# CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces

arXiv: https://arxiv.org/abs/2211.15824 · HTML: https://mlsanity.com/paper/2211.15824/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 57 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 30 of 36)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [TarMAC: Targeted Multi-Agent Communication](https://mlsanity.com/paper/1810.11187/) (2018)
- [PLAS: Latent Action Space for Offline Reinforcement Learning](https://mlsanity.com/paper/2011.07213/) (2020)
- [Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks](https://mlsanity.com/paper/1812.09755/) (2018)
- [Learning Latent Representations to Influence Multi-Agent Interaction](https://mlsanity.com/paper/2011.06619/) (2020)
- [Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks](https://mlsanity.com/paper/1906.08880/) (2019)
- [Modeling Others using Oneself in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.09640/) (2018)
- [Neural Dynamic Policies for End-to-End Sensorimotor Learning](https://mlsanity.com/paper/2012.02788/) (2020)
- [Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?](https://mlsanity.com/paper/1611.01055/) (2016)
- [LASER: Learning a Latent Action Space for Efficient Reinforcement Learning](https://mlsanity.com/paper/2103.15793/) (2021)
- [Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics](https://mlsanity.com/paper/2211.02231/) (2022)
- [Model-Based Opponent Modeling](https://mlsanity.com/paper/2108.01843/) (2021)
- [A Comparison of Action Spaces for Learning Manipulation Tasks](https://mlsanity.com/paper/1908.08659/) (2019)
- [Learning Variable Impedance Control for Contact Sensitive Tasks](https://mlsanity.com/paper/1907.07500/) (2019)
- [MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models](https://mlsanity.com/paper/2103.03662/) (2021)
- [Implicit Kinematic Policies: Unifying Joint and Cartesian Action Spaces in End-to-End Robot Learning](https://mlsanity.com/paper/2203.01983/) (2022)
- [Multi-Agent Interactions Modeling with Correlated Policies](https://mlsanity.com/paper/2001.03415/) (2020)

## Built on by (most cited first, 6 of 6)

- [On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer](https://mlsanity.com/paper/2312.03673/) (2023)
- [DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems](https://mlsanity.com/paper/2407.11472/) (2024)
- [CHORUS: Decentralized Multi-Embodiment Collaboration with One VLA Policy](https://mlsanity.com/paper/2606.12352/) (2026)
- [MeMo: Meaningful, Modular Controllers via Noise Injection](https://mlsanity.com/paper/2407.01567/) (2024)
- [The Shortcomings of Force-from-Motion in Robot Learning](https://mlsanity.com/paper/2407.02904/) (2024)
- [Watch, Infer, Coordinate: Inferring Robot Partner Constraints for Zero-Shot Coordination](https://mlsanity.com/paper/2610.02170/) (2026)

## Abstract (arXiv)

Multi-robot manipulation tasks involve various control entities that can be separated into dynamically independent parts. A typical example of such real-world tasks is dual-arm manipulation. Learning to naively solve such tasks with reinforcement learning is often unfeasible due to the sample complexity and exploration requirements growing with the dimensionality of the action and state spaces. Instead, we would like to handle such environments as multi-agent systems and have several agents control parts of the whole. However, decentralizing the generation of actions requires coordination across agents through a channel limited to information central to the task. This paper proposes an approach to coordinating multi-robot manipulation through learned latent action spaces that are shared across different agents. We validate our method in simulated multi-robot manipulation tasks and demonstrate improvement over previous baselines in terms of sample efficiency and learning performance.