TATIC: Task-Aware Temporal Learning for Human Intent Inference from Physical Corrections in Human-Robot Collaboration

Jiurun Song; Xiao Liang; Minghui Zheng

cs.ROMar 10, 2026

TATIC: Task-Aware Temporal Learning for Human Intent Inference from Physical Corrections in Human-Robot Collaboration

Authors: Jiurun Song, Xiao Liang, Minghui Zheng

Organizations: Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA · Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843, USA

Abstract

In human-robot collaboration (HRC), robots must adapt online to dynamic task constraints and evolving human intent. While physical corrections provide a natural, low-latency channel for operators to convey motion-level adjustments, extracting task-level semantic intent from such brief interactions remains challenging. Existing foundation-model-based approaches primarily rely on vision and language inputs and lack mechanisms to interpret physical feedback. Meanwhile, traditional physical human-robot interaction (pHRI) methods leverage physical corrections for trajectory guidance but struggle to infer task-level semantics. To bridge this gap, we propose TATIC, a unified framework that utilizes torque-based contact force estimation and a task-aware Temporal Convolutional Network (TCN) to jointly infer discrete task-level intent and estimate continuous motion-level parameters from brief physical corrections. Task-aligned feature canonicalization ensures robust generalization across diverse layouts, while an intent-driven adaptation scheme translates inferred human intent into robot motion adaptations. Experiments achieve a 0.904 Macro-F1 score in intent recognition and demonstrate successful hardware validation in collaborative disassembly (see experimental video at https://youtu.be/xF8A52qwEc8).

Figures & tables

Fig. 1: Collaborative desktop disassembly example. During RAM removal, limited visibility may prevent the robot from anticipating collisions with surrounding components. A brief physical correction conveys human intent and triggers motion adaptation to avoid the obstruction.

Fig. 2: Overview of the proposed TATIC framework.

Operator	Precision	Recall	F1-Score
Guide	$.879{\scriptstyle\pm.014}$	$.846{\scriptstyle\pm.023}$	$.862{\scriptstyle\pm.021}$
Yield	$.894{\scriptstyle\pm.016}$	$.865{\scriptstyle\pm.020}$	$.879{\scriptstyle\pm.019}$
Slow	$.931{\scriptstyle\pm.010}$	$.921{\scriptstyle\pm.015}$	$.926{\scriptstyle\pm.013}$
Stop	$.914{\scriptstyle\pm.012}$	$.889{\scriptstyle\pm.022}$	$.901{\scriptstyle\pm.018}$
Switch	$\mathbf{.961}{\scriptstyle\pm.008}$	$\mathbf{.942}{\scriptstyle\pm.013}$	$\mathbf{.951}{\scriptstyle\pm.011}$
Macro Avg.	$.916{\scriptstyle\pm.007}$	$.893{\scriptstyle\pm.010}$	$.904{\scriptstyle\pm.009}$

TABLE I: Per-Class Intent Recognition Performance

Feature Configuration	Op. F1	Tgt. F1	Dir. Cos	Mag.
Feature Configuration	( $\uparrow$ )	( $\uparrow$ )	( $\uparrow$ )	RMSE ( $\downarrow$ )
Kin. only ( $\mathbf{x}^{\mathrm{kin}}$ )	$.583{\scriptstyle\pm.017}$	$.524{\scriptstyle\pm.022}$	$.741{\scriptstyle\pm.019}$	$.182{\scriptstyle\pm.011}$
$+$ Workspace ( $\mathbf{x}^{\mathrm{ws}}$ )	$.712{\scriptstyle\pm.014}$	$.557{\scriptstyle\pm.019}$	$.803{\scriptstyle\pm.015}$	$.149{\scriptstyle\pm.009}$
$+$ Alignment ( $\mathbf{x}^{\mathrm{alg}}$ )	$.841{\scriptstyle\pm.011}$	$.891{\scriptstyle\pm.013}$	$.872{\scriptstyle\pm.012}$	$.112{\scriptstyle\pm.007}$
TATIC (Full)	$\mathbf{.904}{\scriptstyle\pm.009}$	$\mathbf{.938}{\scriptstyle\pm.010}$	$\mathbf{.916}{\scriptstyle\pm.008}$	$\mathbf{.074}{\scriptstyle\pm.005}$

