arXiv:2609.23731v1 Announce Type: cross
Abstract: Robotic systems are typically composed of multiple independently developed modules that work together to perceive, predict, and act in the environmen...
By Rista Baral
arXiv:2608. 07751v1 Announce Type: cross Abstract: Safe and efficient robot navigation in crowds requires anticipating pedestrian motion despite uncertain and potentially shifting prediction errors.
By Cheng Guo, Mingzhe Ni, Zheng Liang, Yihu Ling, Yuan Hu, Michele Caprio, Daniele Pucci, Wei Pan
arXiv:2512.00939v3 Announce Type: replace-cross
Abstract: Recent progress in contact-rich robotic manipulation has been striking, yet most deployed systems remain confined to simple, scripted routine...
By Nayesha Gandotra, Itamar Mishani, Lai Yuan, Oren Salzman, Maxim Likhachev
arXiv:2604. 25897v2 Announce Type: replace-cross Abstract: Contact variability, sensing uncertainty, and external disturbances make grasp execution stochastic.
By Clinton Enwerem, Shreya Kalyanaraman, John S. Baras, Calin Belta
Planning contact-rich whole-arm manipulation is challenging because interactions that involve extended robot geometry give rise to complex contact dynamics that are difficult to model accurately. This creates a need for planning principles that do not rely heavily on precise contact models.
The paper presents a training-free diffusion-based motion planner that replaces learned global trajectory scores with analytical local scores derived from obstacle, smoothness, velocity, and inter-agent feasibility terms. By reconstructing trajectory scores through local interactions between neighboring waypoints and nearby constraints, the method decomposes the denoising process while preserving the optimization structure of classical trajectory methods. Experiments demonstrate that this approach generates smooth, feasible trajectories for large multi-agent tasks in complex environments quickly, outperforming learning-based and optimization baselines without requiring training data.
By Michael Y. Fatemi, Jinhao Liang, Ferdinando Fioretto