Explaining the Decisions of Deep Policy Networks for Robotic Manipulations

• URL: http://arxiv.org/abs/2310.19432v1
• Date: Mon, 30 Oct 2023 10:44:12 GMT
• Title: Explaining the Decisions of Deep Policy Networks for Robotic Manipulations
• Authors: Seongun Kim, Jaesik Choi
• Abstract summary: We present an explicit analysis of deep policy models through input attribution methods to explain how and to what extent each input feature affects the decisions of the robot policy models. To the best of our knowledge, this is the first report to identify the dynamic changes of input attributions of multi-modal sensor inputs in deep policy networks online for robotic manipulation.
• Score: 27.526882375069963
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep policy networks enable robots to learn behaviors to solve various real-world complex tasks in an end-to-end fashion. However, they lack transparency to provide the reasons of actions. Thus, such a black-box model often results in low reliability and disruptive actions during the deployment of the robot in practice. To enhance its transparency, it is important to explain robot behaviors by considering the extent to which each input feature contributes to determining a given action. In this paper, we present an explicit analysis of deep policy models through input attribution methods to explain how and to what extent each input feature affects the decisions of the robot policy models. To this end, we present two methods for applying input attribution methods to robot policy networks: (1) we measure the importance factor of each joint torque to reflect the influence of the motor torque on the end-effector movement, and (2) we modify a relevance propagation method to handle negative inputs and outputs in deep policy networks properly. To the best of our knowledge, this is the first report to identify the dynamic changes of input attributions of multi-modal sensor inputs in deep policy networks online for robotic manipulation.

Related papers

• Learning to Look: Seeking Information for Decision Making via Policy Factorization [36.87799092971961]
  We propose DISaM, a dual-policy solution composed of an information-seeking policy and an information-receiving policy. We demonstrate the capabilities of our dual policy solution in five manipulation tasks that require information-seeking behaviors.
  arXiv  Detail & Related papers  (2024-10-24T17:58:11Z)
• Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regression [53.33734159983431]
  This paper introduces a novel approach to distill neural RL policies into more interpretable forms. We train expert neural network policies using RL and distill them into (i) GBMs, (ii) EBMs, and (iii) symbolic policies.
  arXiv  Detail & Related papers  (2024-03-21T11:54:45Z)
• Learning active tactile perception through belief-space control [21.708391958446274]
  We propose a method that autonomously learns tactile exploration policies by developing a generative world model. We evaluate our method on three simulated tasks where the goal is to estimate a desired object property. We find that our method is able to discover policies that efficiently gather information about the desired property in an intuitive manner.
  arXiv  Detail & Related papers  (2023-11-30T21:54:42Z)
• Learning Vision-based Pursuit-Evasion Robot Policies [54.52536214251999]
  We develop a fully-observable robot policy that generates supervision for a partially-observable one. We deploy our policy on a physical quadruped robot with an RGB-D camera on pursuit-evasion interactions in the wild.
  arXiv  Detail & Related papers  (2023-08-30T17:59:05Z)
• Active Exploration for Robotic Manipulation [40.39182660794481]
  This paper proposes a model-based active exploration approach that enables efficient learning in sparse-reward robotic manipulation tasks. We evaluate our proposed algorithm in simulation and on a real robot, trained from scratch with our method.
  arXiv  Detail & Related papers  (2022-10-23T18:07:51Z)
• Interpreting Neural Policies with Disentangled Tree Representations [58.769048492254555]
  We study interpretability of compact neural policies through the lens of disentangled representation. We leverage decision trees to obtain factors of variation for disentanglement in robot learning. We introduce interpretability metrics that measure disentanglement of learned neural dynamics.
  arXiv  Detail & Related papers  (2022-10-13T01:10:41Z)
• Verifying Learning-Based Robotic Navigation Systems [61.01217374879221]
  We show how modern verification engines can be used for effective model selection. Specifically, we use verification to detect and rule out policies that may demonstrate suboptimal behavior. Our work is the first to demonstrate the use of verification backends for recognizing suboptimal DRL policies in real-world robots.
  arXiv  Detail & Related papers  (2022-05-26T17:56:43Z)
• Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models [67.78935378952146]
  GenRL is a framework for solving sequential decision-making problems. It exploits the combination of reinforcement learning and latent variable generative models. We experimentally determine the characteristics of generative models that have most influence on the performance of the final policy training.
  arXiv  Detail & Related papers  (2022-04-18T22:02:32Z)
• Active Uncertainty Learning for Human-Robot Interaction: An Implicit Dual Control Approach [5.05828899601167]
  We present an algorithmic approach to enable uncertainty learning for human-in-the-loop motion planning based on the implicit dual control paradigm. Our approach relies on sampling-based approximation of dynamic programming model predictive control problem. The resulting policy is shown to preserve the dual control effect for generic human predictive models with both continuous and categorical uncertainty.
  arXiv  Detail & Related papers  (2022-02-15T20:40:06Z)
• Neural Dynamic Policies for End-to-End Sensorimotor Learning [51.24542903398335]
  The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces. We propose Neural Dynamic Policies (NDPs) that make predictions in trajectory distribution space. NDPs outperform the prior state-of-the-art in terms of either efficiency or performance across several robotic control tasks.
  arXiv  Detail & Related papers  (2020-12-04T18:59:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.