Learning from Demonstrations using Signal Temporal Logic

2021-02-15 · Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis

Learning-from-demonstrations is an emerging paradigm to obtain effective robot control policies for complex tasks via reinforcement learning without the need to explicitly design reward functions. However, it is susceptible to imperfections in demonstrations and also raises concerns of safety and interpretability in the learned control policies. To address these issues, we use Signal Temporal Logic to evaluate and rank the quality of demonstrations. Temporal logic-based specifications allow us to create non-Markovian rewards, and also define interesting causal dependencies between tasks such as sequential task specifications. We validate our approach through experiments on discrete-world and OpenAI Gym environments, and show that our approach outperforms the state-of-the-art Maximum Causal Entropy Inverse Reinforcement Learning.

📄 PDF Abstract BibTeX arXiv:2102.07730

Code (0)

등록된 구현이 없습니다.

Tasks

OpenAI Gym reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Learning from Imperfect Demonstrations via Temporal Behavior Tree-Guided Trajectory Repair

2026-04-05 · Aniruddh G. Puranic, Sebastian Schirmer, John S. Baras, Calin Belta arxiv

Learning robot control policies from demonstrations is a powerful paradigm, yet real-world data is often suboptimal, noisy, or otherwise imperfect, posing significant challenges for imitation and reinforcement learning. …

Reinforcement Learning

Signal Temporal Logic-Guided Apprenticeship Learning

2023-11-09 · Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis

Apprenticeship learning crucially depends on effectively learning rewards, and hence control policies from user demonstrations. Of particular difficulty is the setting where the desired task consists of a number of sub-g…

Automata Guided Reinforcement Learning With Demonstrations

2018-09-17 · Xiao Li, Yao Ma, Calin Belta

Tasks with complex temporal structures and long horizons pose a challenge for reinforcement learning agents due to the difficulty in specifying the tasks in terms of reward functions as well as large variances in the lea…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Interactive Learning from Natural Language and Demonstrations using Signal Temporal Logic

2022-07-01 · Sara Mohammadinejad, Jesse Thomason, Jyotirmoy V. Deshmukh

Natural language is an intuitive way for humans to communicate tasks to a robot. While natural language (NL) is ambiguous, real world tasks and their safety requirements need to be communicated unambiguously. Signal Temp…

Formal Logic Q-Learning Reinforcement Learning (RL)Semantic Parsing

Safe and Optimal Learning from Preferences via Weighted Temporal Logic with Applications in Robotics and Formula 1

2025-11-11 · Ruya Karagulle, Cristian-Ioan Vasile, Necmiye Ozay arxiv

Autonomous systems increasingly rely on human feedback to align their behavior, expressed as pairwise comparisons, rankings, or demonstrations. While existing methods can adapt behaviors, they often fail to guarantee saf…