Skill-informed Data-driven Haptic Nudges for High-dimensional Human Motor Learning

Ankur Kamboj; Rajiv Ranganathan; Xiaobo Tan; Vaibhav Srivastava

cs.ROMar 13, 2026

Skill-informed Data-driven Haptic Nudges for High-dimensional Human Motor Learning

Authors: Ankur Kamboj, Rajiv Ranganathan, Xiaobo Tan, Vaibhav Srivastava

Organizations: Department of Electrical and Computer Engineering, Michigan State University, USA. · Department of Kinesiology and the Department of Mechanical Engineering, Michigan State University, USA.

Abstract

In this work, we propose a data-driven framework to design optimal haptic nudge feedback leveraging the learner's estimated skill to address the challenge of learning a novel motor task in a high-dimensional, redundant motor space. A nudge is a series of vibrotactile feedback delivered to the learner to encourage motor movements that aid in task completion. We first model the stochastic dynamics of human motor learning under haptic nudges using an Input-Output Hidden Markov Model (IOHMM), which explicitly decouples latent skill evolution from observable performance measures. Leveraging this predictive model, we formulate the haptic nudge feedback design problem as a Partially Observable Markov Decision Process (POMDP). This allows us to derive an optimal nudging policy that minimizes long-term performance cost and implicitly guides the learner toward superior skill states. We validate our approach through a human participant study (N=30) involving a high-dimensional motor task rendered through a hand exoskeleton. Results demonstrate that participants trained with the POMDP-derived policy exhibit significantly accelerated movement efficiency and endpoint accuracy compared to groups receiving heuristic-based feedback or no feedback. Furthermore, synergy analysis reveals that the POMDP group discovers efficient low-dimensional motor representations more rapidly.

Figures & tables

Panel of Figure 1 — Figure 1: Target Capture Task: (a) shows a participant performing our target capture task with the SenseGlove DK1 exoskeleton strapped to their right hand. (b) and (c) show the cursor trajectories during the first and eighth blocks of the game with the red dots representing the target locations. The trajectories get straighter as the participant learns to control the cursor by the end of the experiment session. (d) shows how the performance metrics, Reaching Error and Straightness of Trajectory, are computed.

Panel of Figure 2 — Figure 2: Simplified IOHMM: Human motor learning behavior modeled as an IOHMM shows how the hidden motor skill state $h_{k}$ transitions to $h_{k+1}$ under the effect of the input $\bf{x}_{k}=\{u_{k}:\texttt{slope}_{k},a_{k}:\texttt{nudge}_{k}\}$ , leading to the emissions $o_{k}=\{\texttt{RE}_{k},\texttt{SoT}_{k}\}$ under the influence of the same input $\bf{x}_{k}$ .

Panel of Figure 3 — Figure 3: Estimated Behavioral Model: (a) Predicted RE and SoT means out of the emission model for the ordered skill states, and (b) State Transition Matrices extracted from the estimated human motor learning behavioral model show how participants tend to transition from low latent skill states to high latent skill states under the effect of the input slope angles $u_{k}=-1.57,-1.11$ (corresponding to target pairs $\{0,2\}$ ) and the nudged finger indices $a_{k}={0,\cdots,5}$ . The latent skill states are ordered best-to-worst $(0-6)$ based on their mean output RE emission values. The $\texttt{nudge}=0$ value represents no nudging.

Panel of Figure 4 — Figure 4: Movement Efficiency: (a) Mean SoT curves (with $95\%$ confidence intervals) of the three group participants, and (b) the mean number of trials (along with the scatter plot taken across participants) to converge to a specific SoT threshold under the three nudging policies shows fastest convergence for participants trained on POMDP-based optimal nudging policy, followed by the heuristic policy. The low p-values from two-tailed tests from the linear mixed model fits show a decrease in SoT across trials and in the number of trials required by the POMDP group for various SoT thresholds. Each scatter plot point represents the number of trials taken by a participant to achieve the SoT threshold.

