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

• URL: http://arxiv.org/abs/2603.11077v1
• Date: Tue, 10 Mar 2026 21:19:25 GMT
• Title: TATIC: Task-Aware Temporal Learning for Human Intent Inference from Physical Corrections in Human-Robot Collaboration
• Abstract summary: In human-robot collaboration (HRC), robots must adapt online to dynamic task constraints and evolving human intent.<n>We propose TATIC, a unified framework that utilizes torque-based contact force estimation and a task-aware Temporal Convolutional Network (TCN) to infer discrete task-level intent.<n>Experiments achieve a 0.904 Macro-F1 score in intent recognition and demonstrate successful hardware validation.
• Score: 3.4672098154671995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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).

Related papers

• HRIBench: Benchmarking Interaction-Centric Human-Robot Collaboration [62.19372189311434]
  We introduce HRIBench, a diagnostic benchmark for intent-aware human-robot collaboration based on executable scenarios.<n>HRIBench represents collaborative tasks as structured scenario scripts that explicitly model agent roles, temporal dependencies, coordination constraints, and human behavior distributions.<n>Results show that current foundation robot policies struggle substantially in collaborative settings despite strong manipulation ability.
  arXiv  Detail & Related papers  (2026-07-05T09:15:35Z)
• Adaptive vs. Static Robot-to-Human Handover: A Study on Orientation and Approach Direction [0.3425341633647625]
  This work presents a novel adaptive framework that adjusts dynamically the object's delivery pose in real time based on the user's hand pose and the intended downstream task.<n>A comprehensive user study compares the proposed adaptive approach against a static baseline across multiple tasks.<n>Results demonstrate that dynamic alignment significantly reduces users' cognitive workload and physiological stress, while increasing perceived trust in the robot's reliability.
  arXiv  Detail & Related papers  (2026-04-24T09:14:44Z)
• Multi-Cycle Spatio-Temporal Adaptation in Human-Robot Teaming [7.507798648267691]
  This paper introduces a framework that unifies approaches by modeling an individual's spatial behavior (motion paths) and temporal behavior (time required to complete tasks) over multiple cycles.<n>We demonstrate the importance of this dual adaptation through an ablation study in simulation and a physical robot scenario using a 7-DOF robot arm.
  arXiv  Detail & Related papers  (2026-04-21T16:49:59Z)
• Language-Grounded Decoupled Action Representation for Robotic Manipulation [78.42228162226839]
  We propose the Language-Grounded Decoupled Action Representation (LaDA) framework to connect perception and control.<n>LaDA introduces a fine-grained intermediate layer of three interpretable action primitives--translation, rotation, and gripper control--providing explicit semantic structure for low-level actions.<n>It further employs a semantic-guided soft-label contrastive learning objective to align similar action primitives across tasks, enhancing generalization and motion consistency.
  arXiv  Detail & Related papers  (2026-03-13T13:08:26Z)
• Task-oriented grasping for dexterous robots using postural synergies and reinforcement learning [4.265773997354608]
  Existing methods, employ a variety of open-loop and closed-loop approaches but lack an end-to-end solution that can grasp several objects.<n>Our proposed approach employs reinforcement learning to enhance task-oriented grasping, prioritizing the post-grasp intention of the agent.
  arXiv  Detail & Related papers  (2026-02-24T13:51:09Z)
• Replanning Human-Robot Collaborative Tasks with Vision-Language Models via Semantic and Physical Dual-Correction [7.668882674013904]
  Human-Robot Collaboration plays an important role in assembly tasks by enabling robots to plan and adjust their motions based on interactive, real-time human instructions.<n>Such instructions are often linguistically ambiguous and underspecified, making it difficult to generate physically feasible and cooperative robot behaviors.<n>We propose an HRC framework that augments a VLM-based reasoning with a dual-correction mechanism.
  arXiv  Detail & Related papers  (2026-02-16T08:24:19Z)
• InterPrior: Scaling Generative Control for Physics-Based Human-Object Interactions [58.329946838699044]
  Humans rarely plan whole-body interactions with objects at the level of explicit whole-body movements.<n>Scaling such priors is key to enabling humanoids to compose and generalize loco-manipulation skills.<n>We introduce InterPrior, a framework that learns a unified generative controller through large-scale imitation pretraining and post-training by reinforcement learning.
  arXiv  Detail & Related papers  (2026-02-05T18:59:27Z)
• ConceptACT: Episode-Level Concepts for Sample-Efficient Robotic Imitation Learning [2.9277370836568264]
  ConceptACT is an extension of Action Chunking with Transformers that leverages episode-level semantic concept annotations during training to improve learning efficiency.<n>We integrate concepts using a modified transformer architecture in which the final encoder layer implements concept-aware cross-attention, supervised to align with human annotations.
  arXiv  Detail & Related papers  (2026-01-23T19:24:00Z)
• AdaMorph: Unified Motion Retargeting via Embodiment-Aware Adaptive Transformers [49.796014934503184]
  We present a unified neural framework that enables a single model to adapt human motion to diverse robot morphologies.<n> Experimental results on 12 distinct humanoid robots demonstrate that AdaMorph effectively control across heterogeneous topologies.
  arXiv  Detail & Related papers  (2026-01-12T07:39:38Z)
• Executable Analytic Concepts as the Missing Link Between VLM Insight and Precise Manipulation [70.8381970762877]
  Vision-Language Models (VLMs) have demonstrated remarkable capabilities in semantic reasoning and task planning.<n>We introduce GRACE, a novel framework that grounds VLM-based reasoning through executable analytic concepts.<n>G GRACE provides a unified and interpretable interface between high-level instruction understanding and low-level robot control.
  arXiv  Detail & Related papers  (2025-10-09T09:08:33Z)
• HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario [63.77482302352545]
  HHI-Assist is a dataset comprising motion capture clips of human-human interactions in assistive tasks.<n>Our work has the potential to significantly enhance robotic assistance policies.
  arXiv  Detail & Related papers  (2025-09-12T09:38:17Z)
• Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis [51.95817740348585]
  Human-X is a novel framework designed to enable immersive and physically plausible human interactions across diverse entities.<n>Our method jointly predicts actions and reactions in real-time using an auto-regressive reaction diffusion planner.<n>Our framework is validated in real-world applications, including virtual reality interface for human-robot interaction.
  arXiv  Detail & Related papers  (2025-08-04T06:35:48Z)
• DTRT: Enhancing Human Intent Estimation and Role Allocation for Physical Human-Robot Collaboration [8.019409744686877]
  Human intent estimation and rational human-robot role allocation are crucial for safe and efficient assistance.<n>We propose a Dual Transformer-based Robot Trajectron (DTRT) featuring a hierarchical architecture, which harnesses human-guided motion and force data.<n>Experiments demonstrate DTRT's accurate intent estimation and superior collaboration performance.
  arXiv  Detail & Related papers  (2025-05-23T05:33:59Z)
• Hierarchical Procedural Framework for Low-latency Robot-Assisted Hand-Object Interaction [45.256762954338704]
  We propose a hierarchical procedural framework to enable robot-assisted hand-object interaction (HOI)<n>An open-loop hierarchy leverages the RGB-based 3D reconstruction of the human hand, based on which motion primitives have been designed to translate hand motions into robotic actions.<n>A case study of ring-wearing tasks indicates the potential application of this work in assistive technologies such as healthcare and manufacturing.
  arXiv  Detail & Related papers  (2024-05-29T21:20:16Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.