arXiv Machine Learning

Higher-Order Morphology Priors for Quadruped Reinforcement Learning Under Actuator Degradation

The paper investigates how incorporating higher‑order mechanical structure into reinforcement learning policies can improve quadruped locomotion when actuators degrade. By modeling the Unitree Go1 robot as a cell complex and using Hodge‑based message passing across node‑edge‑face relationships, the authors show that this higher‑order morphology actor outperforms other approaches on unseen actuator degradations, achieving higher returns, better survival, and lower velocity‑tracking error. The study demonstrates that explicitly encoding complex mechanical relationships provides a powerful inductive bias for whole‑body compensation in degraded systems.

arXiv AI

Aug 24

Graph-Operator World Models for Morphology-Parameter Generalization in Continuous Control

Graph-Operator World Models (GraphOp-WM) are a structured approach to learning world models that generalize across varying morphology parameters in continuous control tasks. The model represents robot bodies and their kinematic relationships as an attributed graph, decomposing each transition into a morphology‑independent local dynamics basis and a morphology‑conditioned structured operator. This operator blends node‑local modulation, kinematic‑tree coupling, and a low‑rank global correction, while architectural design choices encourage the operator to capture static morphology dependence. The framework supports reward, value, and TD‑MPC‑style planning through graph‑level readout and edge‑wise action representations, and is evaluated on controlled MuJoCo parameter splits involving interpolation, extrapolation, and held‑out compositions of link geometry, mass, damping, and actuation in Hopper, Walker2d, and HalfCheetah.

By Xu Yang, Yiqin Yang, Qianchuan Zhao

robotics

arXiv Machine Learning

Aug 18

Morphology-Conditioned World Model for Cross-Embodiment Quadrupedal Locomotion

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

agents robotics fine-tuning

arXiv Machine Learning

Aug 4

Rapid Embodiment Adaptation for Quadrupedal Locomotion

arXiv:2608. 01506v1 Announce Type: cross Abstract: Humans readily adapt their movements as their bodies change through aging, injury, or load carrying, but learning-based robot policies often break when hardware properties shift.

By Dichen Li, Bo Ai, Nico Bohlinger, Jan Peters, Hao Su, Henrik I. Christensen

robotics

arXiv Machine Learning

Sep 17

RecMorph: Topology-Guided Spatial Recurrence for Generalized Morphology Control

RecMorph introduces a topology‑guided spatial recurrent architecture for generalized morphology control, converting a kinematic tree into a sequence that enables joint cross‑limb communication and representation transformation. The design incorporates residual preservation, RMS normalization, and input‑dependent channel modulation to stabilize repeated spatial transformations, achieving linear token complexity. Across five UNIMAL tasks and a four‑platform quadruped setting, RecMorph outperforms existing controllers in training performance, inference throughput, and generalization to unseen bodies with up to 30 limbs, while also demonstrating robust real‑world performance on Go1/Go2 trials.

By Quanrui Rao, Yong Liu, Xueming Xiao, Yingbo Luo, Kun Wu, Zhenyu Xu, Meibao Yao

robotics efficiency benchmarks

arXiv AI

Aug 10

Learning to Walk With Less: A Dyna-Style Approach to Quadrupedal Locomotion

arXiv:2509. 06296v2 Announce Type: replace-cross Abstract: Traditional on-policy reinforcement learning (RL) controllers for quadrupedal locomotion often suffer from low data efficiency, requiring millions of interactions with simulated environments to achieve stable control.

By Francisco Affonso, Felipe Tommaselli, Jo\~ao H. Al\'essio, Vivian S. Medeiros, Mateus V. Gasparino, Girish Chowdhary, Marcelo Becker

reinforcement-learning robotics benchmarks

arXiv Machine Learning

Oct 8

Energy-Efficient Gait Adaptation via Hierarchical Reinforcement Learning for Quadrupedal Locomotion Across Diverse Terrains

arXiv:2610.10297v1 Announce Type: cross Abstract: While energy efficiency is a critical objective for legged-robot locomotion control, achieving low energy consumption while maintaining robust perfor...

By Ammar Issa, Anubhav Singh, Anton Tsaritsin, Sergey Kolyubin

reinforcement-learning robotics

arXiv AI

Oct 16

SOMA: Stack-of-Objects Manipulation using a Quadruped Robot

SOMA is a hierarchical reinforcement learning framework that enables a quadruped robot to perform whole-body, non-prehensile manipulation of multi-object stacks. It incorporates explicit base-height control to allow full three-dimensional pushing, and separates high-level manipulation decisions from low-level locomotion adjustments. In simulation, SOMA achieves over 90% success rates for single and two-box reposing tasks, and its zero-shot sim-to-real transfer is validated on a Unitree Go1 robot handling various payloads.

By Stergios E. Bachoumas, Panagiotis Artemiadis

reinforcement-learning robotics benchmarks

arXiv AI

Oct 1

TERRA: Terrain-Aware Reconstruction, Retargeting and Control for Musculoskeletal Locomotion

arXiv:2609.38653v1 Announce Type: cross Abstract: Recent advances in musculoskeletal modeling and reinforcement learning have enabled muscle-actuated agents to reproduce increasingly complex human mo...

By Merkourios Simos, Chengkun Li, Bianca Ziliotto, Alexander Mathis

agents reinforcement-learning robotics benchmarks

Hugging Face Trending Papers

Jul 13

SKooP: Symmetric Koopman Predictions for Faster and More Generalizable Legged Robot Locomotion with Reinforcement Learning

Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency. In robotics, a recent line of work has emerged addressing this problem by encoding physics priors in the learning process.

agents reinforcement-learning robotics benchmarks

arXiv Machine Learning

Sep 21

Diverse and Adaptable Arm Coordination for Octopus-Crawling via Diffusion-Based Uncertainty-Aware Optimization

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

rag diffusion robotics

arXiv Machine Learning

Jul 23

Isaac Sim-to-Real: Reinforcement Learning based Locomotion for Quadrupeds

arXiv:2607. 18135v1 Announce Type: cross Abstract: Learning-based approaches to locomotion have risen in popularity in recent years, showing the capability for complex legged locomotion and whole-body control.

By Jordan Dowdy, Jean Chagas Vaz

reinforcement-learning robotics

arXiv Machine Learning

Jul 14

SKooP: Symmetric Koopman Predictions for Faster and More Generalizable Legged Robot Locomotion with Reinforcement Learning

arXiv:2607. 11624v1 Announce Type: cross Abstract: Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency.

By Evelyn D'Elia, Weishu Zhan, Giulio Turrisi, Giulio Romualdi, Giuseppe L'Erario, Raffaello Camoriano, Wei Pan, Daniele Pucci

agents reinforcement-learning robotics benchmarks

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