arXiv AI
Constant-Time Planning for Chaining Collision-free Motion to Manipulation Behaviors
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Jul 31
Task and Skill Planning: Hierarchical Robot Planning with Black-Box Skills
arXiv:2504. 17901v3 Announce Type: replace-cross Abstract: Task and motion planning (TAMP) is a well-established approach for solving long-horizon robot planning problems.
arXiv AI
Sep 18
Coding Agents with an Obstacle-Aware Harness for Safe Robot Manipulation
The paper introduces SafeHarness, an obstacle‑aware framework that improves the safety of coding agents for robot manipulation. By decomposing tasks into route planning and contact execution, the harness enables the agent to prioritize collision avoidance, achieving 71.9% task success and 87.5% collision avoidance—significantly better than prior methods. The study demonstrates that safety constraints can be effectively integrated into language‑model‑driven robot controllers.
arXiv AI
Jul 7
MOSAIC: Skill-Centric Manipulation Planning with Physics Simulation
arXiv:2504. 16738v3 Announce Type: replace-cross Abstract: Planning long-horizon manipulation motions using a set of predefined skills is a central challenge in robotics; solving it efficiently could enable general-purpose robots to tackle novel tasks by flexibly composing generic skills.
arXiv AI
Jul 2
Search-Based Spatiotemporal and Multi-Robot Motion Planning on Graphs of Space-Time Convex Sets
arXiv:2607. 00444v1 Announce Type: cross Abstract: Spatiotemporal motion planning, especially in multi-robot settings, requires robots to reason about collision-free regions that change over time, which is challenging in continuous spaces when feasible regions are transient and geometrically constrained.
arXiv AI
Jul 14
TACTIC: Tactile and Vision Conditioned Contact-Centric Control for Whole-Arm Manipulation
arXiv:2607. 09218v2 Announce Type: replace-cross Abstract: Whole-arm manipulation involves direct contact with the environment while the robot completes a task by distributing contact across multiple links as contacts form, slide, and break.
arXiv AI
Jul 20
SLAC: Safe and Efficient Real-Robot Reinforcement Learning via Unsupervised Simulation Pre-Training
arXiv:2506. 04147v5 Announce Type: replace-cross Abstract: Building capable household and industrial robots requires mastering the control of versatile, high-degree-of-freedom (DoF) systems such as mobile manipulators.