Reset-free Trial-and-Error Learning for Robot Damage Recovery (1610.04213v4)

Abstract: The high probability of hardware failures prevents many advanced robots (e.g., legged robots) from being confidently deployed in real-world situations (e.g., post-disaster rescue). Instead of attempting to diagnose the failures, robots could adapt by trial-and-error in order to be able to complete their tasks. In this situation, damage recovery can be seen as a Reinforcement Learning (RL) problem. However, the best RL algorithms for robotics require the robot and the environment to be reset to an initial state after each episode, that is, the robot is not learning autonomously. In addition, most of the RL methods for robotics do not scale well with complex robots (e.g., walking robots) and either cannot be used at all or take too long to converge to a solution (e.g., hours of learning). In this paper, we introduce a novel learning algorithm called "Reset-free Trial-and-Error" (RTE) that (1) breaks the complexity by pre-generating hundreds of possible behaviors with a dynamics simulator of the intact robot, and (2) allows complex robots to quickly recover from damage while completing their tasks and taking the environment into account. We evaluate our algorithm on a simulated wheeled robot, a simulated six-legged robot, and a real six-legged walking robot that are damaged in several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and whose objective is to reach a sequence of targets in an arena. Our experiments show that the robots can recover most of their locomotion abilities in an environment with obstacles, and without any human intervention.

• The paper introduces a reset-free trial-and-error algorithm that enables robots to continuously adapt to hardware damage using a pre-generated action repertoire and online learning.
• It integrates simulation-based behavior generation via MAP-Elites with real-time adaptation using Gaussian Processes and Monte Carlo Tree Search to minimize real-world trials.
• Experimental evaluations on both simulated and real hexapod robots demonstrate robust damage recovery, underscoring its potential for resilient autonomous systems.

Reset-Free Trial-and-Error Learning for Robot Damage Recovery: An Analysis

This paper introduces a new algorithm termed "Reset-free Trial-and-Error" (RTE) designed to facilitate damage recovery in robots through learning and adaptation. The algorithm specifically aims to address challenges faced by robotic systems, such as legged robots, which are vulnerable to hardware failures, complicating their application in unpredictable environments. Unlike many traditional Reinforcement Learning (RL) approaches that require resetting the robot and environment to a predefined initial state after each episode, the RTE algorithm enables robots to learn adaptively without such constraints. This feature allows for continuous damage recovery in real-world conditions without human intervention.

The authors effectively frame the damage recovery task as an RL problem where the robot must adapt its policies in the presence of failures. The solution leverages simulations of a robot to pre-generate a plethora of behaviors using a dynamics simulator, effectively breaking down the complexity involved in online learning. It combines this pre-generated knowledge with online learning to adjust to changes in the robot's condition, allowing the robot to operate while continuously refining its understanding and capabilities.

A core innovation in this paper is the integration of a precomputed action repertoire created via the MAP-Elites algorithm. This repertoire is tested and adapted during runtime using Gaussian Processes (GPs) to estimate the effectiveness of actions in the damaged state. The algorithm not only significantly reduces the amount of real-world trials needed but also dynamically adapts to changing environments, leveraging a simulation of the intact robot to generate initial hypotheses of possible actions.

The proposed architecture employs a Monte Carlo Tree Search (MCTS) for planning, selecting optimal actions while considering model uncertainties. MCTS allows the selection of behaviors that maximize expected rewards over time, which is particularly important in damaged states where ensuring safe interaction with the environment is paramount.

The empirical evaluation conducted on both simulated and real-world platforms, including a hexapod robot, demonstrates the algorithm's capability to recover significant portions of a robot's locomotion abilities without human assistance. The experimental results further establish that RTE can achieve efficient damage recovery with minimal computational overhead during the online adaptation phase.

The results also implicate RTE’s high potential in enabling robust, versatile autonomous systems, particularly in environments where robots encounter unforeseen obstacles or damage that necessitates rapid adaptation.

Future research directions could investigate extending the RTE algorithm for handling varying degrees of partial observability in real-world applications. The algorithm could also benefit from enhancements in scalability, particularly by incorporating sparse techniques in GP models for handling larger datasets. Additionally, further exploration could be directed towards generalizing the approach across diverse robotic platforms and functionalities.

In conclusion, the RTE algorithm significantly enhances the potential for robotic systems to adapt and continue operation in the face of hardware failures. This work contributes a practical methodology for overcoming the challenges of autonomous learning and adaptation in complex, unpredictable environments, with implications for advancing the deployment of robots in critical applications such as rescue operations and environmental monitoring.