mlsanityData to 14 Oct 2026

PolyMorph: Learning Locomotion Policy for Cross-Embodiment Adaptation

arXiv 2610.19615 ↗First posted 14 Oct 2026cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

Reinforcement learning has enabled robust locomotion for legged robots, but learned policies are typically tied to a specific robot morphology and require additional engineering when the number of legs, actuators, or mechanical properties change. We present PolyMorph, a morphology-conditioned framework that learns a single locomotion policy for heterogeneous legged robots. PolyMorph converts each robot's URDF into joint-link modules and combines a recurrent encoder, graph-based Transformer, and per-joint decoder to generate actions across variable leg counts and joint configurations. To improve training stability, PolyMorph uses morphology-normalized rewards, adaptive robot sampling, and URDF-derived PD-gain initialization. We evaluate PolyMorph in simulation across bipedal, tripedal, and quadrupedal robots, including unseen morphology variants and platforms, and deploy the policy on real quadruped platforms operated in multiple configurations. The results show that a single PolyMorph policy generalizes across diverse robot morphologies without per-robot policy retraining.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 17 of its 23 references that are in the map. First posted on arXiv 14 Oct 2026.

Builds on

17 of 23 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Learning coordinated badminton skills for legged manipulators29 May
  2. 2025GCNT: Graph-Based Transformer Policies for Morphology-Agnostic Reinforcement Learning21 May
  3. 2024One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion10 Sep

Other references · 14

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2023Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardwarecited by 1,568
  4. 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 508
  5. 2019Learning agile and dynamic motor skills for legged robotscited by 504
  6. 2022Learning robust perceptive locomotion for quadrupedal robots in the wildcited by 382
  7. 2023Real-World Humanoid Locomotion with Reinforcement Learningcited by 187
  8. 2022MetaMorph: Learning Universal Controllers with Transformerscited by 89
  9. 2022GenLoco: Generalized Locomotion Controllers for Quadrupedal Robotscited by 48
  10. 2024Body Transformer: Leveraging Robot Embodiment for Policy Learningcited by 32
  11. 2025LocoFormer: Generalist Locomotion via Long-context Adaptationcited by 20
  12. 2025High-speed control and navigation for quadrupedal robots on complex and discrete terraincited by 19
2 more
  1. 2022Meta Reinforcement Learning for Optimal Design of Legged Robotscited by 13
  2. 2023Imitating and Finetuning Model Predictive Control for Robust and Symmetric Quadrupedal Locomotioncited by 10

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  1. 2025Multi-Loco: Unifying Multi-Embodiment Legged Locomotion via Reinforcement Learning Augmented Diffusion
  2. 2026MorFiC: Fixing Value Miscalibration for Zero-Shot Quadruped Transfer
  3. 2025McARL:Morphology-Control-Aware Reinforcement Learning for Generalizable Quadrupedal Locomotion
  4. 2025Reference Free Platform Adaptive Locomotion for Quadrupedal Robots using a Dynamics Conditioned Policy
  5. 2026Morphology-Conditioned World Model for Cross-Embodiment Quadrupedal Locomotion
  6. 2025Learning a Unified Policy for Position and Force Control in Legged Loco-Manipulation
  7. 2025GRoQ-LoCO: Generalist and Robot-agnostic Quadruped Locomotion Control using Offline Datasets
  8. 2024Leveraging Symmetry in RL-based Legged Locomotion Control