arXiv AI By Johor Jara Gonzalez, Matthew Guzdial

An Exploration of Collision-based Enemy Morphology Generation

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arXiv:2606. 02832v1 Announce Type: new Abstract: Despite a great deal of prior research into Procedural Content Generation (PCG), relatively little prior work has explored generating enemies for video games.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning
Jun 9

RAM: Reachability Across Morphologies

arXiv:2606. 09108v1 Announce Type: cross Abstract: Many stages of the robotic lifecycle, from morphology synthesis to operation, rely fundamentally on the reachable workspace.

By Tim Walter, Xinyu Chen, Jonathan K\"ulz, Matthias Althoff
arXiv AI
Sep 25

MorphIK: Morphology-Conditioned Neural Inverse Kinematics for Unknown Robots

MorphIK is a flow‑matching neural model that learns inverse kinematics for revolute‑joint kinematic chains it has never seen during training. Using a transformer to encode a robot’s morphology and target pose, the model generates pose solutions from noise and can be fine‑tuned with optimization to achieve sub‑centimeter accuracy. It also efficiently samples the robot’s null space, producing diverse configurations for the same pose.

By Lennart Clasmeier, Jan Gerrit Habekost, Cornelius Weber, Stefan Wermter
arXiv AI
Jul 28

Limbomorphs

arXiv:2607. 23842v1 Announce Type: cross Abstract: Artificial life systems are typically defined by a set of dynamical rules over an environment, an agent, or both, from which lifelike patterns may emerge.

By Alex Alvarez, Michael Levin
arXiv AI
Sep 25

RoboLDA: A Probabilistic Generative Model for Uncovering Embodied Hierarchical Structures in Voxel-based Soft Robots

RoboLDA is a Bayesian probabilistic model that learns a four‑level hierarchy—task, robot, organ, voxel—from existing high‑performing voxel‑based soft robot designs. By training with variational inference, it uncovers consistent, intuitive hierarchical patterns and can generate new robot morphologies that outperform evolutionary algorithms by an average of 106.4% without further optimization. The inferred organ structures also improve modular control policies, demonstrating the model’s utility for zero‑shot design and motion control.

By Junru Song, Yang Yang, Jingdan Shi, Guozhen Li, Weien Zhou, Ying Wen, Feifei Wang, Wen Yao, Tingsong Jiang