mlsanityData to 7 Oct 2026

Learning Quadruped Locomotion Policies using Logical Rules

arXiv 2107.10969 ↗First posted 23 Jul 2021cs.RO cs.AIcited by 6

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Abstract (arXiv)

Quadruped animals are capable of exhibiting a diverse range of locomotion gaits. While progress has been made in demonstrating such gaits on robots, current methods rely on motion priors, dynamics models, or other forms of extensive manual efforts. People can use natural language to describe dance moves. Could one use a formal language to specify quadruped gaits? To this end, we aim to enable easy gait specification and efficient policy learning. Leveraging Reward Machines~(RMs) for high-level gait specification over foot contacts, our approach is called RM-based Locomotion Learning~(RMLL), and supports adjusting gait frequency at execution time. Gait specification is enabled through the use of a few logical rules per gait (e.g., alternate between moving front feet and back feet) and does not require labor-intensive motion priors. Experimental results in simulation highlight the diversity of learned gaits (including two novel gaits), their energy consumption and stability across different terrains, and the superior sample-efficiency when compared to baselines. We also demonstrate these learned policies with a real quadruped robot. Video and supplementary materials: https://sites.google.com/view/rm-locomotion-learning/home

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 13 of its 27 references that are in the map. First posted on arXiv 23 Jul 2021.

Builds on

13 of 27 references.

References · 13

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 430
  3. 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 277
  4. 2020Learning Agile Robotic Locomotion Skills by Imitating Animalscited by 229
  5. 2020Reward Machines: Exploiting Reward Function Structure in Reinforcement Learningcited by 183
  6. 2020Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Compositioncited by 71
  7. 2019Joint Inference of Reward Machines and Policies for Reinforcement Learningcited by 56
  8. 2021Fast and Efficient Locomotion via Learned Gait Transitionscited by 50
  9. 2019Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Controlcited by 43
  10. 2020Reward Machines for Cooperative Multi-Agent Reinforcement Learningcited by 35
  11. 2020Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotioncited by 22
  12. 2021Inferring Probabilistic Reward Machines from Non-Markovian Reward Processes for Reinforcement Learningcited by 13
1 more
  1. 2018Diverse Exploration for Fast and Safe Policy Improvementcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 4.

6 papers.

  1. 2024Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regressioncited by 4
  2. 2024Efficient Reinforcement Learning in Probabilistic Reward Machinescited by 4
  3. 2026Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specificationscited by 2
  4. 2026Active Reward Machine Inference From Raw State Trajectoriescited by 0
  5. 2026From LLM-Generated Specifications to Learned Quadruped Locomotioncited by 0
  6. 2026Video2STL: Grounding VLM-Generated Temporal Specifications for Robot Learningcited by 0