TABLE II: Ablation Study on Feature Construction

Representation	Train	ID Test	OOD-Reconfig
Representation	F1 ( $\uparrow$ )	F1 ( $\uparrow$ )	F1 ( $\uparrow$ )
World Frame	$\mathbf{.921}{\scriptstyle\pm.008}$	$.889{\scriptstyle\pm.011}$	$.614{\scriptstyle\pm.024}$
World + SE(2) Aug	$.903{\scriptstyle\pm.009}$	$.891{\scriptstyle\pm.010}$	$.753{\scriptstyle\pm.019}$
Canonical Frame (Ours)	$.912{\scriptstyle\pm.007}$	$\mathbf{.904}{\scriptstyle\pm.009}$	$\mathbf{.871}{\scriptstyle\pm.013}$
	Direction Cosine Similarity ( $\uparrow$ )
World Frame	$\mathbf{.918}{\scriptstyle\pm.006}$	$.901{\scriptstyle\pm.009}$	$.573{\scriptstyle\pm.028}$

TABLE III: Ablation Study on Feature Canonicalization

Operator	Target	Non-temporal			TATIC (Ours)
Operator	Target	Mean	Lin.	MLP	GT-Filt.	E2E
Guide	Dir. $\hat{\mathbf{d}}$ (cos $\uparrow$ )	.031	.482	.641	$\mathbf{.916}{\scriptstyle\pm.008}$	$.891{\scriptstyle\pm.012}$
Guide	Mag. $\hat{m}$ (RMSE $\downarrow$ )	.213	.156	.124	$\mathbf{.074}{\scriptstyle\pm.005}$	$.098{\scriptstyle\pm.007}$
Slow	Spd. $\hat{\alpha}_{v}$ (RMSE $\downarrow$ )	.184	.142	.113	$\mathbf{.061}{\scriptstyle\pm.004}$	$.079{\scriptstyle\pm.006}$
Yield	Rad. $\hat{r}_{s}$ (RMSE $\downarrow$ )	.197	.152	.126	$\mathbf{.086}{\scriptstyle\pm.006}$	$.108{\scriptstyle\pm.008}$
Switch	Tgt. $\hat{k}$ (F1 $\uparrow$ )	.500	.694	.781	$\mathbf{.938}{\scriptstyle\pm.010}$	$.921{\scriptstyle\pm.013}$

TABLE IV: Regression Performance

Fig. 3: Collaborative desktop disassembly experiment. The human conveys intent through brief physical contact, which is decoded by TATIC into semantic operators and translated into motion adaptation of the robot. Speech-bubble annotations indicate human intent change rather than language prompts. See the supplementary video for the complete execution sequence.

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Current vision-language-action (VLA) benchmarks primarily evaluate isolated manipulation skills while leaving human-robot interaction structure largely unmodeled. However, real-world collaboration fundamentally requires coordination under shared agency, including intent understanding, temporal synchronization, protocol adherence, and safe interaction in dynamic environments. To address this gap, we introduce HRIBench, a diagnostic benchmark for intent-aware human-robot collaboration based on executable interaction scenarios. HRIBench represents collaborative tasks as structured scenario scripts that explicitly model agent roles, temporal dependencies, coordination constraints, and human behavior distributions. Building on this abstraction, HRIBench defines three representative interaction roles: Instructor, Collaborator, and Intruder, covering intent communication, joint coordination, and robustness under human intervention. The benchmark contains 13 role-conditioned tasks with over 650 evaluation episodes generated from diverse interaction trajectories and scene variations. Beyond binary task success, HRIBench introduces interpretable interaction-centric metrics spanning synchronization, responsiveness, protocol compliance, and safety. We evaluate adapted policies based on GR00T, pi0.5, and ACT under a unified protocol. Results show that current foundation robot policies struggle substantially in collaborative settings despite strong manipulation ability, revealing major limitations in temporal coordination and intent-aware behavior. Fine-tuning on HRIBench consistently improves collaborative performance. In a real-world adaptation study, simulation data generated by HRIBench improves GR00T N1.5's physical-task success rate from 0.10 to 0.43, demonstrating the benchmark's value for advancing interaction-centric robot learning.

Abstract

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