Safe and Robust Robot Learning from Demonstration through Conceptual Constraints

Mueller, Carl L.; Hayes, Bradley

doi:10.1145/3371382.3377428

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…Imitation learning (often known as learning from demonstration in robotics [19,20]) has been used in the past to learn robot policies for a variety of human-robot interaction scenarios. In particular, it is common to learn robot behaviors from expert human demonstrations, such as in kinesthetic teaching of manipulation skills [21,22]. Closer to our work, imitation learning was used by Jain et al [23] to predict nonverbal behaviors in a conversation, including back-channelling.…”

Section: B Imitation Learning In Hrimentioning

confidence: 93%

Learning Gaze Behaviors for Balancing Participation in Group Human-Robot Interactions

Gillet

Parreira

Vázquez

et al. 2022

2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

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Robots can affect group dynamics. In particular, prior work has shown that robots that use hand-crafted gaze heuristics can influence human participation in group interactions. However, hand-crafting robot behaviors can be difficult and might have unexpected results in groups. Thus, this work explores learning robot gaze behaviors that balance human participation in conversational interactions. More specifically, we examine two techniques for learning a gaze policy from data: imitation learning (IL) and batch reinforcement learning (RL). First, we formulate the problem of learning a gaze policy as a sequential decision-making task focused on human turn-taking. Second, we experimentally show that IL can be used to combine strategies from hand-crafted gaze behaviors, and we formulate a novel reward function to achieve a similar result using batch RL. Finally, we conduct an offline evaluation of IL and RL policies and compare them via a user study (N=50). The results from the study show that the learned behavior policies did not compromise the interaction. Interestingly, the proposed reward for the RL formulation enabled the robot to encourage participants to take more turns during group human-robot interactions than one of the gaze heuristic behaviors from prior work. Also, the imitation learning policy led to more active participation from human participants than another prior heuristic behavior.

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Section: B Imitation Learning In Hrimentioning

confidence: 93%

Learning Gaze Behaviors for Balancing Participation in Group Human-Robot Interactions

Gillet

Parreira

Vázquez

et al. 2022

2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

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“…These methods use various technical means to encode the teaching data of manipulation tasks and generate optimal reproduction trajectories [4][5][6]. Many studies on robotic demonstration learning focus on mapping and replicating human movements, with the goal of maximizing the imitation of precise motion trajectories associated with the set task [7][8][9][10][11][12]. For example, Fitzgerald et al [13] used "kinesthetic teaching" to complete the QR code block rearrangement task, and Wu et al [14] marked benchmark points on objects and the demonstrator's arms and used visual motion tracking methods to record the trajectory of the benchmark points.…”

Section: Related Workmentioning

confidence: 99%

“…Its function is to estimate the spatial position x v , corresponding to a time step sequence in trajectory X based on the GMM. The formulas for the expectation x v and variance of distribution are Formulas ( 8) and (9).…”

Section: Action Layer Of Lfdmentioning

confidence: 99%

Research on LFD System of Humanoid Dual-Arm Robot

Cui,

Kou,

Chen

et al. 2024

Symmetry

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Although robots have been widely used in a variety of fields, the idea of enabling them to perform multiple tasks in the same way that humans do remains a difficulty. To solve this, we investigate the learning from demonstration (LFD) system with our independently designed symmetrical humanoid dual-arm robot. We present a novel action feature matching algorithm. This algorithm accurately transforms human demonstration data into task models that robots can directly execute, considerably improving LFD’s generalization capabilities. In our studies, we used motion capture cameras to capture human demonstration actions, which included combinations of simple actions (the action layer) and a succession of complicated operational tasks (the task layer). For the action layer data, we employed Gaussian mixture models (GMM) for processing and constructing an action primitive library. As for the task layer data, we created a “keyframe” segmentation method to transform this data into a series of action primitives and build another action primitive library. Guided by our algorithm, the robot successfully imitated complex human tasks. Results show its excellent task learning and execution, providing an effective solution for robots to learn from human demonstrations and significantly advancing robot technology.

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“…Therefore, not P) through simulation of multiple possibilities of interactions and events which is otherwise strenuous within the limits of human cognitive capacity. Mueller et al (2021) propose a visual interactive model for robot skill learning from visual demonstrations. An interactive augmented reality-based approach is used to iteratively program robot skills through the demonstrations.…”

Section: Proposed Counterfactual Learning-based Approach For Resilien...mentioning

confidence: 99%

Counterfactual learning in enhancing resilience in autonomous agent systems

Samarasinghe

2023

Front. Artif. Intell.

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Resilience in autonomous agent systems is about having the capacity to anticipate, respond to, adapt to, and recover from adverse and dynamic conditions in complex environments. It is associated with the intelligence possessed by the agents to preserve the functionality or to minimize the impact on functionality through a transformation, reconfiguration, or expansion performed across the system. Enhancing the resilience of systems could pave way toward higher autonomy allowing them to tackle intricate dynamic problems. The state-of-the-art systems have mostly focussed on improving the redundancy of the system, adopting decentralized control architectures, and utilizing distributed sensing capabilities. While machine learning approaches for efficient distribution and allocation of skills and tasks have enhanced the potential of these systems, they are still limited when presented with dynamic environments. To move beyond the current limitations, this paper advocates incorporating counterfactual learning models for agents to enable them with the ability to predict possible future conditions and adjust their behavior. Counterfactual learning is a topic that has recently been gaining attention as a model-agnostic and post-hoc technique to improve explainability in machine learning models. Using counterfactual causality can also help gain insights into unforeseen circumstances and make inferences about the probability of desired outcomes. We propose that this can be used in agent systems as a means to guide and prepare them to cope with unanticipated environmental conditions. This supplementary support for adaptation can enable the design of more intelligent and complex autonomous agent systems to address the multifaceted characteristics of real-world problem domains.

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Safe and Robust Robot Learning from Demonstration through Conceptual Constraints

Cited by 4 publications

References 13 publications

Learning Gaze Behaviors for Balancing Participation in Group Human-Robot Interactions

Learning Gaze Behaviors for Balancing Participation in Group Human-Robot Interactions

Research on LFD System of Humanoid Dual-Arm Robot

Counterfactual learning in enhancing resilience in autonomous agent systems

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