TGPO: Temporal Grounded Policy Optimization for Signal Temporal Logic Tasks
TGPO method improves task success rate by 31.6% in Signal Temporal Logic tasks.
Key Findings
Methodology
TGPO decomposes Signal Temporal Logic (STL) tasks into timed subgoals and invariant constraints, using a hierarchical framework to address the problem. The high-level component allocates specific times for subgoals, while the low-level time-conditioned policy learns to achieve sequenced subgoals using dense, stage-wise reward signals. During inference, various time allocations are sampled, and the most promising assignment is selected for the policy network to rollout the solution trajectory. Metropolis-Hastings sampling guides the high-level temporal search, focusing exploration on temporally feasible solutions.
Key Results
- TGPO was tested across five environments, ranging from low-dimensional navigation to manipulation, drone, and quadrupedal locomotion tasks. Under a wide range of STL tasks, TGPO significantly outperformed state-of-the-art baselines, especially in high-dimensional and long-horizon cases, with an average of 31.6% improvement in task success rate.
- In 'Quadrotor' and 'Ant' environments, TGPO* achieved 86.46% and 61.57% success rates, respectively, while most baselines achieved less than 10%.
- In low-dimensional systems, TGPO excelled in handling complex STL tasks, achieving significantly higher success rates than other baselines.
Significance
TGPO demonstrates significant performance improvements in Signal Temporal Logic tasks, particularly in high-dimensional and long-horizon tasks. This method holds substantial academic significance, advancing automated solutions for complex tasks, and potential industrial applications, especially in robotics and automation systems. TGPO addresses the sparse reward problem in STL tasks, providing new insights for planning and executing complex tasks.
Technical Contribution
TGPO's technical contributions include a novel hierarchical reinforcement learning framework capable of effectively handling general STL tasks. By decomposing STL tasks into subgoals and invariant constraints and using Metropolis-Hastings sampling for time allocation, TGPO overcomes the sparse reward problem faced by traditional RL methods. This framework offers new theoretical guarantees and engineering possibilities, advancing automated solutions for complex tasks.
Novelty
TGPO is the first hierarchical model-free RL algorithm capable of solving general nested STL tasks. Compared to existing methods, TGPO constructs an augmented MDP with dense, stage-wise rewards through critic-guided Bayesian sampling and STL decomposition, overcoming the sparse reward challenges faced by existing RL approaches.
Limitations
- TGPO may encounter computational complexity issues when dealing with very high-dimensional systems, particularly during the time allocation sampling process.
- In certain specific STL tasks, TGPO may require longer training times to achieve convergence.
Future Work
Future research directions include optimizing TGPO's computational efficiency, particularly in high-dimensional systems. Additionally, exploring TGPO's performance in more practical application scenarios and its integration with other advanced technologies are worth investigating.
AI Executive Summary
Learning control policies for complex, long-horizon tasks is a central challenge in robotics and autonomous systems. Signal Temporal Logic (STL) offers a powerful and expressive language for specifying such tasks, but its non-Markovian nature and inherent sparse reward make it difficult to be solved via standard Reinforcement Learning (RL) algorithms. Prior RL approaches focus only on limited STL fragments or use STL robustness scores as sparse terminal rewards.
This paper proposes TGPO, Temporal Grounded Policy Optimization, to solve general STL tasks. TGPO decomposes STL into timed subgoals and invariant constraints and provides a hierarchical framework to tackle the problem. The high-level component proposes concrete time allocations for these subgoals, and the low-level time-conditioned policy learns to achieve the sequenced subgoals using a dense, stage-wise reward signal. During inference, various time allocations are sampled, and the most promising assignment is selected for the policy network to rollout the solution trajectory.
Experiments were conducted across five environments, ranging from low-dimensional navigation to manipulation, drone, and quadrupedal locomotion tasks. Under a wide range of STL tasks, TGPO significantly outperformed state-of-the-art baselines, especially in high-dimensional and long-horizon cases, with an average of 31.6% improvement in task success rate compared to the best baseline. This method holds substantial academic significance, advancing automated solutions for complex tasks, and potential industrial applications, especially in robotics and automation systems.
Deep Dive
Abstract
Learning control policies for complex, long-horizon tasks is a central challenge in robotics and autonomous systems. Signal Temporal Logic (STL) offers a powerful and expressive language for specifying such tasks, but its non-Markovian nature and inherent sparse reward make it difficult to be solved via standard Reinforcement Learning (RL) algorithms. Prior RL approaches focus only on limited STL fragments or use STL robustness scores as sparse terminal rewards. In this paper, we propose TGPO, Temporal Grounded Policy Optimization, to solve general STL tasks. TGPO decomposes STL into timed subgoals and invariant constraints and provides a hierarchical framework to tackle the problem. The high-level component of TGPO proposes concrete time allocations for these subgoals, and the low-level time-conditioned policy learns to achieve the sequenced subgoals using a dense, stage-wise reward signal. During inference, we sample various time allocations and select the most promising assignment for the policy network to rollout the solution trajectory. To foster efficient policy learning for complex STL with multiple subgoals, we leverage the learned critic to guide the high-level temporal search via Metropolis-Hastings sampling, focusing exploration on temporally feasible solutions. We conduct experiments on five environments, ranging from low-dimensional navigation to manipulation, drone, and quadrupedal locomotion. Under a wide range of STL tasks, TGPO significantly outperforms state-of-the-art baselines (especially for high-dimensional and long-horizon cases), with an average of 31.6% improvement in task success rate compared to the best baseline. The code will be available at https://github.com/mengyuest/TGPO