POMDP-based long-term user intention prediction for wheelchair navigation

This paper presents an intelligent decision-making agent to assist wheelchair users in their daily navigation activities. Several navigational techniques have been successfully developed in the past to assist with specific behaviours such as "door passing" or "corridor following". These shared control strategies normally require the user to manually select the level of assistance required during use. Recent research has seen a move towards more intelligent systems that focus on forecasting users' intentions based on current and past actions. However, these predictions have been typically limited to locations immediately surrounding the wheelchair. The key contribution of the work presented here is the ability to predict the users' intended destination at a larger scale, that of a typical office arena. The systems relies on minimal user input - obtained from a standard wheelchair joystick - in conjunction with a learned Partially Observable Markov Decision Process (POMDP), to estimate and subsequently drive the user to his destination. The projection is constantly being updated, allowing for true user-platform integration. This shifts users' focus from fine motor-skilled control to coarse control broadly intended to convey intention. Successful simulation and experimental results on a real wheelchair robot demonstrate the validity of the approach. ©2008 IEEE

Wheelchair driver assistance and intention prediction using POMDPs

Electric wheelchairs give otherwise immobile people the free-dom of movement, they significantly increase independence and dramatically increase quality of life. However the physical control systems of such wheelchair can be prohibitive for some users; for example, people with severe tremors. Several assisted wheelchair platforms have been developed in the past to assist such users. Algorithms that assist specific behaviors such as door - passing, follow - corridor, or avoid - obstacles have been successful. Recent research has seen a move towards systems that predict the users intentions, based on the users input. These predictions have been typically limited to locations immediately surrounding the wheelchair. This paper presents a new assisted wheelchair driving system with large scale intelligent intention recognition based on POMDPs (Partially Observable Markov Decision Processes). The systems acts as an intelligent agent/decision-maker, it relies on minimal user input; to predict the users intention and then autonomously drives the user to his destination. The prediction is constantly being updated as new user input is received allowing for true user/system integration. This shifts the users focus from fine motor-skilled control to coarse control intended to convey intention. © 2007 IEEE

Planning and sequential decision making for human-aware robots

University of Technology, Sydney. Faculty of Engineering and Information Technology.This thesis explores the use of probabilistic techniques for enhancing the interaction between a human and a robotic assistant. The human in this context is regarded as an integral part of the system, providing a major contribution to the decision making process and is able to overwrite, re-evaluate and correct decisions made by the robot to fulfil her or his true intentions and ultimate goals and needs. Conversely, the robot is expected to behave as an intelligent collaborative agent that predicts human intentions and makes decisions by merging learned behaviours with the information it cmTently possesses. The work is motivated by the rapid increase of the application domains in which robotic systems operate, and the presence of humans in many of these domains. The proposed framework facilitates human-robot social integration by increasing the synergy between robot's capabilities and human needs, primarily during assistive navigational tasks. The first part of the thesis ets the groundwork by developing a path-planning/re-planning strategy able to produce smooth feasible paths to address the issue of navigating a robotic wheelchair in cluttered indoor environments. This strategy integrates a global path-planner that operates as a mission controller, and a local reactive planner that navigates locally in an optimal manner while preventing collisions with static and dynamic obstacles in the local area. The proposed strategy also encapsulates social behaviour, such as navigating through preferred routes, in order to generate socially and behavioura11y acceptable plans. The work then focuses on predicting and responding to human interactions with a robotic agent by exploiting probabilistic techniques for sequential decision making and planning under uncertainty. Dynamic Bayesian networks and partially observable Markov decision processes are examined for estimating human intention in order to minimise the flow of information between the human and the robot during navigation tasks. A framework to capture human behaviour, motivated by the human action cycle as derived from the psychology domain is developed. This framework embeds a human-robot interaction layer, which defines variables and procedures to model interaction scenarios, and facilitates the transfer of information during human-robot collaborative tasks. Experiments using a human-operated robotic wheelchair carrying out navigational daily routines are conducted to demonstrate the capacity of the proposed methodology to understand human intentions and comply with their long term plans. The results obtained are presented as the outcome of a set of trials conducted with actor users, or simulated experiments based on real scenarios

A POMDP framework for modelling human interaction with assistive robots

This paper presents a framework for modelling the interaction between a human operator and a robotic device, that enables the robot to collaborate with the human to jointly accomplish tasks. States of the system are captured in a model based on a partially observable Markov decision process (POMDP). States representing the human operator are motivated by behaviours from the psychology of the human action cycle. Hierarchical nature of these states allows the exploitation of data structures based on algebraic decision diagrams (ADD) to efficiently solve the resulting POMDP. The proposed framework is illustrated using two examples from as-sistive robotics; a robotic wheel chair and an intelligent walking device. Experimental results from trials conducted in an office environment with the wheelchair is used to demonstrate the proposed technique. © 2011 IEEE

