Morphology-Aware Policy and Design Learning Workshop (MAPoDeL)

Morphology-aware policy research offers an exciting intersection of multiple control paradigms, including foundation models, multi-task learning, meta-learning, and co-design learning. In this workshop, we are interested in bringing together researchers from the fields of robotics, machine learning and evolutionary robotics who study morphology-aware policies in order to facilitate connections between the fields' perspectives on the subject. Experts in the three areas understand the importance of such policies but bring different ideas to address the problems. Roboticists are concerned especially with sample efficiency and control on real robots, whereas machine learning researchers are interested in scaling large models with larger datasets and creating artificial generalist agents. In this workshop, we hope to bridge these perspectives in such a way that it helps foster new research directions to contribute to research that advances the field of morphology-aware algorithms. 

Topics of Interest:

• Multi-Embodiment Learning

• Morphology-Agnostic or -Aware Policy Learning

• Reinforcement/Robot Learning for multiple robot morphologies or platforms

• Evolutionary Robotics

• Representation Learning for Multi-Embodiment / Multi-Task Problems

• Co-Design / Co-Adaptation / Co-Optimization of Robot Morphology and Behavior

• Computational Design

• Foundation models for robotics

Accepted Workshop Papers & Posters

P1 - Poster Session 1 (Monring): 11:30 - 12:00
P2 - Poster Session 2 (Afternoon): 16:00 - 17:00

• [P1 - 129] Dynamics-Guided Diffusion Model for Sensor-less Robot Manipulator Design
  Xiaomeng Xu, Huy Ha, Shuran Song

• [P2 - 129] ECoDe: A Sample-Efficient Method for Co-Design of Robotic Agents
  Kishan Reddy Nagiredla, Buddhika Laknath Semage, Arun Kumar A V, Thommen Karimpanal George, Santu Rana

• [P1 - 130] RoboNet: A Sample-Efficient Robot Co-Design Generator
  Kishan Reddy Nagiredla, Arun Kumar A V, Thommen Karimpanal George, Santu Rana

• [P2 - 130] D(R,O) Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping
  Zhenyu Wei, Zhixuan Xu, Jingxiang Guo, Yiwen Hou, Chongkai Gao, Cai Zhehao, Jiayu Luo, Lin Shao

• [P1 - 131] Minimally Invasive Morphology Adaptation via Parameter Efficient Fine-Tuning
  Michael Przystupa, Hongyao Tang, Mariano Phielipp, Santiago Miret, Martin Jägersand, Glen Berseth

• [P2 - 131] Overcoming State and Action Space Disparities in Multi-Domain, Multi-Task Reinforcement Learning
  Reginald McLean, Kai Yuan, Isaac Woungang, Nariman Farsad, Pablo Samuel Castro

• [P1 - 132] One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion
  Nico Bohlinger, Grzegorz Czechmanowski, Maciej Piotr Krupka, Piotr Kicki, Krzysztof Walas, Jan Peters, Davide Tateo

• [P2 - 132] Making use of design-aware policy optimization in legged-robotics co-design
  Gabriele Fadini, Stelian Coros

• [P1 - 133] Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation
  Zhengyu Zhang, Quanquan Peng, Rosario Scalise, Byron Boots

• [P2 - 133] MAC: Morphology-Aware Control Design with Deep Reinforcement Learning for Navigation of Squeezable Unmanned Aerial Vehicles
  Van Huyen Dang, Erdal Kayacan, Adrian Redder

• [P1 - 134] Grammarization-based Multi-Morphology and Multi-Target Grasping via Reinforcement Learning in the Latent Space of Autoencoders
  Leonidas Askianakis

• [P2 - 134] Following Ancestral Footsteps: Co-Designing Agent Morphology and Behaviour with Self-Imitation Learning
  Sergio Hernández-Gutiérrez, Ville Kyrki, Kevin Sebastian Luck