# Dual-Quadruped Collaborative Transportation in Narrow Environments via Safe Reinforcement Learning

arXiv: https://arxiv.org/abs/2602.16353 · HTML: https://mlsanity.com/paper/2602.16353/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Placed in

- Deep reinforcement learning methods / [Safe and constrained RL](https://mlsanity.com/deeprl/safe-and-constrained-rl/)

## Builds on (most cited first, 13 of 13)

- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2109.11251/) (2021)
- [Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior](https://mlsanity.com/paper/2212.03238/) (2022)
- [Actor-Critic Reinforcement Learning for Control with Stability Guarantee](https://mlsanity.com/paper/2004.14288/) (2020)
- [Stabilize to Act: Learning to Coordinate for Bimanual Manipulation](https://mlsanity.com/paper/2309.01087/) (2023)
- [Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing](https://mlsanity.com/paper/2411.07104/) (2024)
- [Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport](https://mlsanity.com/paper/2103.15260/) (2021)
- [Cooperative Aerial-Ground Multi-Robot System for Automated Construction Tasks](https://mlsanity.com/paper/2204.13926/) (2022)
- [Layered Control for Cooperative Locomotion of Two Quadrupedal Robots: Centralized and Distributed Approaches](https://mlsanity.com/paper/2211.06913/) (2022)
- [Sampling-Based Constrained Motion Planning with Products of Experts](https://mlsanity.com/paper/2412.17462/) (2024)
- [Bilevel Learning for Dual-Quadruped Collaborative Transportation under Kinematic and Anisotropic Velocity Constraints](https://mlsanity.com/paper/2412.08644/) (2024)
- [Safety-Critical and Distributed Nonlinear Predictive Controllers for Teams of Quadrupedal Robots](https://mlsanity.com/paper/2503.14656/) (2025)

## Abstract (arXiv)

Collaborative transportation, where multiple robots collaboratively transport a payload, has garnered significant attention in recent years. While ensuring safe and high-performance inter-robot collaboration is critical for effective task execution, it is difficult to pursue in narrow environments where the feasible region is extremely limited. To address this challenge, we propose a novel approach for dual-quadruped collaborative transportation via safe reinforcement learning (RL). Specifically, we model the task as a fully cooperative constrained Markov game, where collision avoidance is formulated as constraints. We introduce a cost-advantage decomposition method that enforces the sum of team constraints to remain below an upper bound, thereby guaranteeing task safety within an RL framework. Furthermore, we propose a constraint allocation method that assigns shared constraints to individual robots to maximize the overall task reward, encouraging autonomous task-assignment among robots, thereby improving collaborative task performance. Simulation and real-time experimental results demonstrate that the proposed approach achieves superior performance and a higher success rate in dual-quadruped collaborative transportation compared to existing methods.