time Convex Sets
Introduces ST-GCS graphs and ECD for time-optimal single- and multi-robot motion planning via best-first graph search with continuous optimization and prioritized coordination.
-
Ambient Diffusion Policy: Imitation Learning from Suboptimal Data in Robotics
Ambient Diffusion Policy enables better imitation learning from suboptimal robot data by leveraging spectral properties to restrict data usage to specific diffusion times.
-
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.
-
A Framework for Motion Planning with Temporal Logic Precedence Specifications via Augmented Graphs of Convex Sets
Framework encodes key-door precedence logic in a layered augmented graph of convex sets for simultaneous optimal sequence selection and continuous trajectory computation with STL specs.
-
Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.