# Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning

arXiv: https://arxiv.org/abs/2409.14435 · HTML: https://mlsanity.com/paper/2409.14435/

## Key facts

- Cited by 3 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 10 of its 50 references that are in the map.
- First posted on arXiv 22 Sep 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, 10 of 10)

- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark](https://mlsanity.com/paper/2109.14545/) (2021)
- [Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks](https://mlsanity.com/paper/2407.12998/) (2024)
- [SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation](https://mlsanity.com/paper/2310.09998/) (2023)
- [Reinforcement Learning with Adaptive Curriculum Dynamics Randomization for Fault-Tolerant Robot Control](https://mlsanity.com/paper/2111.10005/) (2021)
- [Deep-Learning Framework for Optimal Selection of Soil Sampling Sites](https://mlsanity.com/paper/2309.00974/) (2023)
- [Comparison of Model Predictive and Reinforcement Learning Methods for Fault Tolerant Control](https://mlsanity.com/paper/2008.04403/) (2020)
- [Incremental Nonlinear Fault-Tolerant Control of a Quadrotor with Complete Loss of Two Opposing Rotors](https://mlsanity.com/paper/2002.07837/) (2020)
- [Enhanced Droplet Analysis Using Generative Adversarial Networks](https://mlsanity.com/paper/2402.15909/) (2024)

## Built on by (most cited first, 3 of 3)

- [Moving On, Even When You're Broken: Fail-Active Trajectory Generation via Diffusion Policies Conditioned on Embodiment and Task](https://mlsanity.com/paper/2602.02895/) (2026)
- [Uncovering Vulnerability of Vision-Language-Action Models under Joint-Level Physical Faults](https://mlsanity.com/paper/2606.10501/) (2026)
- [Predicting Space Tourism Demand Using Explainable AI](https://mlsanity.com/paper/2503.03113/) (2025)

## Abstract (arXiv)

Robotic manipulators are widely used in various industries for complex and repetitive tasks. However, they remain vulnerable to unexpected hardware failures. In this study, we address the challenge of enabling a robotic manipulator to complete tasks despite joint malfunctions. Specifically, we develop a reinforcement learning (RL) framework to adaptively compensate for a non-functional joint during task execution. Our experimental platform is the Franka robot with 7 degrees of freedom (DOFs). We formulate the problem as a partially observable Markov decision process (POMDP), where the robot is trained under various joint failure conditions and tested in both seen and unseen scenarios. We consider scenarios where a joint is permanently broken and where it functions intermittently. Additionally, we demonstrate the effectiveness of our approach by comparing it with traditional inverse kinematics-based control methods. The results show that the RL algorithm enables the robot to successfully complete tasks even with joint failures, achieving a high success rate with an average rate of 93.6%. This showcases its robustness and adaptability. Our findings highlight the potential of RL to enhance the resilience and reliability of robotic systems, making them better suited for unpredictable environments. All related codes and models are published online.