Multi-agent robot tasks improved by logic-guided data and policies
LTLDiff: Finite Linear Temporal Logic-Guided Data Generation and Diffusion Policies for Multi-agent Robotic Manipulation
Robotics
Summary
Multi-agent robots often need to work together carefully in the right order and at the right times to finish tasks successfully. The authors developed a method called LTLDiff that uses logic rules learned from instructions to guide how robots learn and plan their moves. This helps the robots coordinate better and avoid mistakes like acting out of order or not syncing up. They tested LTLDiff on robot tasks and found it led to more success than previous methods.
What this means in practice
- •For industrial robot integrators: Coordinate multiple robots in factory assembly lines using logic-guided policies for improved task timing and order.
- •For automation system developers: Design multi-robot systems that automatically generate and use logical task specifications to reduce coordination errors.
Authors
Chuhan Meng, Haiyan Yin
Abstract
Multi-agent robotic manipulation tasks require coordination among agents to satisfy task-level temporal, logical, and safety constraints. Recently, diffusion policies have been used to perform the task. However, they still suffer from desynchronization, incorrect action ordering, and coordination failures in tasks that require simultaneous or sequential multi-agent interaction. Therefore, LTLDiff is proposed as a framework that combines Finite Linear Temporal Logic (LTLf) specification learning for both the generation of demonstrations and learning via diffusion policies. Each task has a specific LTLf formula that is learned from a set of natural language instructions using a large-scale language model. To enable a fixed-dimensional vector embedding of the learned specification from the language model, LTLf uses an abstract syntax tree representation scheme. This embedding of logic serves as a condition for (i) logic-guided data collection and (ii) diffusion-based policy training, encouraging trajectories that are consistent with the desired ordering and coordination requirements. Experiments on multi-agent LTLDiff manipulation tasks demonstrate improved task success rates compared to the baseline. Together, these contributions demonstrate the effectiveness of LTLDiff for coordinated multi-agent manipulation.