PrioriTouch: Adapting to User Contact Preferences for Whole-Arm Physical Human-Robot Interaction

• URL: http://arxiv.org/abs/2509.18447v1
• Date: Mon, 22 Sep 2025 22:05:11 GMT
• Title: PrioriTouch: Adapting to User Contact Preferences for Whole-Arm Physical Human-Robot Interaction
• Authors: Rishabh Madan, Jiawei Lin, Mahika Goel, Angchen Xie, Xiaoyu Liang, Marcus Lee, Justin Guo, Pranav N. Thakkar, Rohan Banerjee, Jose Barreiros, Kate Tsui, Tom Silver, Tapomayukh Bhattacharjee,
• Abstract summary: Physical human-robot interaction (pHRI) requires robots to adapt to individual contact preferences.<n>We present PrioriTouch, a framework for ranking and executing control objectives across multiple contacts.
• Score: 9.532236911248452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Physical human-robot interaction (pHRI) requires robots to adapt to individual contact preferences, such as where and how much force is applied. Identifying preferences is difficult for a single contact; with whole-arm interaction involving multiple simultaneous contacts between the robot and human, the challenge is greater because different body parts can impose incompatible force requirements. In caregiving tasks, where contact is frequent and varied, such conflicts are unavoidable. With multiple preferences across multiple contacts, no single solution can satisfy all objectives--trade-offs are inherent, making prioritization essential. We present PrioriTouch, a framework for ranking and executing control objectives across multiple contacts. PrioriTouch can prioritize from a general collection of controllers, making it applicable not only to caregiving scenarios such as bed bathing and dressing but also to broader multi-contact settings. Our method combines a novel learning-to-rank approach with hierarchical operational space control, leveraging simulation-in-the-loop rollouts for data-efficient and safe exploration. We conduct a user study on physical assistance preferences, derive personalized comfort thresholds, and incorporate them into PrioriTouch. We evaluate PrioriTouch through extensive simulation and real-world experiments, demonstrating its ability to adapt to user contact preferences, maintain task performance, and enhance safety and comfort. Website: https://emprise.cs.cornell.edu/prioritouch.

Related papers

• 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)
• CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics [44.30880626337739]
  CooHOI is a framework designed to tackle the challenge of multi-humanoid object transportation problem. A single humanoid character learns to interact with objects through imitation learning from human motion priors. Then, the humanoid learns to collaborate with others by considering the shared dynamics of the manipulated object.
  arXiv  Detail & Related papers  (2024-06-20T17:59:22Z)
• Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences [53.353022588751585]
  We present Promptable Behaviors, a novel framework that facilitates efficient personalization of robotic agents to diverse human preferences. We introduce three distinct methods to infer human preferences by leveraging different types of interactions. We evaluate the proposed method in personalized object-goal navigation and flee navigation tasks in ProcTHOR and RoboTHOR.
  arXiv  Detail & Related papers  (2023-12-14T21:00:56Z)
• InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint [67.6297384588837]
  We introduce a novel controllable motion generation method, InterControl, to encourage the synthesized motions maintaining the desired distance between joint pairs. We demonstrate that the distance between joint pairs for human-wise interactions can be generated using an off-the-shelf Large Language Model.
  arXiv  Detail & Related papers  (2023-11-27T14:32:33Z)
• Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics [99.86385468927638]
  We propose a contact point discovery approach (CPDeform) that guides the differentiable physics solver to deform soft-body plasticines. On single-stage tasks, our method can automatically find suitable initial contact points based on transport priorities. On complex multi-stage tasks, we can iteratively switch the contact points of end-effectors based on transport priorities.
  arXiv  Detail & Related papers  (2022-05-05T17:59:41Z)
• COUCH: Towards Controllable Human-Chair Interactions [44.66450508317131]
  We study the problem of synthesizing scene interactions conditioned on different contact positions on the object. We propose a novel synthesis framework COUCH that plans ahead the motion by predicting contact-aware control signals of the hands. Our method shows significant quantitative and qualitative improvements over existing methods for human-object interactions.
  arXiv  Detail & Related papers  (2022-05-01T19:14:22Z)
• Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
  Planning motions that take human comfort into account is not a part of the human-robot handover process. We propose to generate smooth motions via an efficient model-predictive control framework. We conduct human-to-robot handover experiments on a diverse set of objects with several users.
  arXiv  Detail & Related papers  (2022-03-31T23:08:20Z)

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.