Multimodal Control of a Robotic Wheelchair: Using Contextual Information for Usability Improvement

International audienceIn this paper, a method to perform semi-autonomous navigation on a wheelchair is presented. The wheelchair could be controlled in semi-autonomous mode estimating the user's intention by using a face pose recognition system or in manual mode. The estimator was performed within a Bayesian network approach. To switch these two modes, a speech interface was used. The user's intention was modeled as a set of typical destinations visited by the user. The algorithm was implemented to one experimental wheelchair robot. The new application of the wheelchair system with more natural and easy-to-use human machine interfaces was one of the main contributions. as user's habits and points of interest are employed to infer the user's desired destination in a map. Erroneous steering signals coming from the user- machine interface input are filtered out, improving the overall performance of the system. Human aware navigation, path planning and obstacle avoidance are performed by the robotic wheelchair while the user is just concerned with "looking where he wants to go"

Using social cues to estimate possible destinations when driving a robotic wheelchair

International audienceApproaching a group of humans is an important navigation task. Although many methods have been proposed to avoid interrupting groups of people engaged in a conversation, just a few works have considered the proper way of joining those groups. Research in the field of social sciences have proposed geometric models to compute the best points to join a group. In this article we propose a method to use those points as possible destinations when driving a robotic wheelchair. Those points are considered together with other possible destinations in the environment such as points of interest or typical static destinations defined by the user's habits. The intended destination is inferred using a Dynamic Bayesian Network that takes into account the contextual information of the environment and user's orders to compute the probability for each destination

Collaborative Control for a Robotic Wheelchair: Evaluation of Performance, Attention, and Workload

Powered wheelchair users often struggle to drive safely and effectively and in more critical cases can only get around when accompanied by an assistant. To address these issues, we propose a collaborative control mechanism that assists the user as and when they require help. The system uses a multiple–hypotheses method to predict the driver’s intentions and if necessary, adjusts the control signals to achieve the desired goal safely. The main emphasis of this paper is on a comprehensive evaluation, where we not only look at the system performance, but, perhaps more importantly, we characterise the user performance, in an experiment that combines eye–tracking with a secondary task. Without assistance, participants experienced multiple collisions whilst driving around the predefined route. Conversely, when they were assisted by the collaborative controller, not only did they drive more safely, but they were able to pay less attention to their driving, resulting in a reduced cognitive workload. We discuss the importance of these results and their implications for other applications of shared control, such as brain–machine interfaces, where it could be used to compensate for both the low frequency and the low resolution of the user input

Multimodal Control of a Robotic Wheelchair: Using Contextual Information for Usability Improvement

International audienceIn this paper, a method to perform semi-autonomous navigation on a wheelchair is presented. The wheelchair could be controlled in semi-autonomous mode estimating the user's intention by using a face pose recognition system or in manual mode. The estimator was performed within a Bayesian network approach. To switch these two modes, a speech interface was used. The user's intention was modeled as a set of typical destinations visited by the user. The algorithm was implemented to one experimental wheelchair robot. The new application of the wheelchair system with more natural and easy-to-use human machine interfaces was one of the main contributions. as user's habits and points of interest are employed to infer the user's desired destination in a map. Erroneous steering signals coming from the user- machine interface input are filtered out, improving the overall performance of the system. Human aware navigation, path planning and obstacle avoidance are performed by the robotic wheelchair while the user is just concerned with "looking where he wants to go"

Dynamic bayesian networks for learning interactions between assistive robotic walker and human users

Detection of individuals intentions and actions from a stream of human behaviour is an open problem. Yet for robotic agents to be truly perceived as human-friendly entities they need to respond naturally to the physical interactions with the surrounding environment, most notably with the user. This paper proposes a generative probabilistic approach in the form of Dynamic Bayesian Networks (DBN) to seamlessly account for users attitudes. A model is presented which can learn to recognize a subset of possible actions by the user of a gait stability support power rollator walker, such as standing up, sitting down or assistive strolling, and adapt the behaviour of the device accordingly. The communication between the user and the device is implicit, without any explicit intention such as a keypad or voice.The end result is a decision making mechanism that best matches the users cognitive attitude towards a set of assistive tasks, effectively incorporating the evolving activity model of the user in the process. The proposed framework is evaluated in real-life condition. © 2010 Springer-Verlag Berlin Heidelberg