# Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning

arXiv: https://arxiv.org/abs/2405.03113 · HTML: https://mlsanity.com/paper/2405.03113/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 30 of its 59 references that are in the map.
- First posted on arXiv 6 May 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

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

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [Diversity is All You Need: Learning Skills without a Reward Function](https://mlsanity.com/paper/1802.06070/) (2018)
- [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://mlsanity.com/paper/2202.02005/) (2022)
- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [A Divergence Minimization Perspective on Imitation Learning Methods](https://mlsanity.com/paper/1911.02256/) (2019)
- [Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills](https://mlsanity.com/paper/2104.07749/) (2021)
- [METRA: Scalable Unsupervised RL with Metric-Aware Abstraction](https://mlsanity.com/paper/2310.08887/) (2023)
- [Contrastive Preference Learning: Learning from Human Feedback without RL](https://mlsanity.com/paper/2310.13639/) (2023)
- [f-IRL: Inverse Reinforcement Learning via State Marginal Matching](https://mlsanity.com/paper/2011.04709/) (2020)
- [DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization](https://mlsanity.com/paper/2112.04716/) (2021)
- [Dual RL: Unification and New Methods for Reinforcement and Imitation Learning](https://mlsanity.com/paper/2302.08560/) (2023)
- [Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification](https://mlsanity.com/paper/2103.12656/) (2021)
- [Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality](https://mlsanity.com/paper/2205.13521/) (2022)
- [What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery](https://mlsanity.com/paper/2306.15864/) (2023)
- [SMORE: Score Models for Offline Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2311.02013/) (2023)
- [A Ranking Game for Imitation Learning](https://mlsanity.com/paper/2202.03481/) (2022)
- [Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/1906.01408/) (2019)
- [Efficient and Reactive Planning for High Speed Robot Air Hockey](https://mlsanity.com/paper/2107.06140/) (2021)
- [Learning Control for Air Hockey Striking using Deep Reinforcement Learning](https://mlsanity.com/paper/1702.08074/) (2017)
- [AiRLIHockey: Highly Reactive Contact Control and Stochastic Optimal Shooting](https://mlsanity.com/paper/2401.14964/) (2024)

## Built on by (most cited first, 5 of 5)

- [A Dual Approach to Imitation Learning from Observations with Offline Datasets](https://mlsanity.com/paper/2406.08805/) (2024)
- [Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identifications](https://mlsanity.com/paper/2410.20357/) (2024)
- [Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2505.03172/) (2025)
- [A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics](https://mlsanity.com/paper/2411.05718/) (2024)
- [Learning Object Manipulation from Scratch via Contrastive Interaction](https://mlsanity.com/paper/2606.11525/) (2026)

## Abstract (arXiv)

Reinforcement Learning is a promising tool for learning complex policies even in fast-moving and object-interactive domains where human teleoperation or hard-coded policies might fail. To effectively reflect this challenging category of tasks, we introduce a dynamic, interactive RL testbed based on robot air hockey. By augmenting air hockey with a large family of tasks ranging from easy tasks like reaching, to challenging ones like pushing a block by hitting it with a puck, as well as goal-based and human-interactive tasks, our testbed allows a varied assessment of RL capabilities. The robot air hockey testbed also supports sim-to-real transfer with three domains: two simulators of increasing fidelity and a real robot system. Using a dataset of demonstration data gathered through two teleoperation systems: a virtualized control environment, and human shadowing, we assess the testbed with behavior cloning, offline RL, and RL from scratch.