The paper introduces MISCO, an evolutionary framework that uses deep generative models to design voxel-based soft robots (VSRs). MISCO combines an estimation-of-distribution algorithm with a variational autoencoder that includes multi-task learning, position awareness, and inter-voxel signaling to improve representation and sampling efficiency. The authors provide theoretical guarantees of asymptotic convergence to globally optimal designs and demonstrate through simulations that MISCO effectively navigates large design spaces, producing high-performing VSRs for various tasks while balancing efficiency and diversity.
By Junru Song, Huan Xiao, Yang Yang, Guozhen Li, Wei Peng, Xiaoya Zhang, Tingsong Jiang, Weien Zhou, Ying Wen, Feifei Wang, Wen Yao
The paper introduces Morphogene, a compact latent blueprint that links an agent’s body and control policy, enabling coordinated changes at the limb level through AdaConcat. Building on this, GeCode treats co-design as exploration within Morphogene space, using local refinement and global exploration to efficiently navigate design regions. Experiments on various 2D and 3D tasks show that GeCode outperforms state‑of‑the‑art methods, achieving faster convergence and higher performance.
By Fu Feng, Ruixiao Shi, Yucheng Xie, Jing Wang, Xin Geng
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
The paper introduces Auto‑Robotist, a self‑evolving large language model (LLM) agent that transforms evolutionary robot design search traces into an explicit natural‑language skill library. Each skill records a structural archetype, evidence‑grounded rules, and supporting designs, enabling the agent to retrieve and condition LLM edits during search while still using a genetic algorithm for exploration. Experiments on seven EvoGym tasks show that Auto‑Robotist outperforms standard genetic algorithms, especially when transferring learned skills to larger design spaces.
By Yunfei Wang, Xiaohao Xu, Yang Li, Xiaonan Huang
arXiv:2606. 00702v1 Announce Type: cross Abstract: We propose to turn generalist multi-embodiment value functions into reusable models for robot design.
By Nico Bohlinger, Jan Peters
GLAMDRING is a framework that jointly designs a quadruped robot’s morphology and its gait controller using reinforcement learning of Hopf-oscillator Central Pattern Generators (CPGs). Given specifications such as forward‑velocity bounds, actuator power budgets, an actuator library, and payload requirements, the system returns an optimized robot design and a corresponding gait policy, ranking designs by objectives like maximum speed, minimum Cost of Transport, or maximum payload margin. Experiments demonstrate that co‑designing body and gait is essential for meeting locomotion constraints, that actuator feasibility determines payload capacity, and that natural animal gaits emerge from the design process, with a real‑world demonstration confirming the approach’s effectiveness.
By Amogh Joshi, Kaushik Roy
The paper introduces BRIDGE, an open‑source 88 cm tall humanoid robot designed through a data‑driven morphology‑control co‑design framework that optimizes the robot’s body shape for human‑like movement. A new metric combining kinematic retargeting fidelity and dynamic tracking performance is proposed to evaluate morphological fidelity, and the framework achieves state‑of‑the‑art results compared to existing humanoids such as Bumi, K1, and Toddlerbot. The resulting platform, released with its control policy and supporting materials, demonstrates superior fidelity in capturing human motion, robust balance, and highly dynamic maneuvers.
By Jianren Wang, Letian Qian, Zikai Wang, Weiwei Wu, Junjie Zong, Abhinav Gupta, Deepak Pathak
arXiv:2609.21138v1 Announce Type: cross
Abstract: Octopus crawling motivates soft robots that exploit redundancy, yet discovering and organizing diverse coordination modes for adaptation remains chal...
By Seung Hyun Kim, Heng-Sheng Chang, Kimia Kazemi, Prashant Mehta, Mattia Gazzola
arXiv:2607. 00272v1 Announce Type: cross Abstract: Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures.
By Runyu Lu, Yubo Wu, Ethan Kou, Letian Fu, Wenli Xiao, Ajay Mandlekar, Yinzhen Xu, Guanya Shi, Ken Goldberg, Ang Chen, Mosharaf Chowdhury, Yuke Zhu, Linxi "Jim" Fan, Guanzhi Wang
arXiv:2604. 08780v2 Announce Type: replace-cross Abstract: World models promise a paradigm shift in robotics, where an agent learns the physics of its environment once and then acquires behaviors efficiently.
By Mohamad H. Danesh, Chenhao Li, Amin Abyaneh, Anas Houssaini, Kirsty Ellis, Glen Berseth, Marco Hutter, Hsiu-Chin Lin
arXiv:2609.00129v1 Announce Type: cross
Abstract: The performance of artificial intelligence (AI) and machine learning (ML) models degrades when the problem they were trained on drifts. This is a nea...
By J. M. Diederik Kruijssen (Allora Foundation)
arXiv:2607. 06740v1 Announce Type: cross Abstract: Soft robots have attracted significant attention in applications such as medical intervention, rehabilitation, and robotic manipulation due to their inherent compliance, flexibility, and high degrees of freedom.
By Nilay Kushawaha, Muhammad Sunny Nazeer, Baljinder Singh Bal, Cecilia Laschi, Egidio Falotico