SensorPerch: Sense Wherever and Whenever it Matters
- URL: http://arxiv.org/abs/2607.10682v1
- Date: Sun, 12 Jul 2026 10:06:01 GMT
- Title: SensorPerch: Sense Wherever and Whenever it Matters
- Abstract summary: SensorPerch is a novel realization of active perception that decouples sensing from both the robot embodiment and the environment.<n> SensorPerch presents one realization of this paradigm: a lightweight, wireless, reconfigurable sensor platform that can perch on diverse surfaces.<n>We demonstrate the paradigm on two task classes: (i) object-coupled perception, where SensorPerch enables persistent object-state detection beyond the robot's current position, achieving successful event detection even when the robot is not nearby; and (ii) policy-coupled perception, where SensorPerch allows robots to construct diverse, policy-specific viewpoints for various
- Score: 2.1117452999989315
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Existing robotic perception is constrained by sensors that are either robot-mounted or permanently fixed in the environment, locking perception to a limited set of viewpoints. Yet as robots perform increasingly diverse tasks, the most informative viewpoint shifts from one task to the next-often somewhere onboard sensor and static infrastructure can not readily satisfy. To address this gap, we propose SensorPerch, a novel realization of active perception that decouples sensing from both the robot embodiment and the environment by treating sensors as independent physical entities that the robot can autonomously detach and re-attach within the environment. SensorPerch presents one realization of this paradigm: a lightweight, wireless, reconfigurable sensor platform that can perch on diverse surfaces, paired with a viewpoint-selection framework that determines task-optimal sensor placements. Together, these enable robots to construct task-relevant viewpoints on demand, independent of the robot's current position and available fixed infrastructure. We demonstrate the paradigm on two task classes: (i) object-coupled perception, where SensorPerch enables persistent object-state detection beyond the robot's current position, achieving successful event detection even when the robot is not nearby; and (ii) policy-coupled perception, where SensorPerch allows robots to construct diverse, policy-specific viewpoints for various policies, achieving success rates comparable to those obtained using oracle viewpoints.
Related papers
- Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning [128.76135832773102]
Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world.<n>Recent robot learning methods have made substantial progress by integrating force, tactile, vision, language, and proprioceptive sensing into learned manipulation policies.<n>Despite these advances, existing surveys have not explicitly reviewed force- and tactile-aware robot learning from a unified perspective.
arXiv Detail & Related papers (2026-08-02T14:22:01Z) - Requirement-Driven Design of Whole-Body Social Tactile Sensing via Virtual Human-Robot Interaction [5.7755882493185915]
We propose a requirement-driven framework that derives sensing requirements, specifically spatial resolution and placement, directly from interaction data.<n>We collected high-resolution whole-body contact distributions across multiple social scenarios, from which we identified nine recurring social touch gestures.<n>While demonstrated on a single robot platform, the methodology is designed to be transferable to other robot morphologies.
arXiv Detail & Related papers (2026-07-13T15:23:45Z) - Initiation of Interaction Detection Framework using a Nonverbal Cue for Human-Robot Interaction [15.228284553815593]
This paper describes an initiation of interaction(IoI) detection framework based on audio and vision sensor fusion.<n>In the proposed framework, the robot has its own audio and vision sensors, and can employ external vision sensor for stable human detection and tracking.
arXiv Detail & Related papers (2026-05-11T07:07:01Z) - Mobile Robot Localization Using a Novel Whisker-Like Sensor [2.4440761580878823]
This paper presents a framework for estimating contact points and robot localization in a known planar environment using a single whisker sensor.<n>Results demonstrate accurate contact estimation and localization with errors under 7 mm.
arXiv Detail & Related papers (2026-01-09T08:11:15Z) - The RoboSense Challenge: Sense Anything, Navigate Anywhere, Adapt Across Platforms [244.78980521248445]
RoboSense 2025 Challenge is designed to advance robustness and adaptability in robot perception across diverse sensing scenarios.<n>It unifies five complementary research tracks spanning language-grounded decision making, socially compliant navigation, sensor configuration generalization, cross-view and cross-modal correspondence, and cross-platform 3D perception.<n>The challenge attracted 143 teams from 85 institutions across 16 countries, reflecting broad community engagement.
arXiv Detail & Related papers (2026-01-08T15:16:18Z) - Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip
Perception of Mobile Manipulation Robots [22.63980025871784]
The proposed framework integrates heterogeneous data streams collected from various robot sensors, including RGB and depth cameras, a microphone, and a force-torque sensor.
The integrated data is used to train a deep autoencoder to construct latent representations of the multisensory data that indicate the normal status.
Anomalies can then be identified by error scores measured by the difference between the trained encoder's latent values and the latent values of reconstructed input data.
arXiv Detail & Related papers (2024-03-06T09:15:53Z) - Tactile-Filter: Interactive Tactile Perception for Part Mating [54.46221808805662]
Humans rely on touch and tactile sensing for a lot of dexterous manipulation tasks.
vision-based tactile sensors are being widely used for various robotic perception and control tasks.
We present a method for interactive perception using vision-based tactile sensors for a part mating task.
arXiv Detail & Related papers (2023-03-10T16:27:37Z) - See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation [49.925499720323806]
We study how visual, auditory, and tactile perception can jointly help robots to solve complex manipulation tasks.
We build a robot system that can see with a camera, hear with a contact microphone, and feel with a vision-based tactile sensor.
arXiv Detail & Related papers (2022-12-07T18:55:53Z) - See What the Robot Can't See: Learning Cooperative Perception for Visual
Navigation [11.943412856714154]
We train the sensors to encode and communicate relevant viewpoint information to the mobile robot.
We overcome the challenge of enabling all the sensors to predict the direction along the shortest path to the target.
Our results show that by using communication between the sensors and the robot, we achieve up to 2.0x improvement in SPL.
arXiv Detail & Related papers (2022-08-01T11:37:01Z) - Bayesian Imitation Learning for End-to-End Mobile Manipulation [80.47771322489422]
Augmenting policies with additional sensor inputs, such as RGB + depth cameras, is a straightforward approach to improving robot perception capabilities.
We show that using the Variational Information Bottleneck to regularize convolutional neural networks improves generalization to held-out domains.
We demonstrate that our method is able to help close the sim-to-real gap and successfully fuse RGB and depth modalities.
arXiv Detail & Related papers (2022-02-15T17:38:30Z) - Cognitive architecture aided by working-memory for self-supervised
multi-modal humans recognition [54.749127627191655]
The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions.
Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task.
One solution is to make robots learn from their first-hand sensory data with self-supervision.
arXiv Detail & Related papers (2021-03-16T13:50:24Z)
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.