Metrics		POMDP vs Control		POMDP vs Heuristic
Metrics		$p_{\textup{bonf}}$	$d_{\textup{cohen}}$	$p_{\textup{bonf}}$	$d_{\textup{cohen}}$
SoT Curve		0.1053	-0.5368	0.5002	-0.6048
	0.34	0.5290	-0.5151	1.0000	-0.1277
SoT	0.39	0.2865	-0.6845	1.0000	-0.5148
Thresholds	0.44	0.0875	-0.9698	1.0000	-0.2950
	0.49	0.0796	-0.9912	1.0000	-0.1619

Table 1: Two-tailed Contrasts for SoT Group Differences. The p-values for the hypothesis that the POMDP group has different SoT values across gameplay trials and different number of trials to achieve thresholds compared to the control and the heuristic group are reported. The results show that the POMDP group achieves lower SoT values on average, and takes statistically significantly (at significance level

\alpha=0.10

) fewer gameplay trials to achieve SoT thresholds early in the training.

Panel of Figure 5 — Figure 5: Endpoint Accuracy: (a) Mean RE curves (with $95\%$ confidence intervals) of the three group participants, and (b) the mean number of trials (along with the scatter plot taken across participants) to converge to different RE thresholds under the three nudging policies shows fastest convergence for participants trained on POMDP-based optimal nudging policy. The low two-tailed test p-values from the linear mixed model fits show a decrease in RE across trials and in the number of trials required by the POMDP group for all RE thresholds. Each scatter plot point represents the number of trials taken by a participant to achieve that RE threshold.

Panel of Figure 6 — Figure 6: Synergy Analysis: (a) Variance accounted for (VAF) by the first four principal components (PC), and (b) the number of PCs required to explain more than $90\%$ variance in joint data across the three participant groups reveal that the POMDP group achieved a higher VAF by top-PCs (especially in later phases) and required fewer PCs to explain $>90\%$ variance in the data on average, signifying a more rapid discovery of the task relevant subspace towards expedited skill acquisition. Training blocks are consolidated into phases, with each phase consisting of two consecutive blocks.

Metrics		POMDP vs Control		POMDP vs Heuristic
Metrics		$p_{\textup{bonf}}$	$d_{\textup{cohen}}$	$p_{\textup{bonf}}$	$d_{\textup{cohen}}$
RE Curve		0.0642	-0.3863	0.0249	-0.5899
	0.14	1.0000	-0.3025	1.0000	-0.0284
RE	0.19	1.0000	-0.0400	1.0000	-0.0153
Thresholds	0.34	0.1705	-0.8144	0.1223	-0.8932
	0.39	0.3765	-0.6116	0.4802	-0.5433

Table 2: Two-tailed Contrasts for RE Group Differences. The p-values for the hypothesis that the POMDP group has different RE values across gameplay trials and different number of trials to achieve thresholds compared to the control and the heuristic group are reported. The results show that the POMDP group achieves statistically significantly (at significance level

\alpha=0.10

) lower RE values on average, and takes fewer gameplay trials to achieve RE thresholds early in the training.

Figure 7: Learning Dynamics and Convergence Analysis: Expected latent state evolution across trials and the number of trials to reach a steady state mastery level ( $\mathbb{E}[s_{k}]<3$ ) reveals expedited convergence of POMDP group to better performing states and a statistically significantly lower number of trials to mastery, compared to heuristic and control groups.

Haptic Feedback

Haptic feedback conveys touch, force, motion, or material properties through devices that let people and robots perceive and respond to interaction.

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AIDE: Automated Instruction via Distilled Expertise for Reference-Free Motor Skill Coaching

Yoshiki Ito

Research & Development Group Hitachi, Ltd. Tokyo, Japan

Text Generation

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Generating natural-language coaching feedback on motor skills can accelerate learning, yet expert coaches are scarce and expensive. Existing reference-based methods require expert demonstrations at both training and inference time, limiting practical deployment. We propose AIDE (Automated Instruction via Distilled Expertise), a framework that exploits expert references only during training and generates feedback from a learner's pose sequence alone at inference. A teacher model first learns to generate feedback from paired learner-expert poses via a frozen language model, producing separate learner tokens and difference tokens that encode the learner-expert difference. A student model then inherits the teacher's encoder and weight initialization, replacing the explicit expert comparison with an auxiliary module that produces complementary tokens from the learner's pose alone. On the ExpertAF dataset, AIDE outperforms reference-free baselines on most metrics and performs comparably to methods requiring expert demonstrations at both training and inference, with LLM-based evaluation supporting these findings.

Abstract

Haptic Feedback

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