Fast Damage Recovery in Robotics with the T-Resilience Algorithm (1302.0386v1)

Abstract: Damage recovery is critical for autonomous robots that need to operate for a long time without assistance. Most current methods are complex and costly because they require anticipating each potential damage in order to have a contingency plan ready. As an alternative, we introduce the T-resilience algorithm, a new algorithm that allows robots to quickly and autonomously discover compensatory behaviors in unanticipated situations. This algorithm equips the robot with a self-model and discovers new behaviors by learning to avoid those that perform differently in the self-model and in reality. Our algorithm thus does not identify the damaged parts but it implicitly searches for efficient behaviors that do not use them. We evaluate the T-Resilience algorithm on a hexapod robot that needs to adapt to leg removal, broken legs and motor failures; we compare it to stochastic local search, policy gradient and the self-modeling algorithm proposed by Bongard et al. The behavior of the robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using only 25 tests on the robot and an overall running time of 20 minutes, T-Resilience consistently leads to substantially better results than the other approaches.

• The paper introduces the T-Resilience algorithm that enables robots to rapidly adapt to damage through self-modeling and transferability analysis.
• Experimental results on a hexapod robot show a consistent adaptation time of around 20 minutes with only 25 real-world trials across various damage scenarios.
• The approach outperforms traditional fault tolerance methods by eliminating the need for predefined contingency plans, enhancing autonomous system resilience.

Fast Damage Recovery in Robotics with the T-Resilience Algorithm

The paper under discussion presents a novel approach to adaptive behavior in robotics, addressing the need for autonomous fault recovery to facilitate long-term, unassisted robot operations in various environments. The research introduces the T-Resilience algorithm, a method allowing robotic systems to quickly develop compensatory behaviors in response to unexpected damage scenarios without relying on exhaustive pre-planned strategies.

Key Contributions and Methodology

The T-Resilience algorithm represents a significant methodological shift from traditional approaches to fault tolerance, which typically rely on component redundancy or robust controllers designed to handle anticipated failures. Instead, T-Resilience empowers robots to autonomously discover new effective behaviors through an innovative use of self-modeling and transferability analysis. Notably, the algorithm employs:

1. A Self-Model: It uses an inbuilt self-model to comprehend the implications of physical changes due to damage. This model helps the robot assess performance differences between predicted and real-world outcomes.
2. Transferability Function: This function approximates how well simulated behaviors translate to the physical robot. It doesn't attempt to explicitly identify which parts are damaged but infers which behaviors are realizable given the current state of the robot.
3. Multi-Objective Optimization: The algorithm optimizes performance while simultaneously seeking behaviors that align well between the self-model and real-world observations, thereby maximizing what the paper terms "transferability."

Experimental Validation

The effectiveness of the T-Resilience algorithm is substantiated through experiments on a hexapod robot. The hexapod is subjected to various damage scenarios, including leg removal, broken legs, and motor failures. The T-Resilience algorithm demonstrates superior adaptability compared to existing stochastic local search algorithms, policy gradient methods, and self-modeling approaches as proposed by Bongard et al. The highlighted experimental outcomes are particularly noteworthy:

• T-Resilience achieves a consistent adaptation time of approximately 20 minutes, utilizing merely 25 real-world trials.
• It outperforms alternative methods by delivering more efficient locomotion and adaptability across diverse damage configurations.

Implications and Future Directions

The practical implications of this research are substantial, particularly for autonomous systems operating in unpredictable environments, such as disaster recovery robots and space exploration rovers. By minimizing reliance on predefined contingency plans, T-Resilience offers a more generalized solution capable of handling unforeseen circumstances robustly.

Theoretically, the work contributes to reinforcement learning and evolutionary computing by expanding adaptive capabilities without needing precise damage diagnostics. The T-Resilience algorithm paves the way for research into more sophisticated applications of real-time self-modeling, potentially incorporating sensor or actuator redundancy more seamlessly in robotics.

Future exploration could delve into refining self-modeling processes to further reduce reality gaps between simulated and actual behaviors or explore the integration of more advanced machine learning models to enhance the prediction of transferability functions.

In conclusion, the T-Resilience algorithm represents an innovative stride in achieving resilient robotics, highlighting the promising intersection of adaptive learning and self-modeling in autonomous systems. The ongoing advancements hold the potential for wide-ranging implications across multiple fields where autonomous decision-making is critical under uncertain conditions.