mlsanityData to 14 Oct 2026

A Unified Approach to Multi-task Legged Navigation: Temporal Logic Meets Reinforcement Learning

arXiv 2407.06931 ↗First posted 9 Jul 2024cs.ROcited by 0

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

Abstract (arXiv)

This study examines the problem of hopping robot navigation planning to achieve simultaneous goal-directed and environment exploration tasks. We consider a scenario in which the robot has mandatory goal-directed tasks defined using Linear Temporal Logic (LTL) specifications as well as optional exploration tasks represented using a reward function. Additionally, there exists uncertainty in the robot dynamics which results in motion perturbation. We first propose an abstraction of 3D hopping robot dynamics which enables high-level planning and a neural-network-based optimization for low-level control. We then introduce a Multi-task Product IMDP (MT-PIMDP) model of the system and tasks. We propose a unified control policy synthesis algorithm which enables both task-directed goal-reaching behaviors as well as task-agnostic exploration to learn perturbations and reward. We provide a formal proof of the trade-off induced by prioritizing either LTL or RL actions. We demonstrate our methods with simulation case studies in a 2D world navigation environment.

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 7 of its 23 references that are in the map. First posted on arXiv 9 Jul 2024.

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7 of 23 references.

References · 7

  1. 2017Safe Reinforcement Learning via Shieldingcited by 449
  2. 2020Safe Reinforcement Learning in Constrained Markov Decision Processescited by 115
  3. 2018Foundations for Restraining Bolts: Reinforcement Learning with LTLf/LDLf restraining specificationscited by 72
  4. 2020A Tutorial on Sparse Gaussian Processes and Variational Inferencecited by 31
  5. 2015Correct-by-synthesis reinforcement learning with temporal logic constraintscited by 11
  6. 2021Integrated Task and Motion Planning for Safe Legged Navigation in Partially Observable Environmentscited by 8
  7. 2023Walking-by-Logic: Signal Temporal Logic-Guided Model Predictive Control for Bipedal Locomotion Resilient to External Perturbationscited by 6

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  1. 2026GUIDE: Goal-Initialized Directional Understanding for End-to-End Legged Navigation
  2. 2025Attention-Based Map Encoding for Learning Generalized Legged Locomotion
  3. 2024Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots
  4. 2025Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion
  5. 2026Long-Distance Real-World Navigation of the Legged-Wheeled Robot Go2-W Using Deep Reinforcement Learning
  6. 2023NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration
  7. 2026Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specifications
  8. 2026From LLM-Generated Specifications to Learned Quadruped Locomotion