Learning User Preferences in Robot Motion Planning Through Interaction

Wilde, Nils; Kulić, Dana; Smith, Stephen L.

doi:10.1109/icra.2018.8460586

Cited by 28 publications

(41 citation statements)

References 6 publications

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“…In [23], human preferences are learned though ratings based on a pairwise comparison of trajectories. In [24], a robot motion planning problem is considered, where users provide a ranking of paths that enable the evaluation of the importance of different constraints. In [25], a method is presented that actively synthesizes queries to a user to update a distribution over reward parameters.…”

Section: Related Workmentioning

confidence: 99%

Using Human Ratings for Feedback Control: A Supervised Learning Approach With Application to Rehabilitation Robotics

et al. 2020

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This paper presents a method for tailoring a parametric controller based on human ratings. The method leverages supervised learning concepts in order to train a reward model from data. It is applied to a gait rehabilitation robot with the goal of teaching the robot how to walk patients physiologically. In this context, the reward model judges the physiology of the gait cycle (instead of therapists) using sensor measurements provided by the robot and the automatic feedback controller chooses the input settings of the robot so as to maximize the reward. The key advantage of the proposed method is that only a few input adaptations are necessary to achieve a physiological gait cycle. Experiments with non-disabled subjects show that the proposed method permits the incorporation of human expertise into a control law and to automatically walk patients physiologically.

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Section: Related Workmentioning

confidence: 99%

Using Human Ratings for Feedback Control: A Supervised Learning Approach With Application to Rehabilitation Robotics

et al. 2020

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“…As in our previous work [3], we consider a fully known, static environment, represented as a weighted strongly connected multigraph G = (V, E, Ψ, t). The weight t on the graph encodes the time a robot requires to traverse an edge.…”

Section: B Problem Statementmentioning

confidence: 99%

“…In this section we propose a probabilistic model of user behaviour. In our previous work [3] we required the user to always provide feedback consistent with a linear user model. In contrast, we now consider that the user feedback may be noisy and thus the user feedback is not deterministic.…”

Section: Probabilistic Learningmentioning

confidence: 99%

“…Through this interaction with the user, the relative importance of each constraint can be learned. In our previous work [3], we developed an algorithm that iteratively builds a set of linear inequalities on the hidden weights of the user constraints. Our user model evaluated paths based on a cost function trading-off constraint violation and time.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in [4] experts rank the demonstrated task performance for grasping applications while [5], [6] focus on continuous trajectories of dynamical systems like autonomous mobile robots. We propose a framework relying on a deterministic black-box planner that outputs a path for a given set of weights for the user constraints [3]. Different weights can have the same optimal solution, allowing for a discretization of the weight space.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions

Wilde

Kulić

Smith

2019

IEEE Robot. Autom. Lett.

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Specifying complex task behaviours while ensuring good robot performance may be difficult for untrained users. We study a framework for users to specify rules for acceptable behaviour in a shared environment such as industrial facilities. As non-expert users might have little intuition about how their specification impacts the robot's performance, we design a learning system that interacts with the user to find an optimal solution. Using active preference learning, we iteratively show alternative paths that the robot could take on an interface. From the user feedback ranking the alternatives, we learn about the weights that users place on each part of their specification. We extend the user model from our previous work to a discrete Bayesian learning model and introduce a greedy algorithm for proposing alternative that operates on the notion of equivalence regions of user weights. We prove that with this algorithm the revision active learning process converges on the user-optimal path. In simulations on realistic industrial environments, we demonstrate the convergence and robustness of our approach.

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Personalization of industrial human–robot communication through domain adaptation based on user feedback

Mukherjee,

Hong,

Vats

et al. 2024

User Model User-Adap Inter

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Achieving safe collaboration between humans and robots in an industrial work-cell requires effective communication. This can be achieved through a robot perception system developed using data-driven machine learning. The challenge for human-robot communication is the availability of extensive, labelled datasets for training. Due to the variations in human behaviour and the impact of environmental conditions on the performance of perception models, models trained on standard, publicly available datasets fail to generalize well to domain and applicationspecific scenarios. Thus, model personalization involving the adaptation of such models to the individual humans involved in the task in the given environment would lead to better model performance. A novel framework Personalization Human-Robot Communication based on User Feedback is presented that leverages robust modes of communication and gathers feedback from the human partner to auto-label the mode with the sparse dataset. The strength of the contribution lies in using in-commensurable multimodes of inputs for personalizing models with user-specific data. The personalization through feedback-enabled human-robot communication (PF-HRCom) framework is implemented on the use of facial expression recognition as a safety feature to ensure that the human partner is engaged in the collaborative task with the robot. Additionally, PF-HRCom has been applied to a real-time human-robot handover task with a robotic manipulator. The perception module of the manipulator adapts to the user's facial expressions and personalizes the model using feedback. Having said that, the framework is applicable to other combinations of multimodal inputs in human-robot collaboration applications.

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Learning User Preferences in Robot Motion Planning Through Interaction

Cited by 28 publications

References 6 publications

Using Human Ratings for Feedback Control: A Supervised Learning Approach With Application to Rehabilitation Robotics

Using Human Ratings for Feedback Control: A Supervised Learning Approach With Application to Rehabilitation Robotics

Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions

Personalization of industrial human–robot communication through domain adaptation based on user feedback

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