Integrating Human-Provided Information into Belief State Representation Using Dynamic Factorization

Abstract

In partially observed environments, it can be useful for a human to provide the robot with declarative information that represents probabilistic relational constraints on properties of objects in the world, augmenting the robot's sensory observations. For instance, a robot tasked with a searchand-rescue mission may be informed by the human that two victims are probably in the same room. An important question arises: how should we represent the robot's internal knowledge so that this information is correctly processed and combined with raw sensory information? In this paper, we provide an efficient belief state representation that dynamically selects an appropriate factoring, combining aspects of the belief when they are correlated through information and separating them when they are not. This strategy works in open domains, in which the set of possible objects is not known in advance, and provides significant improvements in inference time over a static factoring, leading to more efficient planning for complex partially observed tasks. We validate our approach experimentally in two open-domain planning problems: a 2D discrete gridworld task and a 3D continuous cooking task. A supplementary video can be found at http://tinyurl.com/chitnis-iros-18.

A Survey of Knowledge-based Sequential Decision Making under Uncertainty

“…It is evident that modeling exogenous variables in RDK enables the sequential decision maker to respond to exogenous events without modeling those events in the world model of SDM Lu et al, 2017;Chitnis et al, 2018]. The improvement in scalability was achieved by letting RDK and SDM handling the "dimensionality" and "history" respectively.…”

Learning and Reasoning for Robot Dialog and Navigation Tasks

“…An action language was used to compute a deterministic sequence of actions for robots, where individual actions are then implemented using probabilistic controllers (Sridharan et al, 2019). Recently, human-provided information has been incorporated into belief state representations to guide robot action selection (Chitnis et al, 2018). In comparison to our approach, learning (from reinforcement or not) was not discussed in the abovementioned algorithms.…”

Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language

“…Intelligent agents need the capabilities of both reasoning about declarative knowledge, and probabilistic planning toward achieving long-term goals. A variety of algorithms have been developed to integrate commonsense reasoning and probabilistic planning (Hanheide et al 2017;Zhang and Stone 2015;Sridharan et al 2019;Chitnis et al 2018;Zhang et al 2017;Amiri et al 2018;Veiga et al 2019), and some of them, such as (Sridharan et al 2019) and (Amiri et al 2018), also include non-deterministic dynamic laws for observations. Although the algorithms use very different computational paradigms for representing and reasoning with human knowledge (e.g., logics, probabilities, graphs, etc), they all share the goal of leveraging declarative knowledge to improve the performance in probabilistic planning.…”