# ARMCHAIR: integrated inverse reinforcement learning and model predictive control for human-robot collaboration

arXiv: https://arxiv.org/abs/2402.19128 · HTML: https://mlsanity.com/paper/2402.19128/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Social robot navigation, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 16 of its 64 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Placed in

- Offline RL and imitation learning from data / [Social robot navigation](https://mlsanity.com/offlinerl/social-robot-navigation/)
- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 16 of 16)

- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [Core Challenges of Social Robot Navigation: A Survey](https://mlsanity.com/paper/2103.05668/) (2021)
- [Multi-Agent Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1907.13220/) (2019)
- [On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference](https://mlsanity.com/paper/1906.09624/) (2019)
- [MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning](https://mlsanity.com/paper/2201.00012/) (2021)
- [Meaningful human control: actionable properties for AI system development](https://mlsanity.com/paper/2112.01298/) (2021)
- [Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments](https://mlsanity.com/paper/2102.05382/) (2021)
- [Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2201.06539/) (2022)
- [Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments](https://mlsanity.com/paper/2012.02527/) (2020)
- [Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study](https://mlsanity.com/paper/2305.15187/) (2023)
- [Real-time motion planning and decision-making for a group of differential drive robots under connectivity constraints using robust MPC and mixed-integer programming](https://mlsanity.com/paper/2206.00097/) (2022)
- [Interaction-Aware Sampling-Based MPC with Learned Local Goal Predictions](https://mlsanity.com/paper/2309.14931/) (2023)
- [Distributed Stochastic Model Predictive Control for Human-Leading Heavy-Duty Truck Platoon](https://mlsanity.com/paper/2201.11859/) (2022)
- [An Incremental Inverse Reinforcement Learning Approach for Motion Planning with Separated Path and Velocity Preferences](https://mlsanity.com/paper/2301.10528/) (2023)
- [Robust Robot Planning for Human-Robot Collaboration](https://mlsanity.com/paper/2302.13916/) (2023)
- [Pilot Performance modeling via observer-based inverse reinforcement learning](https://mlsanity.com/paper/2307.13150/) (2023)

## Built on by (most cited first, 2 of 2)

- [MDMP: Multi-modal Diffusion for supervised Motion Predictions with uncertainty](https://mlsanity.com/paper/2410.03860/) (2024)
- [DFPO: Scaling Value Modeling via Distributional Flow towards Robust and Generalizable LLM Post-Training](https://mlsanity.com/paper/2602.05890/) (2026)

## Abstract (arXiv)

One of the key issues in human-robot collaboration is the development of computational models that allow robots to predict and adapt to human behavior. Much progress has been achieved in developing such models, as well as control techniques that address the autonomy problems of motion planning and decision-making in robotics. However, the integration of computational models of human behavior with such control techniques still poses a major challenge, resulting in a bottleneck for efficient collaborative human-robot teams. In this context, we present a novel architecture for human-robot collaboration: Adaptive Robot Motion for Collaboration with Humans using Adversarial Inverse Reinforcement learning (ARMCHAIR). Our solution leverages adversarial inverse reinforcement learning and model predictive control to compute optimal trajectories and decisions for a mobile multi-robot system that collaborates with a human in an exploration task. During the mission, ARMCHAIR operates without human intervention, autonomously identifying the necessity to support and acting accordingly. Our approach also explicitly addresses the network connectivity requirement of the human-robot team. Extensive simulation-based evaluations demonstrate that ARMCHAIR allows a group of robots to safely support a simulated human in an exploration scenario, preventing collisions and network disconnections, and improving the overall performance of the task.