Towards a Theory of Intentions for Human-Robot Collaboration

2016 IEEE International Conference on Robotics and Automation (ICRA), 2016

This paper addresses the problem of having a robot executing motor tasks requested by a human through spoken language. Verbal instructions do not typically have a one-to-one mapping to robot actions, due to various reasons: economy of spoken language, e.g., one short instruction might indeed correspond to a complex sequence of robot actions, and details about action execution might be omitted; grounding, e.g., some actions might need to be added or adapted due to environmental contingencies; embodiment, e.g., a robot might have different means than the human ones to obtain the goals that the instruction refers to. We propose a general cognitive architecture to deal with these issues, based on three steps: i) language-based semantic reasoning on the instruction (highlevel), ii) formulation of goals in robot symbols and probabilistic planning to achieve them (mid-level), iii) action execution (lowlevel). The description of the mid-level is the main focus of this paper. The robot plans are adapted to the current scenario, perceived in real-time and continuously updated, taking in consideration the robot capabilities, modeled through the concept of affordances: this allows for flexibility and creativity in the task execution. We showcase the performance of the proposed architecture with real world experiments using the iCub humanoid robot, also in the presence of unexpected events and action failures.

2015

This paper describes a mixed architecture that couples the non-monotonic logical reasoning capabilities of a declarative language with probabilistic belief revision, enabling robots to represent and reason with qualitative and quantitative descriptions of knowledge and uncertainty. Incomplete domain knowledge, including information that holds in all but a few exceptional situations, is represented as a Answer Set Prolog (ASP) program. The answer set obtained by solving this program is used for inference, planning, and for jointly explaining (a) unexpected action outcomes; and (b) partial scene descriptions extracted from sensor input. For any given task, each action in the plan contained in the answer set is executed probabilistically. For each such action, observations extracted from sensor inputs perform incremental Bayesian updates to a probabilistic (belief) distribution over a relevant subset of the domain, committing high probability beliefs as statements to the ASP program. T...

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014

Beliefs play an important role in human-robot teaming scenarios, where the robots must reason about other agents' intentions and beliefs in order to inform their own plan generation process, and to successfully coordinate plans with the other agents. In this paper, we cast the evolving and complex structure of beliefs, and inference over them, as a planning and plan recognition problem. We use agent beliefs and intentions modeled in terms of predicates in order to create an automated planning problem instance, which is then used along with a known and complete domain model in order to predict the plan of the agent whose beliefs are being modeled. Information extracted from this predicted plan is used to inform the planning process of the modeling agent, to enable coordination. We also look at an extension of this problem to a plan recognition problem. We conclude by presenting an evaluation of our technique through a case study implemented on a real robot.

ABSTRACTThis paper describes an architecture that combines the complementary strengths of probabilistic graphical models and declarative programming to enable robots to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge. An action language is extended to support non-boolean fluents and non-deterministic causal laws. This action language is used to describe tightly-coupled transition diagrams at two levels of granularity, refining a coarse-resolution transition diagram of the domain to obtain a fine-resolution transition diagram. The coarse-resolution system description, and a history that includes (prioritized) defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action probabilistically, the part of the fine-resolution transition diagram relevant to this action is identified, and a probabilistic representation of the uncertainty in sensing and actuation is included and used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions, with the corresponding observations being recorded in the coarse-resolution history and used for subsequent reasoning. The architecture is evaluated in simulation and on a mobile robot moving objects in an indoor domain, to show that it supports reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.

2013 International Conference on Collaboration Technologies and Systems (CTS), 2013

Lecture Notes in Computer Science, 2014

This paper describes an architecture that combines the complemen- tary strengths of probabilistic graphical models and declarative programming to enable robots to represent and reason with qualitative and quantitative descrip- tions of uncertainty and domain knowledge. An action language is used for the architecture’s low-level (LL) and high-level (HL) system descriptions, and the HL definition of recorded history is expanded to allow prioritized defaults. For any given objective, tentative plans created in the HL using commonsense reasoning are implemented in the LL using probabilistic algorithms, and the correspond- ing observations are added to the HL history. Tight coupling between the levels helps automate the selection of relevant variables and the generation of policies in the LL for each HL action, and supports reasoning with violation of defaults, noisy observations and unreliable actions in complex domains. The architecture is evaluated in simulation and on robots moving objects in indoor domains.

KI - Künstliche Intelligenz

This paper makes two contributions towards enabling a robot to provide explanatory descriptions of its decisions, the underlying knowledge and beliefs, and the experiences that informed these beliefs. First, we present a theory of explanations comprising (i) claims about representing, reasoning with, and learning domain knowledge to support the construction of explanations; (ii) three fundamental axes to characterize explanations; and (iii) a methodology for constructing these explanations. Second, we describe an architecture for robots that implements this theory and supports scalability to complex domains and explanations. We demonstrate the architecture’s capabilities in the context of a simulated robot (a) moving target objects to desired locations or people; or (b) following recipes to bake biscuits.

A long-standing goal of AI is to enable robots to plan in the face of uncertain and incomplete information, and to handle task failure intelligently. This paper shows how to achieve this. There are two central ideas. The first idea is to organize the robot's knowledge into three layers: instance knowledge at the bottom, commonsense knowledge above that, and diagnostic knowledge on top. Knowledge in a layer above can be used to modify knowledge in the layer(s) below. The second idea is that the robot should represent not just how its actions change the world, but also what it knows or believes. There are two types of knowledge effects the robot's actions can have: epistemic effects (I believe X because I saw it) and assumptions (I'll assume X to be true). By combining the knowledge layers with the models of knowledge effects, we can simultaneously solve several problems in robotics: (i) task planning and execution under uncertainty; (ii) task planning and execution in open worlds; (iii) explaining task failure; (iv) verifying those explanations. The paper describes how the ideas are implemented in a three-layer architecture on a mobile robot platform. The robot implementation was evaluated in five different experiments on object search, mapping, and room categorization.

2015

This paper describes an architecture that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with qualitative and quantitative descriptions of domain knowledge and uncertainty. An action language is used for the architecture’s low-level (LL) and high-level (HL) system descriptions, and the HL definition of recorded history is expanded to allow prioritized defaults. For any given objective, each action in the plan created in the HL using non-monotonic logical reasoning is executed probabilistically in the LL, refining the HL description to identify the relevant sorts, fluents and actions, and adding the corresponding action outcomes to the HL history. The HL and LL domain representations are translated into an Answer Set Prolog (ASP) program and a partially observable Markov decision process (POMDP) respectively. ASP-based inference provides a multinomial prior for POMDP state estimation, and populates a Beta den...